mirror of https://github.com/hpcaitech/ColossalAI
773 lines
28 KiB
Python
773 lines
28 KiB
Python
import concurrent.futures
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import os
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import re
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import time
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from copy import deepcopy
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from typing import Any, Dict, List
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import matplotlib.pyplot as plt
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import numpy as np
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import openai
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import pandas as pd
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import seaborn as sns
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import tqdm
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from utils import jdump, jload
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ref_step_template = {
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"en":
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"Now please compare the answer with the {adjective} answer, determine whether the answer is able to achieve the same level of {metric}.\n\n",
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"cn":
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"请比较答案与上面的{adjective}答案，确定答案是否可以达到与该{adjective}答案同样水平的{metric}。\n\n"
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}
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ref_answer_template_general = {
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"en": "\nAn example answer with good quality is as follows:\n\n{answer}\n\n",
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"cn": "\n一个优质的示例答案如下：\n\n{answer}\n\n"
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}
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ref_answer_template_correctness = {
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"en": "\nA correct answer is as follows:\n\n{answer}\n\n",
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"cn": "\n标准答案如下：\n\n{answer}\n\n"
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}
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def get_battle_result(sys_prompt: str, user_prompt: str, id: int, max_tokens: int = 2048) -> Dict[str, Any]:
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"""
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Get battle evaluation from GPT-4.
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Args:
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sys_prompt: prompt for the system.
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user_prompt: prompt for the user.
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id: id of the answers for comparison.
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max_tokens: the maximum number of tokens to generate in the chat completion.
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Returns:
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An evaluation of one comparison.
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"""
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MAX_API_RETRY = 3
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for _ in range(MAX_API_RETRY):
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try:
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response = openai.ChatCompletion.create(
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model="gpt-4",
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messages=[
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{
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"role": "system",
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"content": sys_prompt
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},
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{
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"role": "user",
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"content": user_prompt,
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},
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],
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temperature=0.2,
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max_tokens=max_tokens,
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)
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evaluation = response["choices"][0]["message"]["content"]
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return {"evaluation": evaluation, "id": id}
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except Exception as e:
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print(e)
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time.sleep(1)
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print(f"Evaluation {id} failed after {MAX_API_RETRY} retries.")
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return {"evaluation": "", "id": id}
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def parse_battle_score(evaluation: str) -> List[float]:
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"""
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Parse evaluation from GPT-4 and get the scores of model 1 and 2.
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Args:
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evaluation: evaluation from GPT-4.
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Returns:
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A score pair of two different model answers.
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"""
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try:
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pattern = re.compile("([0-9]|10) out of 10")
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sp = re.findall(pattern, evaluation)
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if len(re.findall(pattern, evaluation)) == 2:
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return [float(sp[0]), float(sp[1])]
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pattern = re.compile("a score of ([0-9]|10)")
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sp = re.findall(pattern, evaluation)
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if len(re.findall(pattern, evaluation)) == 2:
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return [float(sp[0]), float(sp[1])]
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pattern = re.compile("([0-9]|10)/10")
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sp = re.findall(pattern, evaluation)
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if len(re.findall(pattern, evaluation)) == 2:
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return [float(sp[0]), float(sp[1])]
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score_pair = evaluation.split("\n")[0]
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score_pair = score_pair.replace(",", " ")
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sp = score_pair.split(" ")
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if len(sp) == 2:
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return [float(sp[0]), float(sp[1])]
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else:
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raise Exception(f"Invalid score pair. Got {evaluation}.")
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except Exception as e:
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return [-1, -1]
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def battle(answer1: List[Dict], answer2: List[Dict], prompt_dict: Dict[str, Any]) -> List[Dict]:
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"""
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Use GPT-4 to compare answers of two different models.
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Args:
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answer1: answers of model 1.
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answer2: answers of model 2.
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prompt_dict: prompt for battle.
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Returns:
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Evaluations of all comparison pairs.
