686 lines
23 KiB
Python
686 lines
23 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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大模型微调验证系统
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用于评估模型输出与参考答案的相似度和质量
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LLM评估配置：
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- 默认使用模拟评估（基于传统指标）
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- 如需使用真实LLM，请配置 llm_config.py 文件
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"""
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import json
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import os
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import re
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from collections import Counter
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from typing import Dict, List, Tuple, Any
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import numpy as np
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from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
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from nltk.tokenize import word_tokenize
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import jieba
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import jieba.posseg as pseg
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from difflib import SequenceMatcher
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import pandas as pd
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from tqdm import tqdm
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# 导入LLM配置
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from llm_config import (
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USE_REAL_LLM,
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OPENAI_CONFIG,
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EVALUATION_WEIGHTS,
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PROMPT_TEMPLATE,
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validate_openai_config,
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print_config_info
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)
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class ModelEvaluator:
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"""模型评估器"""
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def __init__(self):
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# 初始化jieba分词
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jieba.initialize()
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# 停用词列表（中文常见停用词）
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self.stopwords = {
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'的', '了', '是', '在', '我', '有', '和', '就', '不', '人', '都', '一', '一个',
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'上', '也', '很', '到', '说', '要', '去', '你', '会', '着', '没有', '看', '好',
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'自己', '这', '那', '能', '下', '过', '他', '来', '对', '时候', '后', '如果'
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}
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# 初始化平滑函数
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self.smoothing_function = SmoothingFunction()
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def get_llm_evaluation_prompt(self, reference: str, candidate: str, question: str = "") -> str:
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"""生成LLM评估提示词"""
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# 使用配置中的提示词模板
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return PROMPT_TEMPLATE.format(
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question=question,
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reference=reference,
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candidate=candidate
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)
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def call_llm_for_evaluation(self, prompt: str) -> Tuple[int, str]:
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"""调用大语言模型进行评估
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使用配置：
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- API配置来自 llm_config.py 中的 OPENAI_CONFIG
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- 支持环境变量和直接配置
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配置方法：
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1. 设置环境变量：export OPENAI_API_KEY='your-api-key'
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2. 在 llm_config.py 中直接修改 OPENAI_CONFIG
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"""
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# 验证配置
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config = validate_openai_config()
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try:
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from openai import OpenAI
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except ImportError:
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raise ImportError(
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"需要安装openai库：pip install openai\n"
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"详细配置请参考 llm_config.py"
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)
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# 初始化客户端
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client = OpenAI(
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api_key=config["api_key"],
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base_url=config["api_base"]
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)
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# 调用API
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try:
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response = client.chat.completions.create(
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model=config["model"],
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messages=[
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{"role": "system", "content": "你是一个专业的文本质量评估专家。"},
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{"role": "user", "content": prompt}
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],
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temperature=config["temperature"],
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max_tokens=config["max_tokens"]
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)
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# 解析JSON响应
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import json
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content = response.choices[0].message.content
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try:
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result = json.loads(content)
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score = int(result.get("score", 0))
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reason = str(result.get("reason", ""))
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return score, reason
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except (json.JSONDecodeError, KeyError, ValueError):
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# 如果无法解析JSON，尝试从文本中提取
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# 这里可以添加正则表达式来提取评分
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raise ValueError(f"无法解析LLM响应：{content}")
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except Exception as e:
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print("api调用失败")
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raise RuntimeError(f"API调用失败：{str(e)}")
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def get_mock_llm_evaluation(self, reference: str, candidate: str, question: str = "") -> Tuple[int, str]:
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"""获取模拟的LLM评估结果（用于演示）
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使用配置：
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- 权重来自 llm_config.py 中的 EVALUATION_WEIGHTS
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"""
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# 计算一些基本指标作为参考
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bleu = self.calculate_bleu_score(reference, candidate)
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rouge_l = self.calculate_rouge_l(reference, candidate)
