Closed Arkadiy-Garber closed 5 months ago
Hi, You could first ensure that the correct version of FastNLP is installed in your environment. You can run the following command to check the currently installed version of FastNLP: pip show fastNLP If the version is not 0.6.0, you need to install the correct version. If the problem persists after installing the correct version, please let me know.
Best, Yu
This is the output from running that command:
(deepfmt) ark@TheBelly:~$ pip show fastNLP
Name: FastNLP
Version: 1.0.1
Summary: fastNLP: Deep Learning Toolkit for NLP, developed by Fudan FastNLP Team
Home-page: https://gitee.com/fastnlp/fastNLP
Author: Fudan FastNLP Team
Author-email: None
License: Apache License
Location: /home/ark/.local/lib/python3.8/site-packages
Requires: rich, packaging, numpy, regex, prettytable, requests
Required-by:
I just followed the installation instructions provided here: https://github.com/lmsac/DeepFLR/blob/main/User%20guide.docx
I also ran the following:
(deepfmt) ark@TheBelly:~$ pip install fastNLP --upgrade
Requirement already up-to-date: fastNLP in ./.local/lib/python3.8/site-packages (1.0.1)
Requirement already satisfied, skipping upgrade: packaging in ./.local/lib/python3.8/site-packages (from fastNLP) (24.0)
Requirement already satisfied, skipping upgrade: numpy>=1.14.2 in ./.local/lib/python3.8/site-packages (from fastNLP) (1.24.4)
Requirement already satisfied, skipping upgrade: rich==11.2.0 in ./.local/lib/python3.8/site-packages (from fastNLP) (11.2.0)
Requirement already satisfied, skipping upgrade: regex!=2019.12.17 in ./.local/lib/python3.8/site-packages (from fastNLP) (2023.12.25)
Requirement already satisfied, skipping upgrade: requests in /usr/lib/python3/dist-packages (from fastNLP) (2.22.0)
Requirement already satisfied, skipping upgrade: prettytable>=0.7.2 in ./.local/lib/python3.8/site-packages (from fastNLP) (3.10.0)
Requirement already satisfied, skipping upgrade: pygments<3.0.0,>=2.6.0 in ./.local/lib/python3.8/site-packages (from rich==11.2.0->fastNLP) (2.17.2)
Requirement already satisfied, skipping upgrade: commonmark<0.10.0,>=0.9.0 in ./.local/lib/python3.8/site-packages (from rich==11.2.0->fastNLP) (0.9.1)
Requirement already satisfied, skipping upgrade: colorama<0.5.0,>=0.4.0 in /usr/lib/python3/dist-packages (from rich==11.2.0->fastNLP) (0.4.3)
Requirement already satisfied, skipping upgrade: wcwidth in /usr/lib/python3/dist-packages (from prettytable>=0.7.2->fastNLP) (0.1.8)
Hi, As I have mentioned before, If the version is not 0.6.0, you need to install the correct version. Additionally, the User Guide also specifies FastNLP (0.6.0) as the required version. I believe it would be beneficial to switch to the tested version first to avoid any potential issues.
