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import os
import regex
import zipfile
import requests
from math import isclose
from typing import Any, List, Callable
from sympy import N, simplify
from sympy.parsing.latex import parse_latex
from sympy.parsing.sympy_parser import parse_expr
from ..core.logging import logger
from .benchmark import Benchmark
from ..utils.utils import make_parent_folder
from ..core.module_utils import load_json
from ..utils.aflow_utils.data_utils import AFLOW_DATASET_FILES_MAP, download_aflow_benchmark_data
def download_raw_math_data(save_folder: str):
"""
Download the MATH data from the modelscope website.
"""
url = "https://www.modelscope.cn/datasets/opencompass/competition_math/resolve/master/data/MATH.zip"
logger.info(f"Downloading MATH data from {url} ...")
save_file_path = os.path.join(save_folder, "MATH.zip")
make_parent_folder(save_file_path)
if not os.path.exists(save_file_path):
response = requests.get(url, stream=True)
response.raise_for_status()
with open(save_file_path, "wb") as file:
for chunk in response.iter_content(chunk_size=1024):
if chunk:
file.write(chunk)
with zipfile.ZipFile(save_file_path, "r") as zip_ref:
zip_ref.extractall(save_folder)
if os.path.exists(save_file_path):
os.remove(save_file_path)
class MATH(Benchmark):
"""Benchmark class for evaluating mathematical reasoning on the MATH dataset.
MATH is a dataset of challenging competition mathematics problems,
spanning various difficulty levels and subject areas. This class handles
loading the dataset, extracting answers, evaluating solutions through
symbolic and numerical comparisons, and computing accuracy metrics.
The dataset includes problems across 7 subject areas (Algebra, Geometry, etc.)
and 5 difficulty levels. Each problem contains LaTeX-formatted
questions and solutions.
Each MATH example has the following structure:
{
"id": "test-1",
"problem": "the problem",
"solution": "the solution",
"level": "Level 1", # "Level 1", "Level 2", "Level 3", "Level 4", "Level 5", "Level ?"
"type": "Algebra", # 'Geometry', 'Algebra', 'Intermediate Algebra', 'Counting & Probability', 'Precalculus', 'Number Theory', 'Prealgebra'
}
The benchmark evaluates answers using symbolic math equality checking
and numerical approximation to handle equivalent mathematical expressions.
"""
def __init__(self, path: str = None, mode: str = "all", **kwargs):
path = os.path.expanduser(path or "~/.evoagentx/data/math")
super().__init__(name=type(self).__name__, path=path, mode=mode, **kwargs)
def _load_data_from_folders(self, data_folder: str) -> List[dict]:
if data_folder is None:
return None
data = []
typ = "train" if "train" in data_folder else "test"
sub_data_folders = os.listdir(data_folder)
i = 0
logger.info(f"loading MATH data from {data_folder} ...")