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exact_match = self.calculate_exact_match_rate(reference, candidate)
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keyword_overlap = self.calculate_keyword_overlap(reference, candidate)
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# 使用配置中的权重进行加权计算
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composite_score = (
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bleu * EVALUATION_WEIGHTS['bleu_score'] +
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rouge_l * EVALUATION_WEIGHTS['rouge_l_score'] +
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exact_match * EVALUATION_WEIGHTS['exact_match_rate'] +
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keyword_overlap * EVALUATION_WEIGHTS['keyword_overlap_rate']
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)
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score = int(composite_score * 10)
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# 生成评价理由
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reason_parts = []
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if exact_match > 0.9:
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reason_parts.append("生成答案与参考答案内容完全一致")
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elif bleu > 0.1 or rouge_l > 0.3:
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reason_parts.append("生成答案在内容上与参考答案较为一致")
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else:
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reason_parts.append("生成答案与参考答案存在较大差异")
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if keyword_overlap > 0.5:
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reason_parts.append("关键词重叠度较高")
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elif keyword_overlap > 0.3:
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reason_parts.append("关键词重叠度中等")
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else:
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reason_parts.append("关键词重叠度较低")
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reason = "；".join(reason_parts) + f"。综合指标：BLEU={bleu:.3f}, ROUGE-L={rouge_l:.3f}, 完全匹配率={exact_match:.3f}, 关键词重叠率={keyword_overlap:.3f}"
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return min(10, max(1, score)), reason
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def tokenize_chinese(self, text: str) -> List[str]:
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"""中文分词"""
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if not text:
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return []
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# 使用jieba进行分词
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words = jieba.cut(text)
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return [word.strip() for word in words if len(word.strip()) > 0]
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def get_keywords(self, text: str) -> List[str]:
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"""提取关键词（名词、动词、形容词）"""
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if not text:
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return []
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words = pseg.cut(text)
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keywords = []
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for word, flag in words:
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# 选择名词(n)、动词(v)、形容词(a)、区别词(b)等作为关键词
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if flag.startswith(('n', 'v', 'a', 'b', 'i')) and word not in self.stopwords and len(word) > 1:
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keywords.append(word)
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return keywords
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def calculate_bleu_score(self, reference: str, candidate: str) -> float:
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"""计算BLEU分数"""
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if not reference or not candidate:
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return 0.0
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# 分词
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ref_tokens = self.tokenize_chinese(reference)
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cand_tokens = self.tokenize_chinese(candidate)
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if not cand_tokens:
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return 0.0
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# 计算BLEU分数（使用1-gram到4-gram）
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try:
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score = sentence_bleu(
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[ref_tokens],
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cand_tokens,
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smoothing_function=self.smoothing_function.method1
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)
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return score
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except:
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return 0.0
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def calculate_rouge_l(self, reference: str, candidate: str) -> float:
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"""计算ROUGE-L分数"""
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if not reference or not candidate:
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return 0.0
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ref_tokens = self.tokenize_chinese(reference)
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cand_tokens = self.tokenize_chinese(candidate)
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if not ref_tokens or not cand_tokens:
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return 0.0
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# 计算最长公共子序列
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lcs = self._lcs_length(ref_tokens, cand_tokens)
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# 计算ROUGE-L F1分数
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ref_len = len(ref_tokens)
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cand_len = len(cand_tokens)
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if ref_len == 0 or cand_len == 0:
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return 0.0
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precision = lcs / cand_len
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recall = lcs / ref_len
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if precision + recall == 0:
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return 0.0
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rouge_l = (2 * precision * recall) / (precision + recall)
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return rouge_l
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def _lcs_length(self, seq1: List[str], seq2: List[str]) -> int:
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"""计算最长公共子序列长度"""
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m, n = len(seq1), len(seq2)
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dp = [[0] * (n + 1) for _ in range(m + 1)]
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for i in range(1, m + 1):
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for j in range(1, n + 1):
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if seq1[i-1] == seq2[j-1]:
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dp[i][j] = dp[i-1][j-1] + 1
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else:
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dp[i][j] = max(dp[i-1][j], dp[i][j-1])
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return dp[m][n]
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def calculate_character_overlap(self, reference: str, candidate: str) -> float:
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"""计算字符重叠率"""
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if not reference or not candidate:
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return 0.0
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ref_chars = set(reference)
