Constructing Supervised High quality-Tuning Knowledge from NVIDIA Open-SWE-Traces: Trajectory Parsing, Patch Evaluation, Token Budgets, and Instrument-Use Metrics

banner("STEP 3 — Constructing the evaluation DataFrame")
def process_example(ex):
   traj = normalize_trajectory(ex.get("trajectory"))
   rc = role_counts(traj)
   nf, add, dele, _files, _exts = parse_patch(ex.get("model_patch"))
   meta = normalize_metadata(ex.get("metadata"))
   full_text = "n".be a part of(message_text(m) for m in traj)
   return {
       "instance_id": ex.get("instance_id"),
       "repo": ex.get("repo"),
       "language": (ex.get("language") or "unknown").decrease(),
       "license": ex.get("license"),
       "resolved": ex.get("resolved"),
       "agent": ex.get("_agent"),
       "mannequin": ex.get("_model"),
       "n_messages": len(traj),
       "n_system": rc.get("system", 0),
       "n_user": rc.get("person", 0),
       "n_assistant": rc.get("assistant", 0),
       "n_tool": rc.get("device", 0),
       "patch_files": nf,
       "patch_add": add,
       "patch_del": dele,
       "patch_churn": add + dele,
       "traj_tokens": count_tokens(full_text),
       "class": meta.get("class"),
       "meta_files": meta.get("num_modified_files"),
       "meta_lines": meta.get("num_modified_lines"),
       "_tools": extract_tool_names(traj),
   }
information = [process_example(ex) for ex in raw_rows]
df = pd.DataFrame(information)
df["is_resolved"] = (df["resolved"] == 1)
df["known_label"] = df["resolved"].isin([0, 1])
print(f"DataFrame: {df.form[0]} rows x {df.form[1]} cols")
print("nNumeric abstract:")
print(df[["n_messages", "n_assistant", "n_tool",
         "patch_files", "patch_churn", "traj_tokens"]].describe().spherical(1))