nstrodt / udsmprot Goto Github PK
View Code? Open in Web Editor NEWProtein sequence classification with self-supervised pretraining
License: Other
Protein sequence classification with self-supervised pretraining
License: Other
Very detailed analysis!
It seems that you have tried BPE encoding. (I saw BPE part in your data-process scripts) I am just curious is it helpful?
Hi, thank you for the amazing work!
I am just curious if I have my own dataset for which I would like to use your model architecture for protein classification, then what would be the best way to do that?
Also, I am not able to find these Enzyme classification datasets in the repository which are mentioned in code.
path_ec_knna = git_data_path/"suppa.txt"
path_ec_knnb = git_data_path/"suppb.txt"
Thanks.
Hello, when trying to run the benchmarks from the jupyter notebook, which for various reasons I had to port to run in a local script instead, I received the following error message:
2023-12-23 18:19:26.336247: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.
2023-12-23 18:19:26.359764: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2023-12-23 18:19:26.359816: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2023-12-23 18:19:26.360542: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2023-12-23 18:19:26.364383: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.
2023-12-23 18:19:26.364542: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-12-23 18:19:28.150936: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
14945 training set records, 1661 validation set records, 4152 test set records.
Traceback (most recent call last):
File "/home/gradwan/protein_bert/sally1.py", line 37, in
pretrained_model_generator, input_encoder = load_pretrained_model()
File "/home/gradwan/protein_bert/proteinbert/existing_model_loading.py", line 54, in load_pretrained_model
return load_pretrained_model_from_dump(dump_file_path, create_model_function, create_model_kwargs = create_model_kwargs, optimizer_class = optimizer_class, lr = lr,
File "/home/gradwan/protein_bert/proteinbert/model_generation.py", line 159, in load_pretrained_model_from_dump
n_annotations, model_weights, optimizer_weights = pickle.load(f)
_pickle.UnpicklingError: invalid load key, 'v'.
Could you please help? Many thanks!
Hi,
I tried to replicate the data using ./create_datasets.sh but I received the following error:
File "/UDSMProt/code/utils/dataset_utils.py", line 268, in prepare_dataset
tok_num = np.array([[tok_stoi[o] for o in p] for p in tok])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (556825,) + inhomogeneous part.
Could you please help me how I can fix it. Thanks
Hi,
I'm trying to install the UDSMProt through conda env create -f proteomics.yml
, but incompatible packages were found. Could you provide an updated proteomics.yml file?
Thanks!
Hello,
I am trying to replicate the same dataset for EC prediction(EC40 and EC50) as in your paper UDSMProt but I find some difficulties.
First in your script code/create_datasets.sh in line 27 :
python proteomics_preprocessing.py clas_ec --drop_ec7=True --working_folder=datasets/clas_ec/clas_ec_ec50_level1 --pretrained_folder=datasets/lm/lm_sprot_uniref --level=2 --include_NoEC=False --dataset="uniprot" --sampling_method_train=1 --sampling_method_valtest=3 --ignore_pretrained_clusters=True --sampling_ratio=[.8,.1,.1] --save_prev_ids=True
I think it should be :
python proteomics_preprocessing.py clas_ec --drop_ec7=True --working_folder=datasets/clas_ec/clas_ec_ec50_level2 --pretrained_folder=datasets/lm/lm_sprot_uniref --level=2 --include_NoEC=False --dataset="uniprot" --sampling_method_train=1 --sampling_method_valtest=3 --ignore_pretrained_clusters=True --sampling_ratio=[.8,.1,.1] --save_prev_ids=True The working folder should be name clas_ec_ec50_level2 not clas_ec_ec50_level1 .
Secondly when I run the script create_datasets.sh, I have an error that says :
../tmp_data/cdhit04_uniprot_sprot_2017_03.pkl not found.
And I think this maybe because in the link you provide :
there are two files with two different versions of swissprot. The file "cdhit04_uniprot_sprot_2016_07.pkl" which uses the 07/2016 version and the file "uniref50_2017_03_uniprot_sprot_2017_03.pkl" which uses the 03/2017 version.
So I am a little bit confused, I don't know if I have to download the Swiss-Prot release of 03/2017 or 07/2016 with the files in your link in order to replicate exactly the same dataset as you in your UDSMProt paper. And even if I have the correct cdhit04 file, will I have exactly the same test dataset ? And if it is not the case, it would be kind if you could provide a link to download the exact train/dev/test dataset for ECpred.
Thanks a lot in advance
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