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evals moved + readme
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phiraml
parent
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29 changed files with 390 additions and 696 deletions
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"""MathVista evaluation environment."""
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import asyncio
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import base64
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import io
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import os
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import re
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from typing import Dict, List, Optional, Tuple
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import openai
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from datasets import load_dataset
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from openai import AsyncOpenAI
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from PIL import Image
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from environments.eval_environments.eval_base import EvalBase, eval_runner
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ICL_EXAMPLES = [
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"""
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Hint: Please answer the question requiring an integer answer and provide the final value,
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e.g., 1, 2, 3, at the end.
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Question: Which number is missing?
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Model response: The number missing in the sequence is 14.
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Extracted answer: 14
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""",
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"""
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Hint: Please answer the question requiring a floating-point number with one decimal place and provide the final value,
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e.g., 1.2, 1.3, 1.4, at the end.
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Question: What is the fraction of females facing the camera?
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Model response: The fraction of females facing the camera is 0.6,
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which means that six out of ten females in the group are facing the camera.
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Extracted answer: 0.6
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""",
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"""
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Hint: Please answer the question requiring a floating-point number with two decimal places and provide the final value,
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e.g., 1.23, 1.34, 1.45, at the end.
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Question: How much money does Luca need to buy a sour apple candy and a butter-scotch candy? (Unit: $)
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Model response: Luca needs $1.45 to buy a sour apple candy and a butterscotch candy.
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Extracted answer: 1.45
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""",
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"""
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Hint: Please answer the question requiring a Python list as an answer and provide the final list,
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e.g., [1, 2, 3], [1.2, 1.3, 1.4], at the end.
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Question: Between which two years does the line graph saw its maximum peak?
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Model response: The line graph saw its maximum peak between 2007 and 2008.
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Extracted answer: [2007, 2008]
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""",
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"""
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Hint: Please answer the question and provide the correct option letter, e.g., A, B, C, D, at the end.
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Question: What fraction of the shape is blue?
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Choices: (A) 3/11 (B) 8/11 (C) 6/11 (D) 3/5
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Model response: The correct answer is (B) 8/11.
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Extracted answer: B
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""",
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]
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def build_extraction_prompt(question: str, prediction: str) -> str:
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task_description = """Please read the following example.
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Then extract the answer from the model response and type it at the end of the prompt.
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"""
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prompt = task_description
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for example in ICL_EXAMPLES:
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prompt += example + "\n"
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prompt += question + "\n"
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prompt += "Model response: " + prediction + "\n"
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prompt += "Extracted answer:"
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return prompt
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def can_infer_option(answer: str, choices: Dict[str, str]) -> Optional[str]:
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if "Failed to obtain answer via API" in answer:
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return None
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answer_mod = answer
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for c in ".()[],:;!*#{}":
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answer_mod = answer_mod.replace(c, " ")
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splits = [x.strip() for x in answer_mod.split()]
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count = sum(1 for ch in choices if ch in splits)
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if count == 1:
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for ch in choices:
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if "A" in splits and len(splits) > 3:
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continue
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if ch in splits and splits.index(ch) > (len(splits) - 5):
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return ch
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return None
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def can_infer_text(answer: str, choices: Dict[str, str]) -> Optional[str]:
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answer_lower = answer.lower()
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if len(answer_lower) > 2 * sum(len(str(v)) for v in choices.values()):
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return None
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cands = []
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for k, v in choices.items():
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if str(v).lower() in answer_lower:
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cands.append(k)
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if len(cands) == 1:
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return cands[0]
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return None
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def can_infer(answer: str, choices: Dict[str, str]) -> Optional[str]:
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answer = str(answer)
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result = can_infer_option(answer, choices)
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if result:
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return result
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return can_infer_text(answer, choices)
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class MathVista(EvalBase):
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TASK_TYPES = ["FQA", "GPS", "MWP", "TQA", "VQA"]
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SKILL_TYPES = ["ALG", "ARI", "GEO", "LOG", "NUM", "SCI", "STA"]
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def setup_data(self) -> list:
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split = getattr(self, "split", "testmini")
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dataset = load_dataset("AI4Math/MathVista", split=split)
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print(f"Loaded {len(dataset)} examples from MathVista ({split})")
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return list(dataset)
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def encode_image(self, pil_image: Image.Image) -> str:
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buffer = io.BytesIO()
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pil_image.save(buffer, format="PNG")
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return base64.b64encode(buffer.getvalue()).decode("utf-8")
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def get_image_base64(self, item: dict) -> str:
