🧪 ChEB-AI Proteins

python-chebai-proteins repository for protein prediction and classification, built on top of the python-chebai codebase.

🔧 Installation

To install, follow these steps:

1. Clone the repository:

git clone https://github.com/ChEB-AI/python-chebai-proteins.git

2. Install the package:

cd python-chebai
pip install .

🗂 Recommended Folder Structure

To combine configuration files from both python-chebai and python-chebai-proteins, structure your project like this:

my_projects/
├── python-chebai/
│   ├── chebai/
│   ├── configs/
│   └── ...
└── python-chebai-proteins/
    ├── chebai_proteins/
    ├── configs/
    └── ...

This setup enables shared access to data and model configurations.

🚀 Training & Pretraining Guide

⚠️ Important Setup Instructions

Before running any training scripts, ensure the environment is correctly configured:

• Either:

  • Install the python-chebai repository as a package using:

    pip install .

• OR

  • Manually set the PYTHONPATH environment variable if working across multiple directories (python-chebai and python-chebai-proteins):

    • If your current working directory is python-chebai-proteins, set:

      export PYTHONPATH=path/to/python-chebai

      or vice versa.

    • If you're working within both repositories simultaneously or facing module not found errors, we recommend configuring both directories:

      # Linux/macOS
      export PYTHONPATH=path/to/python-chebai:path/to/python-chebai-proteins
      
      # Windows (use semicolon instead of colon)
      set PYTHONPATH=path\to\python-chebai;path\to\python-chebai-proteins

🔎 See the PYTHONPATH Explained section below for more details.

📊 SCOPE hierarchy prediction

Assuming your current working directory is python-chebai-proteins, run the following command to start training:

python -m chebai fit --trainer=../configs/training/default_trainer.yml --trainer.callbacks=../configs/training/default_callbacks.yml --trainer.logger.init_args.name=scope50  --trainer.accumulate_grad_batches=4 --trainer.logger=../configs/training/wandb_logger.yml --trainer.min_epochs=100 --trainer.max_epochs=100 --data=configs/data/scope/scope50.yml --data.init_args.batch_size=32  --data.init_args.num_workers=10 --model=../configs/model/electra.yml --model.train_metrics=../configs/metrics/micro-macro-f1.yml --model.test_metrics=../configs/metrics/micro-macro-f1.yml --model.val_metrics=../configs/metrics/micro-macro-f1.yml  --model.pass_loss_kwargs=false --model.criterion=../configs/loss/bce.yml --model.criterion.init_args.beta=0.99

Same command can be used for DeepGO just by changing the config path for data.

🧭 PYTHONPATH Explained

What is PYTHONPATH?

PYTHONPATH is an environment variable that tells Python where to search for modules that aren't installed via pip or not in your current working directory.

Why You Need It

If your config refers to a custom module like:

class_path: chebai_proteins.preprocessing.datasets.scope.scope.SCOPe50

...and you're running the code from python-chebai, Python won't know where to find chebai_proteins (from another repo like python-chebai-proteins/) unless you add it to PYTHONPATH.

How Python Finds Modules

Python looks for imports in this order:

1. Current directory
2. Standard library
3. Paths in PYTHONPATH
4. Installed packages (site-packages)

You can inspect the full search paths:

python -c "import sys; print(sys.path)"

✅ Setting PYTHONPATH

🐧 Linux / macOS

export PYTHONPATH=/path/to/python-chebai-graph
echo $PYTHONPATH

🪟 Windows CMD

set PYTHONPATH=C:\path\to\python-chebai-graph
echo %PYTHONPATH%

💡 Note: This is temporary for your terminal session. To make it permanent, add it to your system environment variables.