412 lines
19 KiB
Python
412 lines
19 KiB
Python
import glob
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import os
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from collections import defaultdict
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from typing import Any, List
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from evalscope.benchmarks import Benchmark, DataAdapter
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from evalscope.constants import EvalType
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from evalscope.metrics import Metric, mean, metric_registry
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from evalscope.report import Report, ReportKey
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from evalscope.utils.logger import get_logger
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# flake8: noqa
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logger = get_logger()
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GRADER_SYSTEM_PROMPT = "Please act as an impartial judge and evaluate the quality of the responses provided by two AI assistants to the user prompt displayed below. You will be given assistant A's answer and assistant B's answer. Your job is to evaluate which assistant's answer is better.\n\nBegin your evaluation by generating your own answer to the prompt. You must provide your answers before judging any answers.\n\nWhen evaluating the assistants' answers, compare both assistants' answers with your answer. You must identify and correct any mistakes or inaccurate information.\n\nThen consider if the assistant's answers are helpful, relevant, and concise. Helpful means the answer correctly responds to the prompt or follows the instructions. Note when user prompt has any ambiguity or more than one interpretation, it is more helpful and appropriate to ask for clarifications or more information from the user than providing an answer based on assumptions. Relevant means all parts of the response closely connect or are appropriate to what is being asked. Concise means the response is clear and not verbose or excessive.\n\nThen consider the creativity and novelty of the assistant's answers when needed. Finally, identify any missing important information in the assistants' answers that would be beneficial to include when responding to the user prompt.\n\nAfter providing your explanation, you must output only one of the following choices as your final verdict with a label:\n\n1. Assistant A is significantly better: [[A>>B]]\n2. Assistant A is slightly better: [[A>B]]\n3. Tie, relatively the same: [[A=B]]\n4. Assistant B is slightly better: [[B>A]]\n5. Assistant B is significantly better: [[B>>A]]\n\nExample output: \"My final verdict is tie: [[A=B]]\"." # noqa: E501
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GRADER_TEMPLATE = "<|User Prompt|>\n{question}\n\n<|The Start of Assistant A's Answer|>\n{answer_1}\n<|The End of Assistant A's Answer|>\n\n<|The Start of Assistant B's Answer|>\n{answer_2}\n<|The End of Assistant B's Answer|>".strip(
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) # noqa: E501
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@Benchmark.register(
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name='general_arena',
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pretty_name='GeneralArena',
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tags=['Custom', 'Arena'],
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description=
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'GeneralArena is a custom benchmark designed to evaluate the performance of large language models in a competitive setting, '
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'where models are pitted against each other in custom tasks to determine their relative strengths and weaknesses. You should '
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'provide the model outputs in the format of a list of dictionaries, where each dictionary contains the model name and its report path. '
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'For detailed instructions on how to use this benchmark, please refer to the [Arena User Guide](https://evalscope.readthedocs.io/zh-cn/latest/user_guides/arena.html).',
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dataset_id='general_arena',
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metric_list=['winrate'],
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few_shot_num=0,
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train_split=None,
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eval_split='test',
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system_prompt=GRADER_SYSTEM_PROMPT,
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prompt_template=GRADER_TEMPLATE,
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extra_params={
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'models': [{
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'name': 'qwen-plus',
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'report_path': 'outputs/20250627_172550/reports/qwen-plus'
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}, {
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'name': 'qwen2.5-7b',
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'report_path': 'outputs/20250627_172817/reports/qwen2.5-7b-instruct'
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}],
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'baseline':
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'qwen2.5-7b'
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})
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class GeneralArenaAdapter(DataAdapter):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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# register metrics
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metric_registry.register(Metric(name='winrate', object=mean))
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# whether to use LLM as a judge
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self.llm_as_a_judge = True
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extra_params = kwargs.get('extra_params', {})
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self.models = extra_params.get('models', [])
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self.baseline = extra_params.get('baseline', None)
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def load(self, **kwargs):
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self._check_names()
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self._check_reports()
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self._check_datasets()
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logger.info(f'Overall datasets: {self.overall_datasets}')
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dataset_model_dict = self._load_common_datasets()
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data_dict = self._build_pair_wise_data(dataset_model_dict)
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return data_dict
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def gen_prompt(self, input_d, subset_name, few_shot_list, **kwargs):
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return self.gen_prompt_data(input_d['question'])
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def _check_names(self):
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"""Check the names of the models and baseline."""