Best, Yu
Hi Yu,
Ok, I thought that running pip install fastNLP
would give me the most up-to-date version, but looks like that was a misunderstanding on my end. I did re-install the software specifying version 0.6.0:
(deepfmt) ark@TheBelly:~/bin/DeepFLR$ pip show fastNLP
Name: FastNLP
Version: 0.6.0
Summary: fastNLP: Deep Learning Toolkit for NLP, developed by Fudan FastNLP Team
Home-page: https://github.com/fastnlp/fastNLP
Author: FudanNLP
Author-email: None
License: Apache License
Location: /home/ark/.local/lib/python3.8/site-packages
Requires: prettytable, spacy, requests, regex, torch, tqdm, numpy
Required-by:
(deepfmt) ark@TheBelly:~/bin/DeepFLR$
The script seems to have successfully progressed past the error I mentioned before, and now is producing the following output, which looks more promising, but is still, unfortunately, crashing out:
(deepfmt) ark@TheBelly:~/bin/DeepFLR$ python3 replace.py --do_decoy --no_target --inputfile demo_data/mono_target_decoy_msms_sample.csv
+-------------+--------+------+----------------+-----+--------------------+----------------+---------+-------------+---------------+--------------------+
| peptide | charge | ions | decoration | irt | peptide_tokens | peptide_length | pnumber | target | input_ids | decoration_ids |
+-------------+--------+------+----------------+-----+--------------------+----------------+---------+-------------+---------------+--------------------+
| ADEPSSEE... | 2 | 0 | [0, 0, 0, 0... | 0 | [6, 8, 4, 3, 2,... | 15 | 1 | [0. 0. 0... | [22, 6, 23... | [0, 0, 0, 0, 0,... |
| ADEPSSEE... | 2 | 1 | [0, 0, 0, 0... | 1 | [6, 8, 4, 3, 2,... | 15 | 1 | [0. 0. 0... | [22, 6, 23... | [0, 0, 0, 0, 0,... |
| ADEPSSEE... | 2 | 2 | [0, 0, 0, 0... | 2 | [6, 8, 4, 3, 2,... | 15 | 1 | [0. 0. 0... | [22, 6, 23... | [0, 0, 0, 0, 0,... |
| ADEPLSEE... | 2 | 3 | [0, 0, 0, 0... | 3 | [6, 8, 4, 3, 5,... | 15 | 1 | [0. 0. 0... | [22, 6, 23... | [0, 0, 0, 0, 0,... |
| ADESSPEE... | 2 | 4 | [0, 0, 0, 1... | 4 | [6, 8, 4, 2, 2,... | 15 | 1 | [0. 0. 0... | [22, 6, 23... | [0, 0, 0, 0, 0,... |
| ADEPSSEE... | 2 | 5 | [0, 0, 0, 0... | 5 | [6, 8, 4, 3, 2,... | 15 | 1 | [0. 0. 0... | [22, 6, 23... | [0, 0, 0, 0, 0,... |
| ADEPSSEE... | 3 | 6 | [0, 0, 0, 0... | 6 | [6, 8, 4, 3, 2,... | 15 | 1 | [0. 0. 0... | [22, 6, 23... | [0, 0, 0, 0, 0,... |
| ADEPSSEE... | 3 | 7 | [0, 0, 0, 0... | 7 | [6, 8, 4, 3, 2,... | 15 | 1 | [0. 0. 0... | [22, 6, 23... | [0, 0, 0, 0, 0,... |
| ADEPSSEE... | 3 | 8 | [0, 0, 0, 0... | 8 | [6, 8, 4, 3, 2,... | 15 | 1 | [0. 0. 0... | [22, 6, 23... | [0, 0, 0, 0, 0,... |
| ADEPESSE... | 3 | 9 | [0, 0, 0, 0... | 9 | [6, 8, 4, 3, 4,... | 15 | 1 | [0. 0. 0... | [22, 6, 23... | [0, 0, 0, 0, 0,... |
| ADEPSEES... | 3 | 10 | [0, 0, 0, 0... | 10 | [6, 8, 4, 3, 2,... | 15 | 1 | [0. 0. 0... | [22, 6, 23... | [0, 0, 0, 0, 0,... |
| ADEPSSEE... | 3 | 11 | [0, 0, 0, 0... | 11 | [6, 8, 4, 3, 2,... | 15 | 1 | [0. 0. 0... | [22, 6, 23... | [0, 0, 0, 0, 0,... |
| ADEPSSEE... | 2 | 12 | [0, 0, 0, 0... | 12 | [6, 8, 4, 3, 2,... | 15 | 1 | [0. 0. 0... | [22, 6, 23... | [0, 0, 0, 0, 0,... |
| ADEPSSEE... | 2 | 13 | [0, 0, 0, 0... | 13 | [6, 8, 4, 3, 2,... | 15 | 1 | [0. 0. 0... | [22, 6, 23... | [0, 0, 0, 0, 0,... |
