Home Page: https://genomenlp.readthedocs.io/en/latest/
License: MIT License
If you got here by scanning the QR code at ISMB/BOSC 2023, please refer to the links below for more information:
Describe the bug
When running train, if the dataset object has previously encoded class labels, it will break.
This is because it tries to coerce this to a class and fails.
To Reproduce
Run train with any data
Expected behavior
Training to be conducted as normal
Suggested fix
Added a condition to skip type change if it is correct
Screenshots
NA
Issue Description:
While attempting to reproduce the findings in your preprint, I'm encountering an error running the tokenise_bio.py script on large (3GB or larger) FASTA files, including the GCF_000001405.39_GRCh38.p13. It fails with a panic in the Rust code. The code runs successfully on (much) smaller FASTA files (200KB). I have tried using the original .fna file extension as well as .fasta but have not had success in getting past this particular error.
The error log indicates a panic due to a Rust-related error, specifically in the tokenizers-lib library. The panic occurred at the line tokenizers-lib/src/models/unigram rainer.rs:212:53, with the message "called Result::unwrap() on an Err value: Internal." The error thread panicked at called Result::unwrap() on an Err value: Internal, tokenizers-lib/src/models/unigram/trainer.rs:212:53 is related to the Hugging Face Tokenizers library.
To Reproduce
OS: EC2 Instance
Conda venv with python 3.9
Command Used for Error:
RUST_BACKTRACE=full tokenise_bio -i /data/ncbi_dataset/GCF_000001405.39_GRCh38.p13_genomic.fasta -t '/data/generated/ncbi_tokenisers/tokeniser_39_GRCh38.json
Output & error message: https://gist.github.com/stepwise-ai-dev/f23a79faaedd006bf51d486259440dd5
Steps to Reproduce:
Run the tokenise_bio.py script with GCF_000001405.39_GRCh38.p13_genomic.fna as input
Expected output
I expected similar output to previous successfully execution of tokenise_bio.py on a smaller (200KB) FASTA file.
Successful output :https://gist.github.com/stepwise-ai-dev/bbd782f3d09afaca6219b2c3a176bb42
Questions:
Any guidance on resolving this issue would be greatly appreciated.
Thank you so much!
Describe the bug
During build, this error is received.
Error while loading conda entry point: conda-libmamba-solver (libarchive.so.19: cannot open shared object file: No such file or directory)
To Reproduce
Please provide a minimal reproducible example with all steps to reproduce the behaviour before submitting an issue:
Run build (see workflow file)
Expected behavior
Build correctly.
Suggested fix
Possible fix
So far attempted to install libarchive==3.6.2. This was ineffective.
Screenshots
If applicable, add screenshots to help explain your problem.
Describe the bug:
When I attempt to run the create_embedding_bio_sp.py script, it fails with a TypeError: 'float' object is not subscriptable. The error seems to be due to the script's inability to handle 'float' values in the data.
To Reproduce:
Here are the exact commands that I ran:
tokenise_bio.py script:source activate myenv && python "tokenise_bio.py" -i "<your_path>/storage/FASTA_small/smallfasta1.fasta" -t "<your_path>/storage/tokenizer-001.json"
create_dataset_bio.py script:source activate myenv && python "create_dataset_bio.py" "<your_path>/storage/FASTA_small/smallfasta1.fasta" "<your_path>/storage/FASTA_small/smallfasta2.fasta" "<your_path>/storage/tokenizer-001.json" -c 200 -o "<your_path>/storage/dataset-bio-003"
create_embedding_bio_sp.py script:source activate myenv && python "create_embedding_bio_sp.py" -i "<your_path>/storage/dataset-bio-003/train.csv" -t "<your_path>/storage/tokenizer-001.json" -o "<your_path>/storage/embeddings-bio-002"
Expected behavior:
Expected create_embedding_bio_sp.py script to process the dataset and generate the embeddings without errors.
Error message:
TypeError: 'float' object is not subscriptable
Suggested fix:
Unclear. Issue appears to be in utils.py at line 895, in <lambda>:
sp = data[column].apply(lambda x: x[1:-1].replace("'", "").split())
Describe the bug
Many users are confused about what to put for --label_names
To Reproduce
NA
Expected behavior
NA
Suggested fix
Add additional help text and possibly argument name change
Screenshots
NA
Describe the bug
Tokenised transformers dataset object csv files are truncated if the sequence is too long.
To Reproduce
Please provide a minimal reproducible example with all steps to reproduce the behaviour before submitting an issue:
Fields input_tokens, token_type_ids, attention_mask are truncated if the feature is too long. This is true for output csv file only.
# sample run on arbitrary file with very long item
create_dataset_bio <infile_path_1> <infile_path_2> <tokeniser>
# sample output csv file
some_seq,<very very long sequence>,1,"[10 ... 20]","[0 ... 0]","[1 ... 1]"
Please make sure to include environment info including python and dependency versions. You can access this with pip freeze or conda list as needed.
# this was installed with conda install -c tyronechen ziran
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yaml 0.2.5 h7f98852_2 conda-forge
yarl 1.8.2 py39hb9d737c_0 conda-forge
yellowbrick 1.3.post1 pyhd8ed1ab_1 conda-forge
zipp 3.15.0 pyhd8ed1ab_0 conda-forge
ziran 1.0.9 0 tyronechen
zlib 1.2.13 h166bdaf_4 conda-forge
zstd 1.5.2 h3eb15da_6 conda-forge
Expected behavior
A clear and concise description of what you expected to happen.
csv files should not have truncated array values.
Suggested fix
If known.
Temporary fix: Use parquet and json files as input for training since these are unaffected.
Long term fix: Increase the array size limit for printing on pandas and/or numpy.
Screenshots
If applicable, add screenshots to help explain your problem.
Not applicable
Describe the bug
When running interpret, the resulting label order seems to be swapped.
Expected behavior
In attached image: true label is swapped (ie should be intergenic).
Suggested fix
To investigate further. Data labels are encoded correctly, so the issue is not upstream.
Screenshots
Describe the bug
When trying to install the dependencies it fails with pytorch 1.10 not available.
To Reproduce
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
Expected behavior
In installs with no issue
Suggested fix
pytorch was renamed torch. Replace pytorch with torch.
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