from evaluation import eval_by_dockqv2,eval_by_ost import pandas as pd import argparse import os parser = argparse.ArgumentParser() parser.add_argument( "--targets_dir", required=False, default='./examples/targets', help="The dir with the targets files." ) parser.add_argument( "--evaluation_dir", required=False,default='./examples/outputs/evaluation', help="The dir with the evaluation files.", ) parser.add_argument( "--algorithm_name", required=False, default='Protenix', help="The name of the algorithm.", ) parser.add_argument( "--ground_truth_dir", required=False, default='./examples/ground_truths', help="The dir with the ground truth files.", ) parser.add_argument( "--targets", required=False, default= ["interface_protein_ligand","interface_antibody_antigen","interface_protein_dna", "monomer_protein"], nargs='+', help="targets to evaluate.", ) args = parser.parse_args() evaluation_dir = os.path.join(args.evaluation_dir,args.algorithm_name) os.makedirs(os.path.join(evaluation_dir,'raw'), exist_ok=True) target_types = args.targets prediction_summary_path = f'{evaluation_dir}/prediction_reference.csv' prediction_summary_df = pd.read_csv(prediction_summary_path) # caculation for target_type in target_types: target_df_path = f'{args.targets_dir}/{target_type}.csv' if not os.path.exists(target_df_path): print(f"target_df_path is not exists for {target_type}") continue target_df = pd.read_csv(target_df_path) target_df = pd.merge(target_df,prediction_summary_df, on='pdb_id', how='left') if target_type in ["interface_protein_protein","interface_antibody_antigen","interface_protein_peptide","interface_protein_ligand","interface_protein_dna","interface_protein_rna","monomer_dna","monomer_rna","monomer_protein"]: eval_by_ost(target_df,target_type,evaluation_dir,args.ground_truth_dir) if target_type in ["interface_protein_dna","interface_protein_rna"]: eval_by_dockqv2(target_df,target_type,evaluation_dir,args.ground_truth_dir)