| ADEPSSEE... | 2 | 14 | [0, 0, 0, 0... | 14 | [6, 8, 4, 3, 2,... | 15 | 1 | [0. 0. 0... | [22, 6, 23... | [0, 0, 0, 0, 0,... |
| ADEPLSEE... | 2 | 15 | [0, 0, 0, 0... | 15 | [6, 8, 4, 3, 5,... | 15 | 1 | [0. 0. 0... | [22, 6, 23... | [0, 0, 0, 0, 0,... |
| ASEPSDEE... | 2 | 16 | [0, 1, 0, 0... | 16 | [6, 2, 4, 3, 2,... | 15 | 1 | [0. 0. 0... | [22, 6, 23... | [0, 0, 0, 1, 0,... |
| ADESSSEE... | 2 | 17 | [0, 0, 0, 1... | 17 | [6, 8, 4, 2, 2,... | 15 | 1 | [0. 0. 0... | [22, 6, 23... | [0, 0, 0, 0, 0,... |
| ADEPSSEE... | 2 | 18 | [0, 0, 0, 0... | 18 | [6, 8, 4, 3, 2,... | 15 | 1 | [0. 0. 0... | [22, 6, 23... | [0, 0, 0, 0, 0,... |
| ADEPSSEE... | 2 | 19 | [0, 0, 0, 0... | 19 | [6, 8, 4, 3, 2,... | 15 | 1 | [0. 0. 0... | [22, 6, 23... | [0, 0, 0, 0, 0,... |
| ADEPSSEE... | 2 | 20 | [0, 0, 0, 0... | 20 | [6, 8, 4, 3, 2,... | 15 | 1 | [0. 0. 0... | [22, 6, 23... | [0, 0, 0, 0, 0,... |
| ASEPDSEE... | 2 | 21 | [0, 1, 0, 0... | 21 | [6, 2, 4, 3, 8,... | 15 | 1 | [0. 0. 0... | [22, 6, 23... | [0, 0, 0, 1, 0,... |
| ADEPSEES... | 2 | 22 | [0, 0, 0, 0... | 22 | [6, 8, 4, 3, 2,... | 15 | 1 | [0. 0. 0... | [22, 6, 23... | [0, 0, 0, 0, 0,... |
| ADEPSSEE... | 2 | 23 | [0, 0, 0, 0... | 23 | [6, 8, 4, 3, 2,... | 15 | 1 | [0. 0. 0... | [22, 6, 23... | [0, 0, 0, 0, 0,... |
| ADEPSSEE... | 2 | 24 | [0, 0, 0, 0... | 24 | [6, 8, 4, 3, 2,... | 15 | 1 | [0. 0. 0... | [22, 6, 23... | [0, 0, 0, 0, 0,... |
| ADEPSSEE... | 2 | 25 | [0, 0, 0, 0... | 25 | [6, 8, 4, 3, 2,... | 15 | 1 | [0. 0. 0... | [22, 6, 23... | [0, 0, 0, 0, 0,... |
| ADEPSSEE... | 2 | 26 | [0, 0, 0, 0... | 26 | [6, 8, 4, 3, 2,... | 15 | 1 | [0. 0. 0... | [22, 6, 23... | [0, 0, 0, 0, 0,... |
| ADESPSEE... | 2 | 27 | [0, 0, 0, 1... | 27 | [6, 8, 4, 2, 3,... | 15 | 1 | [0. 0. 0... | [22, 6, 23... | [0, 0, 0, 0, 0,... |
| SDEPSAEE... | 2 | 28 | [1, 0, 0, 0... | 28 | [2, 8, 4, 3, 2,... | 15 | 1 | [0. 0. 0... | [22, 2, 23... | [0, 1, 0, 0, 0,... |
| ADEPSSEE... | 2 | 29 | [0, 0, 0, 0... | 29 | [6, 8, 4, 3, 2,... | 15 | 1 | [0. 0. 0... | [22, 6, 23... | [0, 0, 0, 0, 0,... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
+-------------+--------+------+----------------+-----+--------------------+----------------+---------+-------------+---------------+--------------------+
config.json: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 570/570 [00:00<00:00, 44.2kB/s]
Traceback (most recent call last):
File "replace.py", line 175, in <module>
bestmodel=torch.load(bestmodelpath).state_dict()
File "/home/ark/.local/lib/python3.8/site-packages/torch/serialization.py", line 998, in load
with _open_file_like(f, 'rb') as opened_file:
File "/home/ark/.local/lib/python3.8/site-packages/torch/serialization.py", line 445, in _open_file_like
return _open_file(name_or_buffer, mode)
File "/home/ark/.local/lib/python3.8/site-packages/torch/serialization.py", line 426, in __init__
super().__init__(open(name, mode))
FileNotFoundError: [Errno 2] No such file or directory: 'phosT/best__2deepchargeModelms2_bert_mediancos_2021-09-20-01-17-50-729399'
Please advise on how to proceed.
Thanks, Arkadiy
Hi, It is specified here.
Best, Yu
Ok, great. Thank you, that worked.
