Download source/workbench/grading.py from ajinkyamulay/analyst-workbench: direct link, hf CLI and curl.
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https://huggingface.co/datasets/ajinkyamulay/analyst-workbench/resolve/main/source/workbench/grading.py
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hf download hf://datasets/ajinkyamulay/analyst-workbench/source/workbench/grading.py
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curl -L -o grading.py https://huggingface.co/datasets/ajinkyamulay/analyst-workbench/resolve/main/source/workbench/grading.py
2.7 kB
| """Answer parsing shared by the task verifiers. Verifiers never trust the agent's format.""" | |
| import math | |
| import re | |
| from dataclasses import dataclass, field | |
| from typing import Callable | |
| class TaskInstance: | |
| instruction: str | |
| files: dict[str, str] | |
| grade: Callable[[str], float] | |
| oracle: str | |
| # Plausible but wrong answers, used only by tests. | |
| wrong: list[str] = field(default_factory=list) | |
| # Extra data for tests and workspace graders (never shown to the agent). | |
| meta: dict = field(default_factory=dict) | |
| def norm(text: str) -> str: | |
| return " ".join(str(text).strip().strip("`'\"").lower().split()) | |
| def to_number(text: str): | |
| cleaned = re.sub(r"[\s,$€£%]", "", str(text)).strip("`'\"") | |
| cleaned = re.sub(r"(usd|eur|mm|mmol/l|g|s|sec|seconds)$", "", cleaned, flags=re.I) | |
| try: | |
| value = float(cleaned) | |
| except ValueError: | |
| return None | |
| return value if math.isfinite(value) else None | |
| def number_close(text: str, target: float, abs_tol: float = 0.0, rel_tol: float = 0.0) -> bool: | |
| value = to_number(text) | |
| if value is None: | |
| return False | |
| return abs(value - target) <= max(abs_tol, rel_tol * abs(target)) + 1e-9 | |
| def split_parts(text: str, count: int, sep: str = ","): | |
| parts = [p.strip() for p in str(text).strip().strip("`'\"").split(sep)] | |
| return parts if len(parts) == count else None | |
| def token_set(text: str): | |
| body = norm(text).strip("[](){}") | |
| if body == "": | |
| return None # an empty submission never matches, even when the answer is "none" | |
| if body in ("none", "no", "nothing"): | |
| return frozenset() | |
| return frozenset(t.strip().strip("'\"") for t in re.split(r"[,\s;]+", body) if t.strip()) | |
| def parts_score(text: str, checks: list[Callable[[str], bool]], sep: str = ",") -> float: | |
| """Mean of per-part checks; 0 when the part count is wrong.""" | |
| parts = split_parts(text, len(checks), sep) | |
| if parts is None and sep == ",": # tolerate thousands separators such as 12,345.67 | |
| parts = split_parts(re.sub(r"(?<=\d),(?=\d{3}(?!\d))", "", str(text)), len(checks), sep) | |
| if parts is None: | |
| return 0.0 | |
| return sum(1.0 for part, check in zip(parts, checks) if check(part)) / len(checks) | |
| def exact(target: str) -> Callable[[str], bool]: | |
| return lambda part: norm(part) == norm(target) | |
| def close(target: float, abs_tol: float = 0.0, rel_tol: float = 0.0) -> Callable[[str], bool]: | |
| return lambda part: number_close(part, target, abs_tol, rel_tol) | |
| def csv_text(header: list[str], rows: list[list]) -> str: | |
| lines = [",".join(header)] | |
| lines += [",".join(str(v) for v in row) for row in rows] | |
| return "\n".join(lines) + "\n" | |