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# check duplicate models
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model_names = [model['name'] for model in self.models]
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if len(model_names) != len(set(model_names)):
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raise ValueError(f'Duplicate model names found in the models list {model_names}.')
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# check if models list is empty
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if len(self.models) < 2:
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raise ValueError('Models list must contain at least two models.')
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# check baseline model
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if self.baseline and self.baseline not in model_names:
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raise ValueError(f'Baseline model {self.baseline} not found in the models list.')
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# check if the baseline model is not set
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if not self.baseline:
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logger.warning('Baseline model is not set. Using the first model as the baseline.')
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self.baseline = self.models[0]['name']
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def _check_reports(self):
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"""Check if the report paths are valid."""
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for model in self.models:
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report_path = model.get('report_path', None)
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if not report_path or not os.path.exists(report_path):
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raise ValueError(f'Report path {report_path} for model {model["name"]} does not exist.')
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reports = []
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for report_item in glob.glob(os.path.join(report_path, '*.json')):
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report = Report.from_json(report_item)
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reports.append(report)
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model['reports'] = reports
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def _check_datasets(self):
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"""Check common datasets in the reports."""
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overall_datasets = set()
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for model in self.models:
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datasets = set()
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for report in model['reports']:
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report_df = report.to_dataframe()
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# get unique (dataset, subset) tuples
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unique_datasets = set(zip(report_df[ReportKey.dataset_name], report_df[ReportKey.subset_name]))
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datasets.update(unique_datasets)
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model['datasets'] = datasets
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# get overall datasets by intersecting all models' datasets
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overall_datasets = set.intersection(*[model['datasets'] for model in self.models if 'datasets' in model])
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self.overall_datasets = overall_datasets
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def _load_common_datasets(self):
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"""Load common datasets from the local path."""
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from evalscope.utils import OutputsStructure, jsonl_to_list
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dataset_dict = defaultdict(dict)
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for dataset_name, subset_name in self.overall_datasets:
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for model in self.models:
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dataset_path = model['report_path'].replace(OutputsStructure.REPORTS_DIR, OutputsStructure.REVIEWS_DIR)
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dataset_file_path = os.path.join(dataset_path, f'{dataset_name}_{subset_name}.jsonl')
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if not os.path.exists(dataset_file_path):
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raise ValueError(
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f'Dataset {dataset_name} with subset {subset_name} not found in model {model["name"]}.')
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dataset = jsonl_to_list(dataset_file_path)
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# sort by index
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dataset.sort(key=lambda x: x.get('index'))
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dataset_dict[(dataset_name, subset_name)][model['name']] = dataset
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return dataset_dict
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def _build_pair_wise_data(self, dataset_dict):
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"""Build pairwise data for the models."""
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from .utils import process_review_item
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pairwise_data = defaultdict(dict)
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for (dataset_name, subset_name), model_data in dataset_dict.items():
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if len(model_data) < 2:
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logger.warning(f'Not enough models for dataset {dataset_name} with subset {subset_name}. Skipping.')
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continue
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# create pairwise data for each model against the baseline
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model_names = list(model_data.keys())
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for name in model_names:
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if name == self.baseline:
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continue
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pairs = []
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for model_item, baseline_item in zip(model_data[name], model_data[self.baseline]):
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for model_choice, baseline_choice in zip(
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process_review_item(model_item), process_review_item(baseline_item)):
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pairs.append({
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'question': model_choice['Question'],
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'answer_1': model_choice['Generated'],
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'answer_2': baseline_choice['Generated'],
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'model_1': name,
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'model_2': self.baseline
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})
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pairwise_data[f'{dataset_name}&{subset_name}@{name}&{self.baseline}'][self.eval_split] = pairs
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return pairwise_data
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def llm_match(self, gold, pred, judge=None, **kwargs):
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from .utils import get_judge_score, post_process_result
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try:
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raw_input = kwargs.get('raw_input', None)
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question = raw_input['question']
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answer_1 = raw_input['answer_1']