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cand_chars = set(candidate)
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if not cand_chars:
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return 0.0
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overlap = len(ref_chars & cand_chars)
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total_cand = len(cand_chars)
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return overlap / total_cand if total_cand > 0 else 0.0
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def calculate_length_similarity(self, reference: str, candidate: str) -> float:
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"""计算长度相似度"""
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if not reference or not candidate:
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return 0.0
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ref_len = len(reference)
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cand_len = len(candidate)
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if ref_len == 0 and cand_len == 0:
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return 1.0
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if ref_len == 0 or cand_len == 0:
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return 0.0
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# 使用相对差异计算相似度
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similarity = 1 - abs(ref_len - cand_len) / max(ref_len, cand_len)
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return max(0, similarity)
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def calculate_exact_match_rate(self, reference: str, candidate: str) -> float:
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"""计算完全匹配率"""
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if not reference and not candidate:
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return 1.0
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if not reference or not candidate:
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return 0.0
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# 去除空白字符后比较
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ref_clean = re.sub(r'\s+', '', reference.strip())
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cand_clean = re.sub(r'\s+', '', candidate.strip())
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return 1.0 if ref_clean == cand_clean else 0.0
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def calculate_keyword_overlap(self, reference: str, candidate: str) -> float:
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"""计算关键词重叠率"""
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ref_keywords = set(self.get_keywords(reference))
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cand_keywords = set(self.get_keywords(candidate))
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if not cand_keywords:
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return 0.0
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overlap = len(ref_keywords & cand_keywords)
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total_cand = len(cand_keywords)
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return overlap / total_cand if total_cand > 0 else 0.0
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def evaluate_all_metrics(self, reference: str, candidate: str) -> Dict[str, float]:
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"""计算所有评估指标"""
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return {
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'bleu_score': self.calculate_bleu_score(reference, candidate),
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'rouge_l_score': self.calculate_rouge_l(reference, candidate),
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'character_overlap_rate': self.calculate_character_overlap(reference, candidate),
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'length_similarity': self.calculate_length_similarity(reference, candidate),
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'exact_match_rate': self.calculate_exact_match_rate(reference, candidate),
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'keyword_overlap_rate': self.calculate_keyword_overlap(reference, candidate)
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}
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def load_data(file_path: str) -> List[Dict[str, Any]]:
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"""加载数据文件
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支持 .jsonl (JSON Lines) 和 .json (JSON Array) 格式
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"""
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data = []
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file_ext = os.path.splitext(file_path)[1].lower()
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if file_ext == '.jsonl':
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# JSON Lines格式：每行一个JSON对象
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with open(file_path, 'r', encoding='utf-8') as f:
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for line in f:
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line = line.strip()
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if line:
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try:
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data.append(json.loads(line))
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except json.JSONDecodeError:
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print(f"Warning: Failed to parse line: {line[:100]}")
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elif file_ext == '.json':
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# JSON Array格式：一个包含多个JSON对象的数组
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with open(file_path, 'r', encoding='utf-8') as f:
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try:
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json_data = json.load(f)
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if isinstance(json_data, list):
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data = json_data
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else:
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print(f"Warning: JSON file does not contain an array: {file_path}")
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except json.JSONDecodeError as e:
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print(f"Warning: Failed to parse JSON file: {file_path}, error: {e}")
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else:
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print(f"Warning: Unsupported file format: {file_ext}, only .json and .jsonl are supported")
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return data
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def evaluate_dataset(data: List[Dict[str, Any]], evaluator: ModelEvaluator, use_real_llm: bool = False) -> Tuple[List[Dict], Dict[str, float]]:
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"""评估整个数据集
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Args:
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data: 数据列表
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evaluator: 评估器实例
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use_real_llm: 是否使用真实LLM评估（默认False，使用模拟评估）
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"""
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results = []
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total_metrics = {
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'bleu_score': 0.0,
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'rouge_l_score': 0.0,
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'character_overlap_rate': 0.0,
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'length_similarity': 0.0,
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'exact_match_rate': 0.0,
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'keyword_overlap_rate': 0.0,
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'llm_score': 0.0
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}
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print(f"\n开始评估 {len(data)} 条数据...")
