README.md

metadata

title: Tox21 XGBoost Classifier
emoji: 🚀
colorFrom: green
colorTo: purple
sdk: docker
pinned: false
license: cc-by-nc-4.0
short_description: XGBoost Baseline for Tox21

Tox21 XGBoost Classifier

This repository hosts a Hugging Face Space that provides an API for submitting models to the Tox21 Leaderboard.

Here XGBoost models are trained on the Tox21 dataset, and the trained models are provided for inference. The input to the model is a SMILES string of the small molecule, and the output are 12 numeric values for each of the toxic effects of the Tox21 dataset.

Important: For leaderboard submission, your Space needs to include training code. The file train.py should train the model using the config specified inside the config/ folder and save the final model parameters into a file inside the checkpoints/ folder. The model should be trained using the Tox21_dataset provided on Hugging Face. The datasets can be loaded like this:

from datasets import load_dataset
ds = load_dataset("ml-jku/tox21", token=token)
train_df = ds["train"].to_pandas()
val_df = ds["validation"].to_pandas()

Additionally, the Space needs to implement inference in the predict() function inside predict.py. The predict() function must keep the provided skeleton: it should take a list of SMILES strings as input and return a nested prediction dictionary as output, with SMILES as keys and dictionaries containing targetname-prediction pairs as values. Therefore, any preprocessing of SMILES strings must be executed on-the-fly during inference.

Repository Structure

• predict.py - Defines the predict() function required by the leaderboard (entry point for inference).

• app.py - FastAPI application wrapper (can be used as-is).

• preprocess.py - preprocesses SMILES strings to generate feature descriptors and saves results as NPZ files in data/.

• train.py - trains and saves a model using the config in the config/ folder.

• config/ - the config file used by train.py.

• logs/ - all the logs of train.py, the saved model, and predictions on the validation set.

• data/ - XGBoost uses numerical data. During preprocessing in preprocess.py two NPZ files containing molecule features are created and saved here.

• checkpoints/ - the saved model that is used in predict.py is here.

• src/ - Core model & preprocessing logic:

  • preprocess.py - SMILES preprocessing logic
  • model.py - XGBoost model class with processing, saving and loading logic
  • utils.py - utility functions

Quickstart with Spaces

You can easily adapt this project in your own Hugging Face account:

• Open this Space on Hugging Face.

• Click "Duplicate this Space" (top-right corner).

• Modify src/ for your preprocessing pipeline and model class

• Modify predict() inside predict.py to perform model inference while keeping the function skeleton unchanged to remain compatible with the leaderboard.

• Modify train.py and/or preprocess.py according to your model and preprocessing pipeline.

• Modify the file inside config/ to contain all hyperparameters that are set in train.py.

That’s it, your model will be available as an API endpoint for the Tox21 Leaderboard.

Installation

To run (and train) the XGBoost, clone the repository and install dependencies:

git clone https://huggingface.co/spaces/ml-jku/tox21_xgboost_classifier
cd tox_21_xgb_classifier

conda create -n tox21_xgb_cls python=3.11
conda activate tox21_xgb_cls
pip install -r requirements.txt

Training

To train the XGBoost model from scratch, run:

python preprocess.py
python train.py

These commands will:

1. Load and preprocess the Tox21 training dataset
2. Train an XGBoost classifier
3. Store the resulting model in the checkpoints/ directory.

Inference

For inference, you only need predict.py.

Example usage inside Python:

from predict import predict

smiles_list = ["CCO", "c1ccccc1", "CC(=O)O"]
results = predict(smiles_list)

print(results)

The output will be a nested dictionary in the format:

{
    "CCO": {"target1": 0, "target2": 1, ..., "target12": 0},
    "c1ccccc1": {"target1": 1, "target2": 0, ..., "target12": 1},
    "CC(=O)O": {"target1": 0, "target2": 0, ..., "target12": 0}
}

Notes

• Only adapting predict.py for your model inference is required for leaderboard submission.

• Preprocessing (here inside src/data.py) must be applied at inference time, not just training.