Just moving through the demo data, here's the next issue:
(deepfmt) ark@TheBelly:~/bin/DeepFLR$ python3 result_processing/DeepFLR_result_processing.py --modelresultfile demo_data/mono_target_decoy_msms_samplemodelmonomz_modelresult.csv --inputfile1 demo_data/msms_sample.txt --inputfile2 demo_data/Phospho_STY_Sites.txt --sequencefile demo_data/mono_target_decoy_msms_sample.csv --cutoff 0 --outputresult demo_data/DeepFLR_phosphosites_obtained_sample.csv
SourceFile Spectrum PP.PIMID ... mz Tintensity score
0 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 7108 _ADEPS[Phospho (STY)]SEESDLEIDK_ ... [100.9736862, 102.0554504, 110.0718689, 115.08... 0.0 tensor(0.6651)
1 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 7108 _ADEPSS[Phospho (STY)]EESDLEIDK_ ... [100.9736862, 102.0554504, 110.0718689, 115.08... 0.0 tensor(0.6263)
2 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 7108 _ADEPSSEES[Phospho (STY)]DLEIDK_ ... [100.9736862, 102.0554504, 110.0718689, 115.08... 0.0 tensor(0.5372)
3 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 7108 _ADEPLSEESDS[Phospho (STY)]EIDK_ ... [100.9736862, 102.0554504, 110.0718689, 115.08... 0.0 tensor(0.3665)
4 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 7108 _ADES[Phospho (STY)]SPEESDLEIDK_ ... [100.9736862, 102.0554504, 110.0718689, 115.08... 0.0 tensor(0.4724)
5 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 7108 _ADEPSSEEEDLS[Phospho (STY)]IDK_ ... [100.9736862, 102.0554504, 110.0718689, 115.08... 0.0 tensor(0.3884)
6 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 7716 _ADEPS[Phospho (STY)]SEESDLEIDK_ ... [101.0713196, 102.0554504, 104.0534515, 110.07... 0.0 tensor(0.5536)
7 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 7716 _ADEPSS[Phospho (STY)]EESDLEIDK_ ... [101.0713196, 102.0554504, 104.0534515, 110.07... 0.0 tensor(0.5197)
8 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 7716 _ADEPSSEES[Phospho (STY)]DLEIDK_ ... [101.0713196, 102.0554504, 104.0534515, 110.07... 0.0 tensor(0.6530)
9 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 7716 _ADEPESS[Phospho (STY)]ESDLEIDK_ ... [101.0713196, 102.0554504, 104.0534515, 110.07... 0.0 tensor(0.5361)
10 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 7716 _ADEPSEES[Phospho (STY)]SDLEIDK_ ... [101.0713196, 102.0554504, 104.0534515, 110.07... 0.0 tensor(0.5309)
11 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 7716 _ADEPSSEEEDLS[Phospho (STY)]IDK_ ... [101.0713196, 102.0554504, 104.0534515, 110.07... 0.0 tensor(0.5183)
12 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 7736 _ADEPS[Phospho (STY)]SEESDLEIDK_ ... [102.0554657, 115.0870056, 116.0709763, 123.75... 0.0 tensor(0.5151)
13 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 7736 _ADEPSS[Phospho (STY)]EESDLEIDK_ ... [102.0554657, 115.0870056, 116.0709763, 123.75... 0.0 tensor(0.5203)
14 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 7736 _ADEPSSEES[Phospho (STY)]DLEIDK_ ... [102.0554657, 115.0870056, 116.0709763, 123.75... 0.0 tensor(0.6602)
15 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 7736 _ADEPLSEESDS[Phospho (STY)]EIDK_ ... [102.0554657, 115.0870056, 116.0709763, 123.75... 0.0 tensor(0.3649)
16 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 7736 _AS[Phospho (STY)]EPSDEESDLEIDK_ ... [102.0554657, 115.0870056, 116.0709763, 123.75... 0.0 tensor(0.3042)
17 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 7736 _ADES[Phospho (STY)]SSEEPDLEIDK_ ... [102.0554657, 115.0870056, 116.0709763, 123.75... 0.0 tensor(0.2778)
18 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 8107 _ADEPS[Phospho (STY)]SEESDLEIDK_ ... [101.0716248, 102.0555344, 110.071785, 115.087... 0.0 tensor(0.5944)