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if "decoded_image" in item and item["decoded_image"] is not None:
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return self.encode_image(item["decoded_image"])
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if "image" in item and item["image"] is not None:
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if isinstance(item["image"], Image.Image):
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return self.encode_image(item["image"])
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raise ValueError(f"Could not find image for item {item.get('pid', 'unknown')}")
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def build_messages(self, item: dict) -> List[dict]:
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image_base64 = self.get_image_base64(item)
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use_query = getattr(self, "use_query", True)
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if use_query and "query" in item:
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prompt = item["query"]
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else:
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prompt = self._build_custom_prompt(item)
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return [
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{
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"role": "user",
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"content": [
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{
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"type": "image_url",
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"image_url": {"url": f"data:image/png;base64,{image_base64}"},
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},
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{"type": "text", "text": prompt},
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],
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}
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]
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def _build_custom_prompt(self, item: dict) -> str:
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question = item.get("question", "")
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question_type = item.get("question_type", "free_form")
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answer_type = item.get("answer_type", "text")
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precision = item.get("precision", 2)
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if question_type == "multi_choice":
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choices = item.get("choices", [])
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choices_text = "\n".join(choices) if choices else ""
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hint = (
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"Please answer the question and provide the correct option letter, "
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"e.g., A, B, C, D, at the end."
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)
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return f"Hint: {hint}\nQuestion: {question}\nChoices:\n{choices_text}"
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if answer_type == "integer":
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hint = (
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"Please answer the question requiring an integer answer "
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"and provide the final value, e.g., 1, 2, 3, at the end."
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)
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elif answer_type == "float":
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hint = (
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f"Please answer the question requiring a floating-point number "
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f"with {precision} decimal place(s) and provide the final value at the end."
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)
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elif answer_type == "list":
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hint = (
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"Please answer the question requiring a Python list as an answer "
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"and provide the final list, e.g., [1, 2, 3], at the end."
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)
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else:
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hint = "Please answer the question and provide the final answer at the end."
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return f"Hint: {hint}\nQuestion: {question}"
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def _prefetch_answer(self, response: str, item: dict) -> Tuple[Optional[str], bool]:
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question_type = item.get("question_type", "free_form")
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answer_type = item.get("answer_type", "text")
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if question_type == "multi_choice":
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choices_list = item.get("choices", [])
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if choices_list:
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choices = {chr(65 + i): val for i, val in enumerate(choices_list)}
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result = can_infer(response, choices)
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if result:
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return result, True
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# Fallback: find last letter
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for char in reversed(response.upper()):
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if char in "ABCDEFGH":
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return char, True
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return None, False
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response = response.strip()
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if answer_type == "integer":
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numbers = re.findall(r"-?\d+", response)
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if numbers:
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return numbers[-1], True
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elif answer_type == "float":
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numbers = re.findall(r"-?\d+\.?\d*", response)
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if numbers:
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return numbers[-1], True
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elif answer_type == "list":
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match = re.search(r"\[[\d\.,\s-]+\]", response)
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if match:
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return match.group(0), True
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return None, False
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async def _extract_with_gpt(self, question: str, response: str) -> Optional[str]:
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judge_model = getattr(self, "judge_model", "gpt-4o-mini")
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judge_base_url = getattr(self, "judge_base_url", "https://api.openai.com/v1")
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judge_api_key = os.environ.get(
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getattr(self, "judge_api_key_env", "OPENAI_API_KEY"), ""
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)
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if not judge_api_key:
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return None
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try:
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judge_client = openai.AsyncOpenAI(
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api_key=judge_api_key,
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base_url=judge_base_url,
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)
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prompt = build_extraction_prompt(question, response)
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completion = await judge_client.chat.completions.create(
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model=judge_model,
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messages=[{"role": "user", "content": prompt}],
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temperature=0.0,
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max_tokens=128,
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)
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result = completion.choices[0].message.content.strip()
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return result if result else None
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except Exception as e:
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print(f"GPT extraction error: {e}")
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return None
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def extract_answer(
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self, response: str, answer_type: str, question_type: str
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) -> str:
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response = response.strip()