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"""
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assert len(answer1) == len(answer2)
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handles = []
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evaluation_file = []
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total_len = len(answer1)
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question_idx_list = list(range(total_len))
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print(f" Total number of answers: {len(answer1)}.")
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evaluations = []
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with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor:
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futures = []
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for i in question_idx_list:
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assert answer1[i]["id"] == answer2[i]["id"]
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answer_id = answer1[i]["id"]
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ques = answer1[i]["instruction"] if answer1[i][
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"input"] == "" else answer1[i]["instruction"] + " " + answer1[i]["input"]
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cat = answer1[i]["category"]
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ans1 = answer1[i]["output"]
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ans2 = answer2[i]["output"]
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sys_prompt = prompt_dict["system_prompt"]
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prompt_template = prompt_dict["prompt_template"]
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prompt = prompt_template.format(
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question=ques,
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answer_1=ans1,
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answer_2=ans2,
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prompt=prompt_dict["prompt"],
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)
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future = executor.submit(get_battle_result, sys_prompt, prompt, answer_id, 2048)
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futures.append(future)
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for future in tqdm.tqdm(concurrent.futures.as_completed(futures), total=len(futures)):
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evaluations.append(future.result())
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evaluations.sort(key=lambda x: x["id"])
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return evaluations
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def save_battle_results(evaluations: List[Dict], name1: str, name2: str, save_path: str) -> None:
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"""
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Save evaluation results (model 1 vs model 2) from GPT-4.
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Args:
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evaluations: evaluation results from GPT-4.
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name1: model 1 's name.
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name2: model 2 's name.
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save_path: path to save battle results.
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"""
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evaluation_file = deepcopy(evaluations)
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ans1_score = 0
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ans2_score = 0
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better_count = 0
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worse_count = 0
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tie_count = 0
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invalid_count = 0
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better_file = []
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worse_file = []
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tie_file = []
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invalid_file = []
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for idx, evaluation in enumerate(evaluations):
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scores = parse_battle_score(evaluation["evaluation"])
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evaluation_file[idx]["score"] = scores
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if scores[0] == -1 and scores[1] == -1:
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invalid_count += 1
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invalid_file.append(evaluation_file[idx])
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print(f'Invalid score pair: {evaluation_file[idx]["id"]}.')
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else:
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if scores[0] > scores[1]:
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worse_count += 1
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worse_file.append(evaluation_file[idx])
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elif scores[0] < scores[1]:
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better_count += 1
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better_file.append(evaluation_file[idx])
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else:
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tie_count += 1
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tie_file.append(evaluation_file[idx])
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ans1_score += scores[0]
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ans2_score += scores[1]
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prefix = f"{name1}_vs_{name2}"
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if not os.path.exists(save_path):
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os.makedirs(save_path)
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jdump(better_file, os.path.join(save_path, prefix, f"{name2}_better.json"))
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jdump(worse_file, os.path.join(save_path, prefix, f"{name2}_worse.json"))
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jdump(tie_file, os.path.join(save_path, prefix, f"{prefix}_tie.json"))
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jdump(invalid_file, os.path.join(save_path, prefix, f"{prefix}_invalid.json"))
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jdump(evaluation_file, os.path.join(save_path, prefix, f"{prefix}_evaluations.json"))
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if os.path.exists(os.path.join(save_path, "battle_results.json")):
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results = jload(os.path.join(save_path, "battle_results.json"))
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else:
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results = {}
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results[prefix] = {
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"model": [name1, name2],
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"better": better_count,
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"worse": worse_count,
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"tie": tie_count,
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"win_rate": better_count / (len(evaluations) - invalid_count),
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"score": [
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ans1_score / (len(evaluations) - invalid_count),
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ans2_score / (len(evaluations) - invalid_count),
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],
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}
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jdump(results, os.path.join(save_path, "battle_results.json"))
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print(f"Total {invalid_count} invalid score pair(s).")
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print(f"Model {name2} has {better_count} better answer(s).")