19 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 8107 _ADEPSS[Phospho (STY)]EESDLEIDK_ ... [101.0716248, 102.0555344, 110.071785, 115.087... 0.0 tensor(0.5465)
20 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 8107 _ADEPSSEES[Phospho (STY)]DLEIDK_ ... [101.0716248, 102.0555344, 110.071785, 115.087... 0.0 tensor(0.5006)
21 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 8107 _AS[Phospho (STY)]EPDSEESDLEIDK_ ... [101.0716248, 102.0555344, 110.071785, 115.087... 0.0 tensor(0.4846)
22 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 8107 _ADEPSEES[Phospho (STY)]SDLEIDK_ ... [101.0716248, 102.0555344, 110.071785, 115.087... 0.0 tensor(0.4918)
23 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 8107 _ADEPSSEEDS[Phospho (STY)]LEIDK_ ... [101.0716248, 102.0555344, 110.071785, 115.087... 0.0 tensor(0.4661)
24 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 8499 _ADEPS[Phospho (STY)]SEESDLEIDK_ ... [102.0554352, 110.0714493, 115.0870285, 116.07... 0.0 tensor(0.6242)
25 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 8499 _ADEPSS[Phospho (STY)]EESDLEIDK_ ... [102.0554352, 110.0714493, 115.0870285, 116.07... 0.0 tensor(0.5982)
26 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 8499 _ADEPSSEES[Phospho (STY)]DLEIDK_ ... [102.0554352, 110.0714493, 115.0870285, 116.07... 0.0 tensor(0.5557)
27 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 8499 _ADES[Phospho (STY)]PSEESDLEIDK_ ... [102.0554352, 110.0714493, 115.0870285, 116.07... 0.0 tensor(0.4998)
28 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 8499 _S[Phospho (STY)]DEPSAEESDLEIDK_ ... [102.0554352, 110.0714493, 115.0870285, 116.07... 0.0 tensor(0.3630)
29 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 8499 _ADEPSSEEIDLES[Phospho (STY)]DK_ ... [102.0554352, 110.0714493, 115.0870285, 116.07... 0.0 tensor(0.3614)
[30 rows x 27 columns]
Fspectrum SourceFile Peptide 0
0 7108 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 ADEPSS1EESDLEIDK 0.0388
1 7716 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 ADEPSSEES1DLEIDK 0.0994
2 7736 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 ADEPSSEES1DLEIDK 0.1399
3 8107 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 ADEPS1SEESDLEIDK 0.0479
4 8499 20190215_QE7_nLC3_ChKe_SA_SigmaMix5_DDA_1 ADEPS1SEESDLEIDK 0.0260
(0, 8)
(5, 8)
(5, 8)
5
5
5
5.0
5.0
5
5
nan
nan
5
9.0
9.0
5
9
5.0
5.0
9
9
nan
nan
9
9.0
9.0
9
9
5.0
5.0
9
9
nan
nan
9
9.0
9.0
9
5
5.0
5.0
5
5
nan
nan
5
9.0
9.0
5
5
5.0
5.0
5
5
nan
nan
5
9.0
9.0
5
Traceback (most recent call last):
File "result_processing/DeepFLR_result_processing.py", line 191, in <module>
df["model_proteinsite"]=df.apply(combine,axis=1)
File "/home/ark/.local/lib/python3.8/site-packages/pandas/core/frame.py", line 3940, in __setitem__
self._set_item_frame_value(key, value)
File "/home/ark/.local/lib/python3.8/site-packages/pandas/core/frame.py", line 4094, in _set_item_frame_value
raise ValueError(
ValueError: Cannot set a DataFrame with multiple columns to the single column model_proteinsite
It looks like a table was generated beore the ValueError was raised. Any thoughts on whether this is a serious issue?
Thanks, Arkadiy
Hi Arkadiy, Thank you for your message. I have successfully executed the code on my end without encountering any issues. If it is convenient for you, could you please provide me with the input files or any additional relevant details? This would allow me to replicate the conditions under which you experienced the problem and better assist you in debugging.
As I am currently approaching a deadline for a research project, It may take a certain amount of time but I will ensure to address this matter and respond to you within the next one or two weeks.
Best, Yu
Ok, sounds good, thanks. I am attaching here the demo folder that I've been working out of.
