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"""Evaluate BioinfoMCP conversion outputs against gold MCP source servers."""
from __future__ import annotations
import argparse
import ast
import json
import math
from pathlib import Path
from typing import Any
DEFAULT_BENCHMARK = Path(
"/225040511/project/Hypo_Bio_OS/experiments/bioinfomcp_benchmark/configs/benchmark_subset_500.json"
)
DEFAULT_OUTPUT = Path(
"/225040511/project/Hypo_Bio_OS/experiments/bioinfomcp_benchmark/results/conversion_metrics.json"
)
def normalize(text: str) -> str:
return "".join(ch.lower() for ch in text if ch.isalnum())
def effective_code_lines_from_text(text: str) -> int:
count = 0
for line in text.splitlines():
stripped = line.strip()
if not stripped or stripped.startswith("#"):
continue
count += 1
return count
def extract_functions_from_code(code: str) -> tuple[bool, bool, list[dict[str, Any]], str | None]:
try:
tree = ast.parse(code)
except SyntaxError as exc:
return False, False, [], str(exc)
functions = []
has_mcp_tool = False
for node in tree.body:
if not isinstance(node, ast.FunctionDef):
continue
decorators = []
for dec in node.decorator_list:
decorators.append(ast.unparse(dec) if hasattr(ast, "unparse") else "")
decorated = any("mcp.tool" in dec.replace(" ", "") for dec in decorators)
has_mcp_tool = has_mcp_tool or decorated
if not decorated:
continue
defaults = list(node.args.defaults)
args = [arg for arg in node.args.args if arg.arg not in {"self", "cls"}]
first_optional = len(args) - len(defaults)
params = []
for idx, arg in enumerate(args):
params.append(
{
"name": normalize(arg.arg),
"annotation": normalize(ast.unparse(arg.annotation)) if arg.annotation is not None else "",
"required": idx < first_optional,
}
)
functions.append({"name": normalize(node.name), "params": params})
return True, has_mcp_tool, functions, None
def load_code(path: Path) -> str:
return path.read_text(encoding="utf-8", errors="ignore")
def discover_prediction_code(pred_root: Path, server_name: str) -> Path | None:
candidates = [
pred_root / f"{server_name}.py",
pred_root / server_name / "generated.py",
pred_root / server_name / f"{server_name}_server.py",
pred_root / f"mcp_{server_name}" / "app" / f"{server_name}_server.py",
]
for path in candidates:
if path.exists():
return path
return None
def discover_usage(pred_root: Path, server_name: str) -> dict[str, Any] | None:
candidates = [
pred_root / f"{server_name}.usage.json",
pred_root / server_name / "usage.json",
pred_root / server_name / "metadata.json",
]
for path in candidates:
if path.exists():
return json.loads(path.read_text(encoding="utf-8"))
return None
def flatten_params(functions: list[dict[str, Any]]) -> set[tuple[str, str]]:
items = set()
for fn in functions:
for param in fn["params"]:
items.add((fn["name"], param["name"]))
return items
def flatten_signatures(functions: list[dict[str, Any]]) -> dict[str, list[tuple[str, str, bool]]]:
result = {}
for fn in functions:
result[fn["name"]] = [(p["name"], p["annotation"], p["required"]) for p in fn["params"]]
return result
def f1(pred: set[Any], gold: set[Any]) -> tuple[float, float, float]:
if not pred and not gold:
return 1.0, 1.0, 1.0
if not pred:
return 0.0, 0.0, 0.0
if not gold:
return 0.0, 0.0, 0.0
hit = len(pred & gold)
precision = hit / len(pred)
recall = hit / len(gold)
if precision + recall == 0:
return precision, recall, 0.0
return precision, recall, 2 * precision * recall / (precision + recall)
def usage_tokens(usage: dict[str, Any] | None) -> tuple[int | None, int | None, int | None]:
if not usage:
return None, None, None
prompt = usage.get("prompt_tokens")
completion = usage.get("completion_tokens")
total = usage.get("total_tokens")
nested = usage.get("usage")
if isinstance(nested, dict):
prompt = prompt if prompt is not None else nested.get("prompt_tokens")
completion = completion if completion is not None else nested.get("completion_tokens")
total = total if total is not None else nested.get("total_tokens")
if total is None and isinstance(prompt, int) and isinstance(completion, int):
total = prompt + completion
return prompt, completion, total
def main() -> int:
parser = argparse.ArgumentParser()
parser.add_argument("--benchmark", type=Path, default=DEFAULT_BENCHMARK)
parser.add_argument("--pred-root", type=Path, required=True)
parser.add_argument("--out", type=Path, default=DEFAULT_OUTPUT)
args = parser.parse_args()
benchmark = json.loads(args.benchmark.read_text(encoding="utf-8"))
results = []
for item in benchmark["items"]:
server_name = item["server_name"]