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print(f"Model {name2} has {worse_count} worse answer(s).")
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print(f"{tie_count} answer(s) play(s) to a tie.")
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print(f"Win rate of model {name2}: {better_count/(len(evaluations)-invalid_count):.2f}")
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print(f"Model {name1} average score: {ans1_score/(len(evaluations)-invalid_count):.2f}")
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print(f"Model {name2} average score: {ans2_score/(len(evaluations)-invalid_count):.2f}")
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def reference_template(metric: str, language: str, reference: Dict[str, Any]) -> str:
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"""
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Get prompt template for GPT evaluation with reference.
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Different languages have different prompt templates.
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Args:
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metric: metric used in GPT evaluation with reference.
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language: language for the template.
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reference: the instruction that contains target answer.
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Returns:
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Prompt template for GPT evaluation with reference.
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"""
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step_to_add = ref_step_template[language]
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for_the_given_answer = "{metric} (1-5) (directly give the score for the given answer):" if language == "en" else "{metric} (1-5) (直接对给定答案打分)"
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# adjective is used to describe the word "answer" in the prompt.
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adjective = "example" if language == "en" else "示例"
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answer_to_add = ref_answer_template_general[language]
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# Only for correctness, we will provide a correct answer and so the adjective for "answer" will be "correct". The prompt words will be "a correct answer".
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# In other cases, the prompt words will be "an example answer with good quality" by default.
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if metric.lower() == "correctness":
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adjective = "correct" if language == "en" else "标准"
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answer_to_add = ref_answer_template_correctness[language]
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answer_to_add = answer_to_add.format(answer=reference["target"] if reference["target"] else reference["output"])
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step_to_add = step_to_add.format(metric=metric.lower(),
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adjective=adjective) + for_the_given_answer.format(metric=metric)
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return answer_to_add + step_to_add
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def fill_in_message(role: str, content: str) -> Dict[str, str]:
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"""
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Generate one formatted message to send through chat completion.
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Args:
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role: the role of the author of this message.
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content: the contents of the message.
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Returns:
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One message to send through chat completion.
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"""
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return {"role": role, "content": content}
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def multiturn_chat_completion(user_messages: List[str], model: str, max_tokens: int = 1, turns=2) -> Dict[str, Any]:
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"""
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Do multi-turn chat completion.
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When turns == 1, it is a one-turn conversation for normal GPT evaluation.
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When turns == 2, it is a two-turn conversation which is used for GPT evaluation with reference answers.
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Args:
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user_messages: messages user wants to send.
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model: the model used to evaluate answers.
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max_tokens: the maximum number of tokens to generate in the chat completion.
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turns: the number of turns for conversation.
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Returns:
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Last turn's response.
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"""
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if len(user_messages) != turns:
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raise Exception("The length of user messages should be equal to the turn number!")
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assistant_responses = []
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for i in range(turns):
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messages_to_send = []
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for j in range(i):
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messages_to_send.append(fill_in_message("user", user_messages[j]))
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messages_to_send.append(
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fill_in_message("assistant", assistant_responses[j]["choices"][0]["message"]["content"]))
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# Length of user messages == Length of assistant messages + 1
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# Because we always expect the api to response
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messages_to_send.append(fill_in_message("user", user_messages[i]))
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response = openai.ChatCompletion.create(
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model=model,
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messages=messages_to_send,
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temperature=0,
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max_tokens=max_tokens,
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)
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# Avoid exceeding rate limits.
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# You can comment this line if your request doesn't contain many tokens.
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time.sleep(1)
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assistant_responses.append(response)
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return assistant_responses[-1]
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def get_gpt_evaluation_without_logprobs(prompt: Dict[str, Any],
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inst: Dict[str, Any],
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metrics: List[str],
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language: str,
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reference: Dict[str, Any] = None,
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model: str = "gpt-3.5-turbo",
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max_tokens: int = 2048) -> Dict[str, Any]:
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"""
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Use chat models(gpt-3.5-turbo or gpt-4) to evaluate one model answer.
