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import argparse
import pickle
import sys
from pathlib import Path
basedir = Path(__file__).resolve().parent.parent.parent
sys.path.append(str(basedir))
from src.sbdd_metrics.evaluation import compute_all_metrics_drugflow
if __name__ == '__main__':
p = argparse.ArgumentParser()
p.add_argument('--in_dir', type=Path, required=True, help='Directory with samples')
p.add_argument('--out_dir', type=str, required=True, help='Output directory')
p.add_argument('--reference_smiles', type=str, default=None, help='Path to the .npy file with reference SMILES (optional)')
p.add_argument('--gnina', type=str, default=None, help='Path to the gnina binary file (optional)')
p.add_argument('--reduce', type=str, default=None, help='Path to the reduce binary file (optional)')
p.add_argument('--n_samples', type=int, default=None, help='Top-N sampels to evaluate (optional)')
p.add_argument('--exclude', type=str, nargs='+', default=[], help='Evaluator IDs to exclude')
p.add_argument('--job_id', type=int, default=0, help='Job ID')
p.add_argument('--n_jobs', type=int, default=1, help='Number of jobs')
args = p.parse_args()
Path(args.out_dir).mkdir(exist_ok=True, parents=True)
if args.job_id == 0 and args.n_jobs == 1:
out_detailed_table = Path(args.out_dir, 'metrics_detailed.csv')
out_aggregated_table = Path(args.out_dir, 'metrics_aggregated.csv')
out_distributions_file = Path(args.out_dir, 'metrics_data.pkl')
else:
out_detailed_table = Path(args.out_dir, f'metrics_detailed_{args.job_id}.csv')
out_aggregated_table = Path(args.out_dir, f'metrics_aggregated_{args.job_id}.csv')
out_distributions_file = Path(args.out_dir, f'metrics_data_{args.job_id}.pkl')
if out_detailed_table.exists() and out_aggregated_table.exists() and out_distributions_file.exists():
print(f'Data already exist. Terminating')
sys.exit(0)
print(f'Evaluating: {args.in_dir}')
data, detailed, aggregated = compute_all_metrics_drugflow(
in_dir=args.in_dir,
gnina_path=args.gnina,
reduce_path=args.reduce,
reference_smiles_path=args.reference_smiles,
n_samples=args.n_samples,
exclude_evaluators=args.exclude,
job_id=args.job_id,
n_jobs=args.n_jobs,
)
detailed.to_csv(out_detailed_table, index=False)
aggregated.to_csv(out_aggregated_table, index=False)
with open(Path(out_distributions_file), 'wb') as f:
pickle.dump(data, f) |