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answer_2 = raw_input['answer_2']
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model_1 = raw_input['model_1']
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model_2 = raw_input['model_2']
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except KeyError as e:
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logger.error(f'Missing key in raw input: {e}. Raw input: {raw_input}')
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raise
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system_template = self.system_prompt
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prompt_template = self.prompt_template
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prompt1 = prompt_template.format(question=question, answer_1=answer_1, answer_2=answer_2)
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# reverse the order
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prompt2 = prompt_template.format(question=question, answer_1=answer_2, answer_2=answer_1)
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# get grading response
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game1_response = judge(prompt1, system_prompt=system_template)
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game2_response = judge(prompt2, system_prompt=system_template)
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# parse grading response
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# game1
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res1 = post_process_result(game1_response)
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score1 = get_judge_score(res1, reverse=False)
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# game2
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res2 = post_process_result(game2_response)
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score2 = get_judge_score(res2, reverse=True)
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return {
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'score':
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mean([score1, score2]),
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'games': [
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{
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'model_a': model_1,
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'model_b': model_2,
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'response': game1_response,
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'judgment': res1
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},
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{
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'model_a': model_2,
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'model_b': model_1,
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'response': game2_response,
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'judgment': res2
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},
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]
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}
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def compute_metric(self, review_res_list: List[dict], **kwargs) -> List[dict]:
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"""
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compute score of the model
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"""
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import numpy as np
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import pandas as pd
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from .utils import compute_mle_elo, get_battles_from_row, get_bootstrap_result, get_win_rate_column
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if isinstance(review_res_list[0], list):
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review_res_list = [item for sublist in review_res_list for item in sublist]
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battles = pd.concat([get_battles_from_row(res) for res in review_res_list])
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bt_model_coef = compute_mle_elo(battles, baseline_model=self.baseline)
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bootstrap_model_coef = get_bootstrap_result(
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battles, func_compute_elo=compute_mle_elo, num_round=100, baseline_model=self.baseline)
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stats = pd.DataFrame()
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stats['results'] = None
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stats['results'] = stats['results'].astype('object')
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for i, model in enumerate(bt_model_coef.index):
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# assert model in bootstrap_elo_lu.columns
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stats.at[i, 'model'] = model
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stats.at[i, 'score'] = bt_model_coef[model]
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stats.at[i, 'lower'] = np.percentile(bootstrap_model_coef[model], 2.5)
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stats.at[i, 'upper'] = np.percentile(bootstrap_model_coef[model], 97.5)
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metrics_dict = {}
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metrics_dict['winrate'] = get_win_rate_column(stats, 'score', self.baseline).to_dict()
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metrics_dict['winrate_lower'] = get_win_rate_column(stats, 'lower', self.baseline).to_dict()
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metrics_dict['winrate_upper'] = get_win_rate_column(stats, 'upper', self.baseline).to_dict()
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metrics = []
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for metric_name, models in metrics_dict.items():
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for model_name, score in models.items():
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if model_name == self.baseline:
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continue
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metrics.append({'metric_name': metric_name, 'score': score, 'num': len(review_res_list)})
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return metrics
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def post_process_report(self, report: 'Report', **kwargs):
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"""Post-process the report to convert it to a DataFrame with winrate leaderboards."""
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import pandas as pd
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import tabulate
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report_path = kwargs.get('report_path')
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leaderboard_file = os.path.join(report_path, 'leaderboard.txt')
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# Ensure report directory exists
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os.makedirs(report_path, exist_ok=True)
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# Convert report to dataframe
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df = report.to_dataframe()
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# Filter for winrate-related metrics
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winrate_df = df[df[ReportKey.metric_name].str.contains('winrate')].copy()
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if winrate_df.empty:
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logger.warning('No winrate data found in the report.')
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return
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# Get all model names from self.models
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all_model_names = [model['name'] for model in self.models]
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# Collect all leaderboard outputs
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leaderboard_outputs = []
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def format_leaderboard(data_df, title):
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"""Format DataFrame as leaderboard with CI."""