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Temperature is set to 0 to make the model more deterministic.
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Args:
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prompt: a dictionary including prompt template, CoT and metrics.
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inst: the instruction that is needed to be evaluated.
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metrics: the metrics for evaluation.
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language: language used to change the CoT(add one more step about comparing the given answer and reference) if reference is not None.
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reference: the reference answer.
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model: the model used to evaluate answers.
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max_tokens: the maximum number of tokens to generate in the chat completion.
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Returns:
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An evaluation of one answer.
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"""
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MAX_API_RETRY = 3
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question = (inst["instruction"] if inst["input"] == "" else inst["instruction"] + "\n" + inst["input"])
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answer = inst["output"]
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inst["evaluation"] = {}
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for metric in metrics:
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if prompt["metrics"].get(metric, None) is None:
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raise Exception(
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f"Unsupported metric {metric} for category {inst['category']}! You should add this metric in the prompt file!"
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)
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for i in range(MAX_API_RETRY):
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try:
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prompt_reference = "" if reference is None else reference_template(metric, language, reference)
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prompt_1st_round = prompt["prompt"].format(
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question=question,
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answer=answer,
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metric=prompt["metrics"][metric],
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steps=prompt["CoT"][metric],
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)
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if prompt_reference:
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# Do a 2-round conversation
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response = multiturn_chat_completion([prompt_1st_round, prompt_reference],
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model,
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max_tokens=max_tokens,
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turns=2)
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else:
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response = multiturn_chat_completion([prompt_1st_round], model, max_tokens=max_tokens, turns=1)
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inst["evaluation"][metric] = {
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"response": response["choices"][0]["message"]["content"],
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"logprobs": None,
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}
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# Prevent exceeding rate limits because we have multiple workers.
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# But this will slow down the evaluation process.
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# You can comment this line if your request doesn't contain many tokens.
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time.sleep(len(metrics) * 0.5)
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break
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except Exception as e:
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print(e)
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time.sleep(1)
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if metric not in inst["evaluation"]:
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print(f"Evaluation {inst['id']} for metric {metric} failed after {MAX_API_RETRY} retries.")
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inst["evaluation"][metric] = {}
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return inst
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def get_gpt_evaluation_with_logprobs(prompt: Dict[str, Any],
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inst: Dict[str, Any],
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metrics: List[str],
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max_tokens: int = 2048) -> Dict[str, Any]:
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"""
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Use completion model(text-davinci-003) to evaluate one model answer.
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Only completion models can return log probabilities.
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Temperature is set to 0 to make the model more deterministic.
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Args:
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prompt: a dictionary including prompt template, CoT and metrics.
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inst: the instruction that is needed to be evaluated.
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metrics: the metrics for evaluation.
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max_tokens: the maximum number of tokens to generate in the completion.
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Returns:
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An evaluation of one answer.
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"""
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MAX_API_RETRY = 3
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question = (inst["instruction"] if inst["input"] == "" else inst["instruction"] + "\n" + inst["input"])
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answer = inst["output"]
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inst["evaluation"] = {}
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for metric in metrics:
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if prompt["metrics"].get(metric, None) is None:
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raise Exception(
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f"Unsupported metric {metric} for category {inst['category']}! You should add this metric in the prompt file!"
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)
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for i in range(MAX_API_RETRY):
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try:
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response = openai.Completion.create(
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model="text-davinci-003",
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prompt=prompt["prompt"].format(
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question=question,
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answer=answer,
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metric=prompt["metrics"][metric],
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steps=prompt["CoT"][metric],
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),
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logprobs=5,
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temperature=0,
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max_tokens=max_tokens,
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)
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inst["evaluation"][metric] = {
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"response": response["choices"][0]["text"],
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"logprobs": response["choices"][0]["logprobs"]["top_logprobs"],
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}
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# Prevent exceeding rate limits because we have multiple workers.
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# But this will slow down the evaluation process.
