diff --git a/README.md b/README.md index 1bdd63a86..063540e97 100644 --- a/README.md +++ b/README.md @@ -70,6 +70,7 @@ Just like a compass guides us on our journey, OpenCompass will guide you through ## 🚀 What's New +- **\[2024.04.26\]** We supported the evaluation of [ArenaHard](configs/eval_subjective_arena_hard.py) welcome to try!🔥🔥🔥. - **\[2024.04.22\]** We supported the evaluation of [LLaMA3](configs/models/hf_llama/hf_llama3_8b.py) 和 [LLaMA3-Instruct](configs/models/hf_llama/hf_llama3_8b_instruct.py), welcome to try! 🔥🔥🔥 - **\[2024.02.29\]** We supported the MT-Bench, AlpacalEval and AlignBench, more information can be found [here](https://opencompass.readthedocs.io/en/latest/advanced_guides/subjective_evaluation.html) - **\[2024.01.30\]** We release OpenCompass 2.0. Click [CompassKit](https://github.com/open-compass), [CompassHub](https://hub.opencompass.org.cn/home), and [CompassRank](https://rank.opencompass.org.cn/home) for more information ! diff --git a/README_zh-CN.md b/README_zh-CN.md index f399ed652..291524f9e 100644 --- a/README_zh-CN.md +++ b/README_zh-CN.md @@ -69,6 +69,7 @@ ## 🚀 最新进展 +- **\[2024.04.26\]** 我们支持了 [ArenaHard评测](configs/eval_subjective_arena_hard.py) 欢迎试用！🔥🔥🔥. - **\[2024.04.22\]** 我们支持了 [LLaMA3](configs/models/hf_llama/hf_llama3_8b.py) 和 [LLaMA3-Instruct](configs/models/hf_llama/hf_llama3_8b_instruct.py) 的评测，欢迎试用！🔥🔥🔥. - **\[2024.02.29\]** 我们支持了MT-Bench、AlpacalEval和AlignBench，更多信息可以在[这里](https://opencompass.readthedocs.io/en/latest/advanced_guides/subjective_evaluation.html)找到。 - **\[2024.01.30\]** 我们发布了OpenCompass 2.0。更多信息，请访问[CompassKit](https://github.com/open-compass)、[CompassHub](https://hub.opencompass.org.cn/home)和[CompassRank](https://rank.opencompass.org.cn/home)。 diff --git a/configs/datasets/subjective/arena_hard/arena_hard_scoring.py b/configs/datasets/subjective/arena_hard/arena_hard_scoring.py new file mode 100644 index 000000000..8312a7813 --- /dev/null +++ b/configs/datasets/subjective/arena_hard/arena_hard_scoring.py @@ -0,0 +1,72 @@ +from opencompass.openicl.icl_prompt_template import PromptTemplate +from opencompass.openicl.icl_retriever import ZeroRetriever +from opencompass.openicl.icl_inferencer import GenInferencer +from opencompass.openicl.icl_evaluator import LMEvaluator +from opencompass.datasets import ArenaHardDataset +from mmengine.config import read_base + +subjective_reader_cfg = dict( + input_columns=['question'], + output_column='judge', + ) + +subjective_all_sets = [ + "question", +] + + +subjective_datasets = [] + +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]]\"." + +judge_prompt = "<|User Prompt|>\n{question}\n\n<|The Start of Assistant A's Answer|>\n{prediction}\n<|The End of Assistant A's Answer|>\n\n<|The Start of Assistant B's Answer|>\n{prediction2}\n<|The End of Assistant B's Answer|>" + + +for _name in subjective_all_sets: + subjective_infer_cfg = dict( + prompt_template=dict( + type=PromptTemplate, + template=dict(round=[ + dict( + role='HUMAN', + prompt="{question}" + ), + ]), + ), + retriever=dict(type=ZeroRetriever), + inferencer=dict(type=GenInferencer, max_out_len=4096), + ) + + subjective_eval_cfg = dict( + evaluator=dict( + type=LMEvaluator, + prompt_template=dict( + type=PromptTemplate, + template=dict( + begin=[ + dict( + role='SYSTEM', + fallback_role='HUMAN', + prompt=system_prompt) + ], + round=[ + dict( + role='HUMAN', + prompt = judge_prompt + ), + ]), + ), + ), + pred_role="BOT", + ) + + subjective_datasets.append( + dict( + abbr=f"{_name}", + type=ArenaHardDataset, + path="./data/subjective/arena_hard", + name=_name, + reader_cfg=subjective_reader_cfg, + infer_cfg=subjective_infer_cfg, + eval_cfg=subjective_eval_cfg + )) \ No newline at end of file diff --git a/configs/eval_subjective_arena_hard.py b/configs/eval_subjective_arena_hard.py new file mode 100644 index 000000000..42d504780 --- /dev/null +++ b/configs/eval_subjective_arena_hard.py @@ -0,0 +1,104 @@ +from opencompass.models import HuggingFaceCausalLM +from copy import deepcopy +from opencompass.models import TurboMindModel +from mmengine.config import read_base + +from opencompass.models import HuggingFaceCausalLM, HuggingFace, HuggingFaceChatGLM3, OpenAI +from opencompass.partitioners import NaivePartitioner, SizePartitioner +from opencompass.partitioners.sub_naive import SubjectiveNaivePartitioner +from opencompass.partitioners.sub_size import SubjectiveSizePartitioner +from opencompass.runners import LocalRunner +from opencompass.runners import SlurmSequentialRunner +from opencompass.tasks import OpenICLInferTask +from opencompass.tasks.subjective_eval import SubjectiveEvalTask +from opencompass.summarizers import ArenaHardSummarizer + +with read_base(): + from .datasets.subjective.arena_hard.arena_hard_scoring import subjective_datasets + +api_meta_template = dict( + round=[ + dict(role='HUMAN', api_role='HUMAN'), + dict(role='BOT', api_role='BOT', generate=True), + ] +) + +_meta_template = dict( + round=[ + dict(role="HUMAN", begin="<|begin_of_text|>user<|end_header_id|>\n\n", end="<|eot_id|>"), + dict(role="BOT", begin="<|begin_of_text|>assistant<|end_header_id|>\n\n", end="<|eot_id|>", generate=True), + ], +) + +models = [ + dict( + type=HuggingFaceCausalLM, + abbr="llama-3-8b-instruct-hf", + path="meta-llama/Meta-Llama-3-8B-Instruct", + model_kwargs=dict(device_map="auto"), + tokenizer_kwargs=dict( + padding_side="left", + truncation_side="left", + use_fast=False, + ), + meta_template=_meta_template, + max_out_len=4096, + max_seq_len=2048, + batch_size=8, + run_cfg=dict(num_gpus=1, num_procs=1), + generation_kwargs={"eos_token_id": [128001, 128009]}, + batch_padding=True, + ) +] + +datasets = [*subjective_datasets] + +work_dir = 'outputs/arena_hard/' +# -------------Inferen Stage ---------------------------------------- + + +infer = dict( + partitioner=dict(type=SizePartitioner, max_task_size=1000000), + runner=dict( + type=LocalRunner, + max_num_workers=32, + task=dict(type=OpenICLInferTask)), +) + +judge_models = [dict( + abbr='GPT4-Turbo', + type=OpenAI, + path='gpt-4-1106-preview', + key='', + meta_template=api_meta_template, + query_per_second=1, + max_out_len=1024, + max_seq_len=4096, + batch_size=10, + retry=10, + temperature = 0, +)] + +## ------------- Evaluation Configuration +gpt4_0314 = dict( + abbr='gpt4-0314', + type=OpenAI, +) + +eval = dict( + partitioner=dict( + type=SubjectiveSizePartitioner, + max_task_size=1000000, + mode='m2n', + infer_order='double', + base_models=[gpt4_0314], + compare_models=models, + judge_models=judge_models, + ), + runner=dict(type=LocalRunner, max_num_workers=16, task=dict(type=SubjectiveEvalTask)), + given_pred = [{'abbr':'gpt4-0314', 'path':''}] +) + +summarizer = dict( + type=ArenaHardSummarizer +) \ No newline at end of file diff --git a/docs/en/advanced_guides/subjective_evaluation.md b/docs/en/advanced_guides/subjective_evaluation.md index dda9d6f5f..76c26e94e 100644 --- a/docs/en/advanced_guides/subjective_evaluation.md +++ b/docs/en/advanced_guides/subjective_evaluation.md @@ -15,10 +15,11 @@ We support the use of GPT-4 (or other JudgeLLM) for the subjective evaluation of ## Current Supported Subjective Evaluation Datasets -1. AlginBench (https://github.com/THUDM/AlignBench) +1. AlignBench (https://github.com/THUDM/AlignBench) 2. MTBench (https://github.com/lm-sys/FastChat) 3. AlpacaEvalv2 (https://github.com/tatsu-lab/alpaca_eval) -4. CompassArena (Internal dataset) +4. ArenaHard (https://github.com/lm-sys/arena-hard/tree/main) +5. CompassArena (Internal dataset) ## Subjective Evaluation with Custom Dataset diff --git a/docs/zh_cn/advanced_guides/subjective_evaluation.md b/docs/zh_cn/advanced_guides/subjective_evaluation.md index 565b03ea5..5c3de35c0 100644 --- a/docs/zh_cn/advanced_guides/subjective_evaluation.md +++ b/docs/zh_cn/advanced_guides/subjective_evaluation.md @@ -15,10 +15,11 @@ ## 目前已支持的主观评测数据集 -1. AlginBench（https://github.com/THUDM/AlignBench） +1. AlignBench（https://github.com/THUDM/AlignBench） 2. MTBench （https://github.com/lm-sys/FastChat） 3. AlpacaEvalv2 （https://github.com/tatsu-lab/alpaca_eval） -4. CompassArena（内部数据集） +4. ArenaHard (https://github.com/lm-sys/arena-hard/tree/main) +5. CompassArena（内部数据集） ## 自定义主观数据集评测 diff --git a/opencompass/datasets/subjective/__init__.py b/opencompass/datasets/subjective/__init__.py index 4711d5dab..1c4de400c 100644 --- a/opencompass/datasets/subjective/__init__.py +++ b/opencompass/datasets/subjective/__init__.py @@ -1,4 +1,5 @@ from .alignbench import AlignmentBenchDataset # noqa: F401, F403 +from .arena_hard import ArenaHardDataset # noqa: F401, F403 from .compass_arena import CompassArenaDataset # noqa: F401, F403 from .corev2 import Corev2Dataset # noqa: F401, F403 from .creationbench import CreationBenchDataset # noqa: F401, F403 diff --git a/opencompass/datasets/subjective/arena_hard.py b/opencompass/datasets/subjective/arena_hard.py new file mode 100644 index 000000000..59398e477 --- /dev/null +++ b/opencompass/datasets/subjective/arena_hard.py @@ -0,0 +1,34 @@ +import json +import os.path as osp + +from datasets import Dataset, DatasetDict + +from opencompass.registry import LOAD_DATASET + +from ..base import BaseDataset + + +@LOAD_DATASET.register_module() +class ArenaHardDataset(BaseDataset): + + def load(self, path: str, name: str): + filename = osp.join(path, f'{name}.jsonl') + dataset = DatasetDict() + raw_data = [] + with open(filename, 'r', encoding='utf-8') as file: + for line in file: + problem = json.loads(line) + question_id = problem['question_id'] + category = problem['category'] + cluster = problem['cluster'] + question = problem['turns'][0]['content'] # only one turn in arena_hard + raw_data.append({ + 'question': question, + 'capability': cluster, + 'judge': { + 'capability': cluster, + 'question': question + } + }) + dataset = Dataset.from_list(raw_data) + return dataset diff --git a/opencompass/runners/slurm_sequential.py b/opencompass/runners/slurm_sequential.py index cf61f23bb..3b4dcad52 100644 --- a/opencompass/runners/slurm_sequential.py +++ b/opencompass/runners/slurm_sequential.py @@ -188,7 +188,6 @@ def _launch(self, cfg: ConfigDict, child_conn: Pipe = None): tmpl += f' --gres=gpu:{num_gpus}' for extra_cmd in self.extra_command: tmpl += f' {extra_cmd}' - tmpl += ' -x HOST-10-140-60-7' tmpl += f" -N1 -u -J '{task_name[:512]}'" + ' {task_cmd}' get_cmd = partial(task.get_command, cfg_path=param_file, diff --git a/opencompass/summarizers/subjective/__init__.py b/opencompass/summarizers/subjective/__init__.py index 4fe21a33a..55b0042ce 100644 --- a/opencompass/summarizers/subjective/__init__.py +++ b/opencompass/summarizers/subjective/__init__.py @@ -1,6 +1,7 @@ # flake8: noqa: F401, E501 from .alignmentbench import AlignmentBenchSummarizer from .alpacaeval import AlpacaSummarizer +from .arenahard import ArenaHardSummarizer