Here are the commands that I ran successfully so far:
python3 sequence_generation/Targetdecoy_phosphopeptides_generation_mono.py --inputfile demo_data/msms_sample.txt --outputfile demo_data/mono_target_decoy_msms_sample.csv
python3 replace.py --do_decoy --no_target --inputfile demo_data/mono_target_decoy_msms_sample.csv
python3 mgfprocess.py --ppm 25 --do_mgfprocess --do_scoreprediction --inputfile demo_data/mono_target_decoy_msms_samplemodelmonomz.csv --outputfile demo_data/mono_target_decoy_msms_samplemodelmonomz_modelresult.csv --mgfdatafold demo_data
python3 result_processing/DeepFLR_FLR_visualization.py --modelresultfile demo_data/mono_target_decoy_msms_samplemodelmonomz_modelresult.csv --sequencefile demo_data/mono_target_decoy_msms_sample.csv --outputfile demo_data/FLRPSM_demo.csv
python3 result_processing/DeepFLR_result_processing.py --modelresultfile demo_data/mono_target_decoy_msms_samplemodelmonomz_modelresult.csv --inputfile1 demo_data/msms_sample.txt --inputfile2 demo_data/Phospho_STY_Sites.txt --sequencefile demo_data/mono_target_decoy_msms_sample.csv --cutoff 0 --outputresult demo_data/DeepFLR_phosphosites_obtained_sample.csv
And here's a list of python packages installed in that conda env that I'm using for this:
Package Version
------------------------ --------------------
annotated-types 0.6.0
apturl 0.5.2
atomicwrites 1.1.5
attrs 19.3.0
Automat 0.8.0
bcrypt 3.1.7
beautifulsoup4 4.8.2
bidict 0.23.1
biopython 1.79
blinker 1.4
blis 0.7.11
Brlapi 0.7.0
CacheControl 0.12.6
catalogue 2.0.10
certifi 2019.11.28
chardet 3.0.4
click 8.1.7
cloud-init 23.1.2
cloudpathlib 0.16.0
colorama 0.4.3
coloredlogs 7.3
command-not-found 0.3
commonmark 0.9.1
confection 0.1.4
configobj 5.0.6
constantly 15.1.0
cryptography 2.8
cupshelpers 1.0
cutadapt 2.8
cwltool 2.0.20200224214940
cymem 2.0.8
dbus-python 1.2.16
decorator 4.4.2
defer 1.0.6
distro 1.4.0
distro-info 0.23ubuntu1
dnaio 0.4.1
duplicity 0.8.12.0
entrypoints 0.3
fasteners 0.14.1
FastNLP 0.6.0
filelock 3.13.4
fq2fa 1.1
fsspec 2024.3.1
future 0.18.2
html5lib 1.0.1
HTSeq 0.11.2
httplib2 0.14.0
huggingface-hub 0.22.2
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Hi, In my view, I think that the error in question is not related to the environment. DeepFLR has already provided many demo data files, which should facilitate the direct execution of the problematic step (result_processing/DeepFLR_result_processing.py). Could you clarify whether executing this specific step directly using the files supplied by DeepFLR poses any issues?
Best regards, Yu
Hey Yu,
Sorry, it looks like I forgot to attach the demo folder that I've been working out of. I've been using the files that are in this folder, and the exact commands from the user guide. Are you not able to replicate the error on your end?
Here is the last error:
Traceback (most recent call last):
File "result_processing/DeepFLR_result_processing.py", line 191, in <module>
df["model_proteinsite"]=df.apply(combine,axis=1)
File "/home/ark/.local/lib/python3.8/site-packages/pandas/core/frame.py", line 3940, in __setitem__
self._set_item_frame_value(key, value)
File "/home/ark/.local/lib/python3.8/site-packages/pandas/core/frame.py", line 4094, in _set_item_frame_value
raise ValueError(
ValueError: Cannot set a DataFrame with multiple columns to the single column model_proteinsite
Thanks again, Arkadiy
Hi Arkadiy, Thank you for sharing the demo data and the code. Yes, I have used the demo data and code you provided but still was unable to replicate the error you mentioned. For all the codes in the repository, I've previously tested them for multiple times without issues. May I suggest a couple of things? It might help to keep only the necessary files, or alternatively, you could try using the files directly provided by DeepFLR to execute the step where the error occurs. Additionally, could you please confirm if the problematic step is python3 result_processing/DeepFLR_result_processing.py?
Best, Yu
Hi, I am getting this error after following the user_guide instructions exactly (which I did):
Thanks, Arkadiy