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# You can comment this line if your request doesn't contain many tokens.
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time.sleep(len(metrics) * 0.5)
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break
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except Exception as e:
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print(e)
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time.sleep(1)
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if metric not in inst["evaluation"]:
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print(f"Evaluation {inst['id']} for metric {metric} failed after {MAX_API_RETRY} retries.")
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inst["evaluation"][metric] = {}
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return inst
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def evaluate(answers: List[Dict],
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prompt: Dict[str, Any],
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metrics: List[str],
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category: str,
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model: str,
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language: str,
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references: List[Dict] = None) -> List[Dict]:
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"""
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Use GPT models to evaluate model answers and save evaluation results.
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Args:
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answers: model answers.
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prompt: prompt for GPT evaluation.
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metrics: metrics for GPT evaluation.
|
|
category: the category of the model answers for evaluation.
|
|
model: the specific GPT model used to evaluate answers.
|
|
language: language used in GPT evaluation
|
|
references: references for GPT evaluation
|
|
|
|
Returns:
|
|
Evaluations of the given answers.
|
|
"""
|
|
|
|
print(f"The number of instances of category {category}'s is {len(answers)}.")
|
|
|
|
evaluations = []
|
|
|
|
metrics_str = ", ".join(x for x in metrics)
|
|
print(f"Category {category}'s metrics are {metrics_str}.")
|
|
|
|
with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor:
|
|
futures = []
|
|
for idx, inst in enumerate(answers):
|
|
# Completion models can return log probabilities.
|
|
if model == "text-davinci-003":
|
|
future = executor.submit(get_gpt_evaluation_with_logprobs, prompt, inst, metrics, 1)
|
|
else:
|
|
future = executor.submit(get_gpt_evaluation_without_logprobs,
|
|
prompt,
|
|
inst,
|
|
metrics,
|
|
language,
|
|
reference=None if references is None else references[idx],
|
|
model=model,
|
|
max_tokens=1)
|
|
|
|
futures.append(future)
|
|
|
|
for future in tqdm.tqdm(
|
|
concurrent.futures.as_completed(futures),
|
|
desc=f"{category}: ",
|
|
total=len(futures),
|
|
):
|
|
evaluations.append(future.result())
|
|
|
|
evaluations.sort(key=lambda x: x["id"])
|
|
|
|
print(f"{category} done.")
|
|
|
|
return evaluations
|
|
|
|
|
|
def calculate_scores_form_logprobs(logprobs: Dict[str, Any]) -> float:
|
|
"""
|
|
Calculate the score according to log probabilities returned by text-davinci-003.
|
|
|
|
Calculation formula:
|
|
score = sum(score_i * exp(value)) where score_i is the score which corresponds to the key(predicted token) and value is its log probability.
|
|
|
|
Ref: https://arxiv.org/abs/2303.16634
|
|
This paper proposes NLG evaluation methods using text-davinci-003(log probabilities returned by completion models) and GPT-4(probabilities obtained by sampling).
|
|
|
|
Args:
|
|
logprobs: logprobs returned by openai.Completion.
|
|
|
|
Returns:
|
|
The score of one answer.
|
|
"""
|
|
|
|
# GPT-3.5 only returns score of 1 to 5.
|
|
prob = np.zeros(5)
|
|
|
|
for key, value in logprobs.items():
|
|
# Sometimes the key will be one byte of a unicode character which takes the form of "bytes:\\xe7".
|
|
# It is meaningless and thus we don't calculate probability.
|
|
if "bytes" in key:
|
|
continue
|
|
# results[0] is the score which corresponds to the key(predicted token).
|
|
# For example, key "5" corresponds to score 5.
|
|
results = re.findall(r"\d", key)
|
|
if len(results) == 1:
|
|
prob[int(results[0]) - 1] = prob[int(results[0]) - 1] + np.exp(value)
|
|
|
|
score = np.dot(np.arange(1, 6), prob)
|
|
|
|
return score
|
|
|
|
|
|
def calculate_scores_form_response(response: str, evaluation: Dict[str, Any]) -> int:
|
|
"""
|
|
Calculate the score from the response returned by gpt-3.5-turbo or gpt-4.
