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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) |