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# Pivot to get winrate, winrate_lower, winrate_upper as columns
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pivot_df = data_df.pivot_table(
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index=[ReportKey.model_name], columns=ReportKey.metric_name, values=ReportKey.score, aggfunc='first')
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# Add baseline model with 50% winrate
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baseline_data = {'winrate': 0.5, 'winrate_lower': 0.5, 'winrate_upper': 0.5}
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# Create a complete index with all models
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complete_index = pd.Index(all_model_names, name=pivot_df.index.name)
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pivot_df = pivot_df.reindex(complete_index)
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# Fill baseline model data
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if self.baseline in pivot_df.index:
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for col, val in baseline_data.items():
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if col in pivot_df.columns:
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pivot_df.loc[self.baseline, col] = val
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# Fill missing values with winrate score for other models
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if 'winrate' in pivot_df.columns:
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pivot_df['winrate_lower'] = pivot_df.get('winrate_lower', pivot_df['winrate'])
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pivot_df['winrate_upper'] = pivot_df.get('winrate_upper', pivot_df['winrate'])
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# Format for display
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leaderboard_data = []
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for model in pivot_df.index:
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if pd.isna(pivot_df.loc[model, 'winrate']):
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continue
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score_pct = pivot_df.loc[model, 'winrate'] * 100
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lower_diff = (pivot_df.loc[model, 'winrate_lower'] - pivot_df.loc[model, 'winrate']) * 100
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upper_diff = (pivot_df.loc[model, 'winrate_upper'] - pivot_df.loc[model, 'winrate']) * 100
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leaderboard_data.append({
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'Model': model,
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'WinRate (%)': f'{score_pct:.1f}',
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'CI (%)': f'({lower_diff:+.1f} / {upper_diff:+.1f})'
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})
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# Sort by score descending
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leaderboard_data.sort(key=lambda x: float(x['WinRate (%)'].replace('%', '')), reverse=True)
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# Create DataFrame
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leaderboard_df = pd.DataFrame(leaderboard_data)
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leaderboard_df.index = range(len(leaderboard_df))
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# Format as string
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table_str = tabulate.tabulate(leaderboard_df, headers='keys', showindex=False)
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output = f'{title}\n{table_str}\n'
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logger.info(f'\n{title}\n{table_str}')
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return output
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# Parse dataset and subset information from dataset_name column
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# Format: '{dataset_name}&{subset_name}@{name}&{self.baseline}'
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def parse_dataset_key(dataset_key):
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"""Parse dataset key to extract dataset_name, subset_name, and model pair."""
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parts = dataset_key.split('@')
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dataset_subset = parts[0]
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model_pair = parts[1]
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dataset_name, subset_name = dataset_subset.split('&', 1)
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model_1, model_2 = model_pair.split('&', 1)
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return dataset_name, subset_name, model_1, model_2
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# Add parsed columns
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parsed_data = []
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for _, row in winrate_df.iterrows():
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dataset_name, subset_name, model_1, model_2 = parse_dataset_key(row[ReportKey.subset_name])
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if dataset_name is not None:
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parsed_data.append({
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'dataset_name': dataset_name,
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'subset_name': subset_name,
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ReportKey.model_name: model_1,
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ReportKey.metric_name: row[ReportKey.metric_name],
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ReportKey.score: row[ReportKey.score]
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})
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if not parsed_data:
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logger.warning('No valid dataset keys found for parsing.')
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return
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parsed_df = pd.DataFrame(parsed_data)
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# 1. Overall ranking (aggregate across all datasets and subsets)
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overall_df = parsed_df.groupby([ReportKey.model_name,
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ReportKey.metric_name])[ReportKey.score].mean().reset_index()
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leaderboard_outputs.append(format_leaderboard(overall_df, '=== OVERALL LEADERBOARD ==='))
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# 2. Dataset-level rankings
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datasets = parsed_df['dataset_name'].unique()
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for dataset in sorted(datasets):
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dataset_df = parsed_df[parsed_df['dataset_name'] == dataset]
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dataset_agg = dataset_df.groupby([ReportKey.model_name,
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ReportKey.metric_name])[ReportKey.score].mean().reset_index()
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leaderboard_outputs.append(format_leaderboard(dataset_agg, f'=== DATASET LEADERBOARD: {dataset} ==='))
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# 3. Subset-level rankings
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subsets = parsed_df[['dataset_name', 'subset_name']].drop_duplicates()
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for _, subset_row in subsets.iterrows():
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dataset_name = subset_row['dataset_name']
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subset_name = subset_row['subset_name']
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subset_df = parsed_df[(parsed_df['dataset_name'] == dataset_name)
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& (parsed_df['subset_name'] == subset_name)]
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leaderboard_outputs.append(
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format_leaderboard(subset_df, f'=== SUBSET LEADERBOARD: {dataset_name} - {subset_name} ==='))
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# Write all leaderboard outputs to file
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with open(leaderboard_file, 'w', encoding='utf-8') as f:
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f.write('\n'.join(leaderboard_outputs))
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logger.info(f'Leaderboard results saved to: {leaderboard_file}')
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def get_gold_answer(self, input_d):
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return f"model_1: {input_d['model_1']}\n---\n" + input_d['answer_1']
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def llm_parse_pred_result(self, result, raw_input_d=None, eval_type=EvalType.CHECKPOINT):
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return f"model_2: {raw_input_d['model_2']}\n---\n" + raw_input_d['answer_2']
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def match(self, gold, pred):
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logger.warning(f'Please use LLMJudge to match the result for {self.name}')
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return
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