|
|
Different from text-davinci-003, this function directly calculates the score according to the plain response returned by gpt-3.5-turbo or gpt-4.
|
|
Although text-davinci-003 can return log probabilities, it costs ten times as much as gpt-3.5-turbo.
|
|
|
|
Args:
|
|
response: logprobs returned by openai.Completion.
|
|
evaluation: the evaluation corresponds to the question.
|
|
|
|
Returns:
|
|
The score of one answer.
|
|
"""
|
|
|
|
try:
|
|
results = re.findall(r"\d", response)
|
|
if len(results) == 1:
|
|
return int(results[0])
|
|
else:
|
|
raise Exception(f"Invalid score pair. Got {evaluation}.")
|
|
except Exception as e:
|
|
return 0
|
|
|
|
|
|
def save_gpt_evaluation_results(model_name: str, gpt_evaluation_results: Dict[str, Any],
|
|
save_path: str) -> Dict[str, Any]:
|
|
"""
|
|
Save evaluation results for different categories for one model.
|
|
|
|
Args:
|
|
model_name: name of the model for saving evaluation results.
|
|
gpt_evaluation_results: evaluations results for all of the model answers.
|
|
save_path: path to save GPT evaluation statistics.
|
|
"""
|
|
|
|
all_evaluations = []
|
|
for category, evaluations in gpt_evaluation_results.items():
|
|
jdump(evaluations, os.path.join(save_path, model_name, f"{category}_evaluation_results.json"))
|
|
all_evaluations.extend(evaluations)
|
|
|
|
jdump(all_evaluations, os.path.join(save_path, f"{model_name}_evaluation_results.json"))
|
|
|
|
return all_evaluations
|
|
|
|
|
|
def save_gpt_evaluation_statistics(model_name: str, evaluations: List[Dict], save_path: str) -> None:
|
|
"""
|
|
Generate statistics for one model.
|
|
|
|
Args:
|
|
model_name: name of the model for saving statistics.
|
|
evaluations: evaluations for all of the model answers.
|
|
save_path: path to save GPT evaluation statistics.
|
|
"""
|
|
|
|
if not os.path.exists(save_path):
|
|
os.makedirs(save_path)
|
|
|
|
data_per_category = {}
|
|
for evaluation in evaluations:
|
|
category = evaluation["category"]
|
|
if evaluation["category"] in data_per_category.keys():
|
|
data_per_category[category].append(evaluation)
|
|
else:
|
|
data_per_category[category] = [evaluation]
|
|
|
|
all_statistics = {}
|
|
for category, data in data_per_category.items():
|
|
metrics = data[0]["evaluation"].keys()
|
|
scores = {metric: [] for metric in metrics}
|
|
for evaluation in data:
|
|
for metric in metrics:
|
|
if evaluation["evaluation"][metric] == {}:
|
|
# This means after 3 retries, the server still returns an error and we set the score to 0.
|
|
scores[metric].append(0)
|
|
elif evaluation["evaluation"][metric]["logprobs"] is not None:
|
|
scores[metric].append(
|
|
calculate_scores_form_logprobs(evaluation["evaluation"][metric]["logprobs"][0]))
|
|
else:
|
|
scores[metric].append(
|
|
calculate_scores_form_response(evaluation["evaluation"][metric]["response"], evaluation))
|
|
|
|
statistics = {}
|
|
for metric in metrics:
|
|
arg_sort = np.argsort(scores[metric])
|
|
statistics[metric] = {}
|
|
statistics[metric]["avg_score"] = sum(scores[metric]) / len(data)
|
|
statistics[metric]["best_3"] = {data[i]["id"]: scores[metric][i] for i in arg_sort[-3:][::-1]}
|
|
statistics[metric]["worst_3"] = {data[i]["id"]: scores[metric][i] for i in arg_sort[:3]}
|
|
|
|
all_statistics[category] = statistics
|
|
|
|
jdump(
|
|
all_statistics,
|
|
os.path.join(save_path, f"{model_name}_evaluation_statistics.json"),
|
|
)
|
|
|
|
|
|
def analyze_gpt_evaluation_statistics(statistics_path: str, save_path: str) -> None:
|
|
"""
|
|
Analyze and visualize all GPT evaluation statistics in the given directory.