for sub_data_folder in sub_data_folders:
if os.path.isdir(os.path.join(data_folder, sub_data_folder)):
files = os.listdir(os.path.join(data_folder, sub_data_folder))
for file in files:
if file.endswith(".json"):
example = {"id": f"{typ}-{i+1}"}
example.update(load_json(os.path.join(data_folder, sub_data_folder, file), type="json"))
data.append(example)
i += 1
return data
def _load_data(self):
if not os.path.exists(os.path.join(self.path, "MATH")):
download_raw_math_data(save_folder=self.path)
data_folder = os.path.join(self.path, "MATH")
# load data
if self.mode == "train" or self.mode == "all":
self._train_data = self._load_data_from_folders(data_folder=os.path.join(data_folder, "train"))
if self.mode == "dev" or self.mode == "all":
self._dev_data = None
if self.mode == "test" or self.mode == "all":
self._test_data = self._load_data_from_folders(data_folder=os.path.join(data_folder, "test"))
def _get_label(self, example: Any) -> Any:
return example["solution"]
def _get_id(self, example: Any) -> Any:
return example["id"]
def extract_answer(self, text: str) -> str:
pattern = r"\\boxed{((?:[^{}]|{[^{}]*})*)}"
boxed_matches = regex.findall(pattern, text, regex.DOTALL)
if boxed_matches:
return boxed_matches[-1].strip()
sentence_end_pattern = r"(?<!\d)[.!?]\s+"
sentences = regex.split(sentence_end_pattern, text)
sentences = [s.strip() for s in sentences if s.strip()]
return sentences[-1] if sentences else ""
# Acknowledgement: https://github.com/geekan/MetaGPT/blob/main/metagpt/ext/aflow/benchmark/math.py#L40
def math_equal(self, prediction: Any, reference: Any) -> bool:
if str(prediction) == str(reference):
return True
try:
if self.is_digit(prediction) and self.is_digit(reference):
prediction = self.parse_digits(prediction)
reference = self.parse_digits(reference)
return isclose(prediction, reference, abs_tol=1e-3)
except Exception:
pass
try:
return self.symbolic_equal(prediction, reference)
except Exception:
pass
return False
def is_digit(self, num: Any) -> bool:
return self.parse_digits(num) is not None
def parse_digits(self, num: Any) -> float:
num = regex.sub(",", "", str(num))
try:
return float(num)
except Exception:
if num.endswith("%"):
num = num[:-1]
if num.endswith("\\"):
num = num[:-1]
try:
return float(num) / 100
except Exception:
pass
return None
def symbolic_equal(self, a: Any, b: Any) -> bool:
def _parse(s: Any) -> Any:
for f in [parse_latex, parse_expr]:
try:
return f(s)
except Exception:
pass
return s
a = _parse(a)
b = _parse(b)
try:
if simplify(a - b) == 0:
return True
except Exception:
pass
try:
if isclose(N(a), N(b), abs_tol=1e-3):
return True
except Exception:
pass
return False
def evaluate(self, prediction: Any, label: Any) -> dict:
ground_truth_answer = self.extract_answer(label)
predicted_answer = self.extract_answer(prediction)
solve_rate = 1.0 if self.math_equal(predicted_answer, ground_truth_answer) else 0.0
return {"solve_rate": solve_rate}
class AFlowMATH(MATH):
def __init__(self, path: str = None, mode: str = "all", **kwargs):
path = os.path.expanduser(path or "~/.evoagentx/data/aflow/math")
super().__init__(path=path, mode=mode, **kwargs)
def _load_data_from_file(self, file_name: str):
if file_name is None:
return None
file_path = os.path.join(self.path, file_name)
if not os.path.exists(file_path):
download_aflow_benchmark_data(dataset="math", save_folder=self.path)
return load_json(path=file_path, type="jsonl")
def _load_data(self):
if self.mode == "train" or self.mode == "all":
logger.info(f"Loading train data from {AFLOW_DATASET_FILES_MAP['math']['train']}")
self._train_data = self._load_data_from_file(file_name=AFLOW_DATASET_FILES_MAP["math"]["train"])
if self.mode == "dev" or self.mode == "all":
logger.info(f"Loading dev data from {AFLOW_DATASET_FILES_MAP['math']['dev']}")
self._dev_data = self._load_data_from_file(file_name=AFLOW_DATASET_FILES_MAP["math"]["dev"])
if self.mode == "test" or self.mode == "all":
logger.info(f"Loading test data from {AFLOW_DATASET_FILES_MAP['math']['test']}")
self._test_data = self._load_data_from_file(file_name=AFLOW_DATASET_FILES_MAP["math"]["test"])
async def async_evaluate(self, graph: Callable, example: Any) -> float:
problem = example["problem"]
label = self._get_label(example)
output = await graph(problem)
metrics = await super().async_evaluate(prediction=output, label=label)
return metrics["solve_rate"]
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