from .compass_arena import CompassArenaSummarizer from .corev2 import Corev2Summarizer from .creationbench import CreationBenchSummarizer diff --git a/opencompass/summarizers/subjective/arenahard.py b/opencompass/summarizers/subjective/arenahard.py new file mode 100644 index 000000000..3d24c6592 --- /dev/null +++ b/opencompass/summarizers/subjective/arenahard.py @@ -0,0 +1,320 @@ +# flake8: noqa +# yapf: disable +import os +import os.path as osp +import re +import numpy as np +#import plotly.express as px +import pandas as pd +import tiktoken +import datetime +import argparse +import os +import math +import tiktoken +from glob import glob +from tqdm import tqdm +import json +from sklearn.linear_model import LogisticRegression +from collections import defaultdict +from datetime import datetime +from itertools import product + +import mmengine +from mmengine import ConfigDict +from tabulate import tabulate + +from opencompass.partitioners.sub_naive import remove_duplicate_pairs +from opencompass.utils import dataset_abbr_from_cfg, model_abbr_from_cfg + +from .utils import get_outdir + + +def compute_mle_elo(df, SCALE=400, BASE=10, INIT_RATING=1000): + models = pd.concat([df["model_a"], df["model_b"]]).unique() + models = pd.Series(np.arange(len(models)), index=models) + + # duplicate battles + df = pd.concat([df, df], ignore_index=True) + p = len(models.index) + n = df.shape[0] + + X = np.zeros([n, p]) + X[np.arange(n), models[df["model_a"]]] = +math.log(BASE) + X[np.arange(n), models[df["model_b"]]] = -math.log(BASE) + + # one A win => two A win + Y = np.zeros(n) + Y[df["winner"] == "model_a"] = 1.0 + + # one tie => one A win + one B win + # find tie + tie (both bad) index + tie_idx = (df["winner"] == "tie") | (df["winner"] == "tie (bothbad)") + tie_idx[len(tie_idx)//2:] = False + Y[tie_idx] = 1.0 + + lr = LogisticRegression(fit_intercept=False, penalty=None, tol=1e-8) + lr.fit(X,Y) + + elo_scores = SCALE * lr.coef_[0] + INIT_RATING + + # set anchor as gpt4-0314 = 1000 + if "gpt4-0314" in models.index: + elo_scores += 1000 - elo_scores[models["gpt4-0314"]] + return pd.Series(elo_scores, index = models.index).sort_values(ascending=False) + + +def get_bootstrap_result(battles, func_compute_elo, num_round): + rows = [] + for i in tqdm(range(num_round), desc="bootstrap"): + rows.append(func_compute_elo(battles.sample(frac=1.0, replace=True))) + df = pd.DataFrame(rows) + return df[df.median().sort_values(ascending=False).index] + + +def preety_print_two_ratings(ratings_1, ratings_2, column_names): + df = pd.DataFrame([ + [n, ratings_1[n], ratings_2[n]] for n in ratings_1.keys() + ], columns=["Model", column_names[0], column_names[1]]).sort_values(column_names[0], ascending=False).reset_index(drop=True) + df[column_names[0]] = (df[column_names[0]] + 0.5).astype(int) + df[column_names[1]] = (df[column_names[1]] + 0.5).astype(int) + df.index = df.index + 1 + return df + + +def visualize_bootstrap_scores(df, title): + bars = pd.DataFrame(dict( + lower = df.quantile(.025), + rating = df.quantile(.5), + upper = df.quantile(.975))).reset_index(names="model").sort_values("rating", ascending=False) + bars['error_y'] = bars['upper'] - bars["rating"] + bars['error_y_minus'] = bars['rating'] - bars["lower"] + bars['rating_rounded'] = np.round(bars['rating'], 2) + fig = px.scatter(bars, x="model", y="rating", error_y="error_y", + error_y_minus="error_y_minus", text="rating_rounded", + title=title) + fig.update_layout(xaxis_title="Model", yaxis_title="Rating", + height=600) + return fig + + +def predict_win_rate(elo_ratings, SCALE=400, BASE=10, INIT_RATING=1000): + names = sorted(list(elo_ratings.keys())) + wins = defaultdict(lambda: defaultdict(lambda: 0)) + for a in names: + for b in names: + ea = 1 / (1 + BASE ** ((elo_ratings[b] - elo_ratings[a]) / SCALE)) + wins[a][b] = ea + wins[b][a] = 1 - ea + + data = { + a: [wins[a][b] if a != b else np.NAN for b in names] + for a in names + } + + df = pd.DataFrame(data, index=names) + df.index.name = "model_a" + df.columns.name = "model_b" + return df.T + + +def model_abbr_from_cfg_used_in_summarizer(model): + if model.get('summarizer_abbr', None): + return model['summarizer_abbr'] + else: + return model_abbr_from_cfg(model) + +def post_process_compass_arena(s): + if result := re.findall('\[\[([AB<>=]+)\]\]', s): + return result[0] + else: + return None + +def get_win_rate_column(df, column, baseline="gpt4-0314"): + to_dict = df[["model", column]].set_index("model").to_dict()[column] + win_rate_table = predict_win_rate(to_dict) + return win_rate_table[baseline].fillna(0.5).apply(lambda x: round(x * 100, 2)) + + +def get_battles_from_judgment(dataset, subdir_path, post_process, first_game_only=False, WEIGHT=3): + arena_hard_battles = pd.DataFrame() + print("Turning judgment results into battles...") + + dataset_abbr = dataset_abbr_from_cfg(dataset) + filename = osp.join(subdir_path, dataset_abbr + '.json') + partial_filename = osp.join(subdir_path, dataset_abbr + '_0.json') + if osp.exists(osp.realpath(filename)): + result = mmengine.load(filename) + elif osp.exists(osp.realpath(partial_filename)): + filename = partial_filename + result = {} + i = 1 + partial_dict_flag = 0 + while osp.exists(osp.realpath(filename)): + res = mmengine.load(filename) + for k, v in res.items(): + result[partial_dict_flag] = v + partial_dict_flag += 1 + filename = osp.join(subdir_path, + dataset_abbr + '_' + str(i) + '.json') + i += 1 + else: + result = {} + + if len(result) == 0: + print('*' * 100) + print('There are no results for ' + filename + ' or ' + + partial_filename) + print('*' * 100) + assert len(result) > 0 + + judged_answers = [] + references = [] + for k, v in result.items(): + + output = { + "model_a": v['gold']['answer1'], + "model_b": v['gold']['answer2']} + + processed_judge = post_process(v['prediction']) + if processed_judge is not None: + weight = 1 + if processed_judge == "A=B": + output["winner"] = "tie" + elif processed_judge == "A>B": + output["winner"] = "model_a" + elif processed_judge == "A>>B": + output["winner"] = "model_a" + weight = WEIGHT + elif processed_judge == "B>A": + output["winner"] = "model_b" + elif processed_judge == "B>>A": + output["winner"] = "model_b" + weight = WEIGHT + else: + weight = 0 + else: + weight = 0 + + if weight: + arena_hard_battles = pd.concat([arena_hard_battles, pd.DataFrame([output] * weight)]) + + arena_hard_battles.to_json(os.path.join(subdir_path,"arena_hard_battles.jsonl"), lines=True, orient="records") + return arena_hard_battles + +class ArenaHardSummarizer: + """Do the subjectivity analyze based on evaluation results. + + Args: + config (ConfigDict): The configuration object of the evaluation task. + It's expected to be filled out at runtime. + """ + + def __init__(self, + config: ConfigDict, + judge_type='general', + check_pos_bias=True, + summary_type='single') -> None: + self.tasks = [] + self.cfg = config + self.base_models = self.cfg['eval']['partitioner']['base_models'] + self.compare_models = self.cfg['eval']['partitioner']['compare_models'] + self.judge_models = self.cfg.get('judge_models', None) + self.meta_judge_model = self.cfg.eval.partitioner.get('meta_judge_model', None) + self.judge_type = judge_type + assert self.judge_type in ['general'] + self.judge_map = {'general': post_process_compass_arena} + self.judge_function = self.judge_map[self.judge_type] + self.check_pos_bias = check_pos_bias + self.summary_type = summary_type + + def get_score(self, time_str): + output_dir, results_folder = get_outdir(self.cfg, time_str) + model_combinations = list(product(self.base_models, self.compare_models)) + unique_combinations = remove_duplicate_pairs([combo for combo in model_combinations if combo[0] != combo[1]]) + + if self.meta_judge_model is not None: + self.judge_models.append(self.meta_judge_model) + + scores = {} + + for idx, judge_model_cfg in enumerate(self.judge_models): + judge_model = model_abbr_from_cfg(judge_model_cfg) + for dataset in self.cfg['datasets']: + dataset_abbr = dataset_abbr_from_cfg(dataset) + for model_pair in unique_combinations: + model1 = model_pair[0]['abbr'] + model2 = model_pair[1]['abbr'] + if idx == len(self.judge_models): + subdir = model1 + '_' + model2 + '_summarized-by--' + judge_model + else: + subdir = model1 + '_' + model2 + '_judged-by--' + judge_model + subdir_path = os.path.join(results_folder, subdir) + if not os.path.isdir(subdir_path): + print(subdir_path + ' is not exist! please check!') + continue + + battles = get_battles_from_judgment(dataset, subdir_path, self.judge_function) + + bootstrap_online_elo = compute_mle_elo(battles) + + np.random.seed(42) + bootstrap_elo_lu = get_bootstrap_result(battles, compute_mle_elo, 100) + bootstrap_elo_lu.to_json(os.path.join(subdir_path,"bootstrapping_results.jsonl"), lines=True, orient="records") + + stats = pd.DataFrame() + stats["results"] = None + stats["results"] = stats['results'].astype('object') + + for i, model in enumerate(bootstrap_online_elo.index): + assert model in bootstrap_elo_lu.columns + + stats.at[i, "model"] = model + stats.at[i, "score"] = bootstrap_online_elo[model] + stats.at[i, "lower"] = np.percentile(bootstrap_elo_lu[model], 2.5) + stats.at[i, "upper"] = np.percentile(bootstrap_elo_lu[model], 97.5) + if model == 'gpt4-0314': + stats.at[i, "avg_tokens"] = 423 + else: + with open(os.path.join(output_dir.split('summary')[0], 'predictions', model, dataset_abbr+'.json'), 'r') as f: + model_preds = json.load(f) + pred_length = 0 + for k, v in model_preds.items(): + pred_length += len(tiktoken.encoding_for_model("gpt-3.5-turbo").encode(v['prediction'])) + pred_length /= len(model_preds) + stats.at[i, "avg_tokens"] = pred_length + stats.at[i, "results"] = bootstrap_elo_lu[model].tolist() + stats.sort_values(by="model", inplace=True) + stats["score"] = get_win_rate_column(stats, "score", 'gpt4-0314').tolist() + stats["lower"] = get_win_rate_column(stats, "lower", 'gpt4-0314').tolist() + stats["upper"] = get_win_rate_column(stats, "upper", 'gpt4-0314').tolist() + decimal = 1 + + + stats.sort_values(by="score", ascending=False, inplace=True) + for _, row in stats.iterrows(): + interval = str((round(row['lower'] - row['score'], decimal), round(row['upper'] - row['score'], decimal))) + print(f"{row['model'] : <30} | score: {round(row['score'], decimal) : ^5} | 95% CI: {interval : ^12} | average #tokens: {int(row['avg_tokens'])}") + + stats.to_json(os.path.join(output_dir,"arena_hard_leaderboard.json"), orient="records", indent=4) + + def summarize( + self, + time_str: str = datetime.now().strftime('%Y%m%d_%H%M%S'), + ): + """Summarize the subjectivity analysis based on evaluation results. + + Args: + time_str (str): Timestamp for file naming. + + Returns: + pd.DataFrame: The summary results. + """ + self.get_score(time_str) + + + + + + +