|
|
|
|
Args:
|
|
statistics_path: path to all the models' statistics.
|
|
save_path: path to save table and visualization results.
|
|
"""
|
|
|
|
if not os.path.exists(statistics_path):
|
|
raise Exception(f'The given directory "{statistics_path}" doesn\'t exist! No statistics found!')
|
|
|
|
all_statistics = {}
|
|
|
|
for file_name in os.listdir(statistics_path):
|
|
if file_name.endswith("_evaluation_statistics.json"):
|
|
model_name = file_name.split("_evaluation_statistics.json")[0]
|
|
all_statistics[model_name] = jload(os.path.join(statistics_path, file_name))
|
|
|
|
if len(list(all_statistics.keys())) == 0:
|
|
raise Exception(f'There are no statistics in the given directory "{statistics_path}"!')
|
|
|
|
frame_all = {
|
|
"model": [],
|
|
"category": [],
|
|
"metric": [],
|
|
"avg_score": [],
|
|
"best_3": [],
|
|
"worst_3": [],
|
|
}
|
|
frame_per_category = {}
|
|
for model_name, model_statistics in all_statistics.items():
|
|
for category, category_statistics in model_statistics.items():
|
|
if frame_per_category.get(category) is None:
|
|
frame_per_category[category] = {
|
|
"model": [],
|
|
"metric": [],
|
|
"avg_score": [],
|
|
"best_3": [],
|
|
"worst_3": [],
|
|
}
|
|
|
|
for metric, metric_statistics in category_statistics.items():
|
|
frame_all["model"].append(model_name)
|
|
frame_all["category"].append(category)
|
|
frame_all["metric"].append(metric)
|
|
frame_all["avg_score"].append(metric_statistics["avg_score"])
|
|
frame_all["best_3"].append(metric_statistics["best_3"])
|
|
frame_all["worst_3"].append(metric_statistics["worst_3"])
|
|
|
|
frame_per_category[category]["model"].append(model_name)
|
|
frame_per_category[category]["metric"].append(metric)
|
|
frame_per_category[category]["avg_score"].append(metric_statistics["avg_score"])
|
|
frame_per_category[category]["best_3"].append(metric_statistics["best_3"])
|
|
frame_per_category[category]["worst_3"].append(metric_statistics["worst_3"])
|
|
|
|
if not os.path.exists(save_path):
|
|
os.makedirs(save_path)
|
|
|
|
frame_all = pd.DataFrame(frame_all)
|
|
frame_all.to_csv(os.path.join(save_path, "gpt_evaluation_statistics.csv"))
|
|
|
|
for category in tqdm.tqdm(
|
|
frame_per_category.keys(),
|
|
desc=f"GPT evaluation: ",
|
|
total=len(frame_per_category.keys()),
|
|
):
|
|
data = pd.DataFrame(frame_per_category[category])
|
|
|
|
sns.set()
|
|
fig = plt.figure(figsize=(16, 10))
|
|
plt.ylim((0, 5))
|
|
|
|
fig = sns.barplot(x="metric", y="avg_score", hue="model", data=data, dodge=True)
|
|
fig.set_title(f"Comparison between Different Models for Category {category.title()}")
|
|
plt.xlabel("Evaluation Metric")
|
|
plt.ylabel("Average Score")
|
|
|
|
figure = fig.get_figure()
|
|
figure.savefig(os.path.join(save_path, f"{category}.png"), dpi=400)
|
|
|
|
plt.close()
|