gold_code = load_code(Path(item["gold_source_path"]))
pred_path = discover_prediction_code(args.pred_root, server_name)
usage = discover_usage(args.pred_root, server_name)
gold_ok, _, gold_functions, gold_err = extract_functions_from_code(gold_code)
pred_code = load_code(pred_path) if pred_path else ""
pred_ok, has_mcp_tool, pred_functions, pred_err = extract_functions_from_code(pred_code) if pred_path else (
False,
False,
[],
"missing prediction file",
)
gold_tools = {fn["name"] for fn in gold_functions}
pred_tools = {fn["name"] for fn in pred_functions}
tool_precision, tool_recall, tool_f1 = f1(pred_tools, gold_tools)
gold_params = flatten_params(gold_functions)
pred_params = flatten_params(pred_functions)
param_precision, param_recall, param_f1 = f1(pred_params, gold_params)
gold_signatures = flatten_signatures(gold_functions)
pred_signatures = flatten_signatures(pred_functions)
exact_signature_matches = sum(
1 for name, signature in gold_signatures.items() if pred_signatures.get(name) == signature
)
signature_exact_rate = exact_signature_matches / len(gold_signatures) if gold_signatures else 0.0
structural_pass = 1.0 if pred_ok and has_mcp_tool else 0.0
conversion_accuracy = (
0.25 * structural_pass
+ 0.35 * tool_f1
+ 0.25 * param_f1
+ 0.15 * signature_exact_rate
)
prompt_tokens, completion_tokens, total_tokens = usage_tokens(usage)
code_lines = effective_code_lines_from_text(pred_code) if pred_code else None
acc_per_1k_tokens = None
acc_per_100_loc = None
if isinstance(total_tokens, int) and total_tokens > 0:
acc_per_1k_tokens = conversion_accuracy * 1000 / total_tokens
if isinstance(code_lines, int) and code_lines > 0:
acc_per_100_loc = conversion_accuracy * 100 / code_lines
results.append(
{
"server_name": server_name,
"category": item["category"],
"complexity": item["complexity"],
"gold_tool_count": len(gold_functions),
"prediction_file": str(pred_path) if pred_path else None,
"syntax_valid": pred_ok,
"has_mcp_tool": has_mcp_tool,
"tool_precision": tool_precision,
"tool_recall": tool_recall,
"tool_f1": tool_f1,
"param_precision": param_precision,
"param_recall": param_recall,
"param_f1": param_f1,
"signature_exact_rate": signature_exact_rate,
"conversion_accuracy": conversion_accuracy,
"code_lines": code_lines,
"prompt_tokens": prompt_tokens,
"completion_tokens": completion_tokens,
"total_tokens": total_tokens,
"accuracy_per_1k_tokens": acc_per_1k_tokens,
"accuracy_per_100_loc": acc_per_100_loc,
"gold_error": gold_err,
"prediction_error": pred_err,
}
)
valid = [row for row in results if row["prediction_file"]]
def mean(key: str) -> float | None:
vals = [row[key] for row in valid if isinstance(row.get(key), (int, float))]
return sum(vals) / len(vals) if vals else None
acc_vals = [row["conversion_accuracy"] for row in valid if isinstance(row["conversion_accuracy"], (int, float))]
token_vals = [row["total_tokens"] for row in valid if isinstance(row["total_tokens"], int) and row["total_tokens"] > 0]
loc_vals = [row["code_lines"] for row in valid if isinstance(row["code_lines"], int) and row["code_lines"] > 0]
median_tokens = sorted(token_vals)[len(token_vals) // 2] if token_vals else None
median_loc = sorted(loc_vals)[len(loc_vals) // 2] if loc_vals else None
for row in valid:
if median_tokens and median_loc and row["total_tokens"] and row["code_lines"]:
token_norm = row["total_tokens"] / median_tokens
loc_norm = row["code_lines"] / median_loc
row["token_line_efficiency"] = row["conversion_accuracy"] / (0.6 * token_norm + 0.4 * loc_norm)
else:
row["token_line_efficiency"] = None
payload = {
"benchmark": str(args.benchmark),
"pred_root": str(args.pred_root),
"aggregate": {
"expected_server_count": len(benchmark["items"]),
"predicted_server_count": len(valid),
"mean_conversion_accuracy": mean("conversion_accuracy"),
"mean_tool_f1": mean("tool_f1"),
"mean_param_f1": mean("param_f1"),
"mean_signature_exact_rate": mean("signature_exact_rate"),
"mean_accuracy_per_1k_tokens": mean("accuracy_per_1k_tokens"),
"mean_accuracy_per_100_loc": mean("accuracy_per_100_loc"),
"mean_token_line_efficiency": mean("token_line_efficiency"),
"median_total_tokens": median_tokens,
"median_code_lines": median_loc,
},
"results": results,
}
args.out.parent.mkdir(parents=True, exist_ok=True)
args.out.write_text(json.dumps(payload, indent=2, ensure_ascii=False), encoding="utf-8")
print(json.dumps(payload["aggregate"], indent=2, ensure_ascii=False))
return 0
if __name__ == "__main__":
raise SystemExit(main())
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