Datasets:
| #!/usr/bin/env python3 | |
| """Example: how to format predictions for PROTAC-Bench evaluation. | |
| This script demonstrates the expected prediction format. Replace the | |
| random predictions with your model's output. | |
| Usage: | |
| python examples/example_submission.py | |
| python evaluation/evaluate.py --predictions example_predictions.csv --output my_results.json | |
| """ | |
| import json | |
| from pathlib import Path | |
| import numpy as np | |
| import pandas as pd | |
| DATA_DIR = Path(__file__).resolve().parent.parent / "data" | |
| def main(): | |
| # Load dataset | |
| df = pd.read_csv(DATA_DIR / "protac_bench.csv") | |
| print(f"Dataset: {len(df)} entries") | |
| # === Replace this with your model's predictions === | |
| np.random.seed(42) | |
| predicted_probs = np.random.rand(len(df)) | |
| # ================================================== | |
| # Format predictions: must have 'index' and 'predicted_probability' columns | |
| submission = pd.DataFrame({ | |
| "index": range(len(df)), | |
| "predicted_probability": predicted_probs, | |
| }) | |
| out_path = Path(__file__).resolve().parent.parent / "example_predictions.csv" | |
| submission.to_csv(out_path, index=False) | |
| print(f"Saved {len(submission)} predictions to {out_path}") | |
| print(f"\nTo evaluate, run:") | |
| print(f" python evaluation/evaluate.py --predictions {out_path.name} --output results.json") | |
| if __name__ == "__main__": | |
| main() | |