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if question_type == "multi_choice":
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for char in reversed(response.upper()):
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if char in "ABCDEFGH":
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return char
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return ""
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if answer_type == "integer":
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numbers = re.findall(r"-?\d+", response)
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return numbers[-1] if numbers else ""
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if answer_type == "float":
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numbers = re.findall(r"-?\d+\.?\d*", response)
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return numbers[-1] if numbers else ""
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if answer_type == "list":
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match = re.search(r"\[[\d\.,\s-]+\]", response)
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return match.group(0) if match else ""
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return response
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def score(
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self, prediction: str, answer: str, answer_type: str, precision: int = 0
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) -> bool:
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pred = prediction.strip()
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ans = answer.strip()
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if not pred:
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return False
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if answer_type == "text":
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return pred.upper() == ans.upper()
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if answer_type == "integer":
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try:
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return int(float(pred)) == int(float(ans))
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except (ValueError, OverflowError):
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return False
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if answer_type == "float":
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try:
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tolerance = 10 ** (-precision) if precision > 0 else 0.01
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return abs(float(pred) - float(ans)) < tolerance
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except ValueError:
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return False
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if answer_type == "list":
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try:
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pred_list = eval(pred)
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ans_list = eval(ans)
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return pred_list == ans_list
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except Exception:
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return False
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return pred.lower() == ans.lower()
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async def run_item(self, client: AsyncOpenAI, data_item: dict) -> Tuple[dict, dict]:
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try:
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messages = self.build_messages(data_item)
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gen_params = self.get_generation_params()
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completion = await client.chat.completions.create(
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model=self.model_name,
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messages=messages,
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temperature=gen_params["temperature"],
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max_tokens=gen_params["max_tokens"],
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)
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if not completion.choices:
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return {"accuracy": 0.0}, {"error": "Empty response"}
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message = completion.choices[0].message
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response = message.content or ""
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if hasattr(message, "reasoning") and message.reasoning and not response:
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response = message.reasoning
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if not response and hasattr(message, "model_extra"):
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reasoning = message.model_extra.get("reasoning", "")
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if reasoning:
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response = reasoning
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if not response:
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return {"accuracy": 0.0}, {"error": "Empty response"}
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answer_type = data_item.get("answer_type", "text")
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question_type = data_item.get("question_type", "free_form")
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precision = data_item.get("precision", 0)
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use_gpt_extraction = getattr(self, "use_gpt_extraction", True)
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prefetch_result, prefetch_success = self._prefetch_answer(
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response, data_item
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)
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if prefetch_success and prefetch_result:
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extracted = prefetch_result
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extraction_method = "prefetch"
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elif use_gpt_extraction:
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question = data_item.get("query", data_item.get("question", ""))
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gpt_result = await self._extract_with_gpt(question, response)
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if gpt_result:
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extracted = gpt_result
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extraction_method = "gpt"
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else:
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extracted = self.extract_answer(
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response, answer_type, question_type
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)
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extraction_method = "regex_fallback"
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else:
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extracted = self.extract_answer(response, answer_type, question_type)
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extraction_method = "regex"
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answer = data_item.get("answer", "")
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correct = self.score(extracted, answer, answer_type, precision)
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sample = {
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"pid": data_item.get("pid", ""),
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"question": data_item.get("question", ""),
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"answer": answer,
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"prediction": extracted,
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"raw_response": response[:500],
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"correct": correct,
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"question_type": question_type,
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"answer_type": answer_type,
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"extraction_method": extraction_method,
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}
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return {"accuracy": 1.0 if correct else 0.0}, sample
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except Exception as e:
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return {"accuracy": 0.0}, {"error": str(e)}
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if __name__ == "__main__":
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asyncio.run(
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eval_runner(
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MathVista,
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split="testmini",
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use_query=True,
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use_gpt_extraction=True,
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judge_model="gpt-4o-mini",
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temperature=0.0,
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max_tokens=4096,
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)
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)
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