feat: write ranking affinity score to csv file

main.py

@@ -13,6 +13,7 @@
 
 import click
 import sqlitedict
+import numpy as np
 
 from profold2.data.dataset import ProteinStructureDataset
 from profold2.data.parsers import parse_fasta, parse_a3m
@@ -615,6 +616,7 @@ def attr_update_weight_and_task(**args):
 )
 @click.option("--mask", type=str, default="-", hidden=True)
 @click.option("--task_def", type=str, default=json.dumps(task.make_def()), hidden=True)
+@click.option("--task_pid_prefix", type=str, default="tcr_pmhc_", hidden=True)
 @click.option(
     "--chunksize",
     type=int,
@@ -642,7 +644,23 @@ def predict(**args):
       max_var_depth=None
   )
 
+  def _parse_result(a3m_string):
+    _, descriptions = parse_fasta(a3m_string)
+    for fields in map(lambda x: x.split("\t"), descriptions):
+      pid, fields = fields[0], fields[1:]
+      if pid.startswith(args.task_pid_prefix):
+        pred = [None] * task.task_num
+        for field in fields:
+          i = field.find(":")
+          if i != -1:
+            if field[:i] == "Elo_score":
+              pred = json.loads(field[i + 1:])
+              break
+        yield pid, pred
+
   for model in args.model:
+    pred_dict = defaultdict(list)
+
     for ref_pkl in glob.glob(os.path.join(args.ref_pkl, f"{model}_*.pkl")):
       pdb_id = os.path.basename(ref_pkl)
       assert pdb_id.startswith(f"{model}_")
@@ -663,11 +681,32 @@ def predict(**args):
       )
       with io.StringIO(a3m_string) as a3m_file:
         setattr(args, "a3m_file", [a3m_file])
-        with open(
-            os.path.join(args.output_dir, f"{model}_{pid}.a3m"), "w"
-        ) as output_file:
+
+        output_file_path = os.path.join(args.output_dir, f"{model}_{pid}.a3m")
+        with open(output_file_path, "w") as output_file:
           setattr(args, "output_file", output_file)
           energy.main(args)
+        with open(output_file_path, "r") as output_file:
+          a3m_string = output_file.read()
+        for pid, pred in _parse_result(a3m_string):
+          pred_dict[pid].append(pred)
+
+    with open(os.path.join(args.output_dir, f"{model}_pred.csv"), "w") as f:
+      writer = csv.DictWriter(f, fieldnames=["id", "chains"] + task.task_name_list)
+      writer.writeheader()
+      for pid, pred_list in pred_dict.items():
+        chain_list, *_ = data.chain_list[pid]  # FIX: data.get_chain_list(protein_id)
+        assert chain_list, (pid, pid in data.chain_list, len(data.chain_list))
+        _, pred_mask = task.make_label(0, chain_list)
+
+        pred_list, pred_mask = np.asarray(pred_list), np.asarray(pred_mask)
+        pred_list = np.sum(pred_list * pred_mask[None], axis=0) / pred_list.shape[0]
+
+        row = {"id": pid, "chains": "_".join(chain_list)}
+        for idx, (pred, mask) in enumerate(zip(pred_list, pred_mask)):
+          if mask:
+            row[task.task_name_list[idx]] = pred
+        writer.writerow(row)
 
 
 if __name__ == "__main__":

task.py

@@ -9,6 +9,7 @@
 
 task_num = len(task_mapping.keys())
 
+task_name_list = ["pMHC", "pTCR", "TCR_pMHC"]
 
 def make_def():
   task_def = defaultdict(list)