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if use_real_llm:
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print("注意：LLM评分功能使用真实的大语言模型API")
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print("配置来源：llm_config.py")
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else:
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print("注意：LLM评分功能使用模拟评估（基于传统指标的综合评分）")
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print("配置来源：llm_config.py 中的 EVALUATION_WEIGHTS")
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for idx, item in enumerate(tqdm(data, desc="评估进度")):
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# 支持多种字段名格式
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input_text = item.get('question', item.get('Input', item.get('问题', '')))
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output_text = item.get('output', item.get('Output', item.get('生成答案', '')))
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answer_text = item.get('answer', item.get('Answer', item.get('参考答案', '')))
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# 计算各项指标
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metrics = evaluator.evaluate_all_metrics(answer_text, output_text)
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# 获取LLM评估
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if use_real_llm:
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try:
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prompt = evaluator.get_llm_evaluation_prompt(answer_text, output_text, input_text)
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llm_score, llm_reason = evaluator.call_llm_for_evaluation(prompt)
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except Exception as e:
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print(f" [Warning] LLM API调用失败，使用模拟评估: {e}")
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llm_score, llm_reason = evaluator.get_mock_llm_evaluation(answer_text, output_text, input_text)
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else:
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llm_score, llm_reason = evaluator.get_mock_llm_evaluation(answer_text, output_text, input_text)
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# 添加原始数据
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result = {
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'index': idx + 1,
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'Input': input_text,
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'Output': output_text,
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'Answer': answer_text,
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**metrics,
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'llm_score': llm_score,
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'llm_reason': llm_reason
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}
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results.append(result)
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# 累加指标
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for key, value in metrics.items():
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total_metrics[key] += value
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total_metrics['llm_score'] += llm_score
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# 计算平均值
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num_samples = len(data)
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for key in total_metrics:
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total_metrics[key] /= num_samples if num_samples > 0 else 1
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return results, total_metrics
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def save_to_excel(results: List[Dict], total_metrics: Dict, output_dir: str, filename: str):
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"""保存结果到Excel文件"""
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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# 创建DataFrame
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df = pd.DataFrame(results)
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# 重命名列名
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column_mapping = {
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'index': '序号',
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'Input': '问题',
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'Answer': '参考答案',
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'Output': '生成答案',
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'bleu_score': 'BLEU',
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'rouge_l_score': 'ROUGE-L',
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'character_overlap_rate': '字符重叠率',
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'length_similarity': '长度相似度',
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'exact_match_rate': '完全匹配率',
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'keyword_overlap_rate': '关键词重叠率',
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'llm_score': 'LLM评分',
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'llm_reason': 'LLM评价理由'
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}
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df = df.rename(columns=column_mapping)
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# 计算整体统计信息
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stats_df = pd.DataFrame([total_metrics])
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stats_column_mapping = {
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'bleu_score': 'BLEU',
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'rouge_l_score': 'ROUGE-L',
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'character_overlap_rate': '字符重叠率',
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'length_similarity': '长度相似度',
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'exact_match_rate': '完全匹配率',
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'keyword_overlap_rate': '关键词重叠率',
|
||
'llm_score': 'LLM评分'
|
||
}
|
||
stats_df = stats_df.rename(columns=stats_column_mapping)
|
||
|
||
# 保存到Excel
|
||
excel_path = os.path.join(output_dir, filename)
|
||
with pd.ExcelWriter(excel_path, engine='openpyxl') as writer:
|
||
# 写入详细结果
|
||
df.to_excel(writer, sheet_name='详细结果', index=False)
|
||
|
||
# 写入统计信息
|
||
stats_df.to_excel(writer, sheet_name='整体统计', index=False)
|
||
|
||
print(f"\n结果已保存到: {excel_path}")
|
||
return excel_path
|
||
|
||
|
||
def print_summary(total_metrics: Dict):
|
||
"""打印评估摘要"""
|
||
print("\n" + "="*60)
|
||
print("模型评估结果摘要")
|
||
print("="*60)
|
||
print(f"BLEU分数: {total_metrics['bleu_score']:.4f}")
|
||
print(f"ROUGE-L分数: {total_metrics['rouge_l_score']:.4f}")
|
||
print(f"字符重叠率: {total_metrics['character_overlap_rate']:.4f}")
|
||
print(f"长度相似度: {total_metrics['length_similarity']:.4f}")
|
||
print(f"完全匹配率: {total_metrics['exact_match_rate']:.4f}")
|
||
print(f"关键词重叠率: {total_metrics['keyword_overlap_rate']:.4f}")
|
||
print(f"LLM评分: {total_metrics['llm_score']:.4f}")
|
||
print("="*60)
|
||
|
||
# 计算综合评分（加权平均）
|
||
weights = {
|
||
'bleu_score': 0.2,
|
||
'rouge_l_score': 0.25,
|
||
'character_overlap_rate': 0.15,
|
||
'length_similarity': 0.1,
|
||
'exact_match_rate': 0.15,
|
||
'keyword_overlap_rate': 0.15
|
||
}
|
||
|
||
composite_score = sum(total_metrics[key] * weight for key, weight in weights.items())
|
||
print(f"综合评分: {composite_score:.4f}")
|
||
print("="*60)
|
||
|
||
|
||
def main():
|
||
"""主函数"""
|
||
print("大模型微调验证系统")
|
||
print("="*60)
|
||
|
||
# 数据目录
|
||
data_dir = "data"
|
||
output_dir = "outputs"
|
||
|
||
# 打印当前配置信息
|
||
print_config_info()
|
||
|
||
# 获取所有数据文件（支持 .jsonl 和 .json 格式）
|
||
data_files = [f for f in os.listdir(data_dir) if f.endswith(('.jsonl', '.json'))]
|
||
print(f"\n发现 {len(data_files)} 个数据文件:")
|
||
for file in data_files:
|
||
file_ext = os.path.splitext(file)[1]
|
||
print(f" - {file} ({file_ext})")
|
||
|
||
# 初始化评估器
|
||
evaluator = ModelEvaluator()
|
||
|
||
# 存储所有结果
|
||
all_results = []
|
||
all_stats = {}
|
||
|
||
# 逐个评估每个文件
|
||
for filename in data_files:
|
||
print(f"\n{'='*60}")
|
||
print(f"评估文件: {filename}")
|
||
print(f"{'='*60}")
|
||
|
||
file_path = os.path.join(data_dir, filename)
|
||
data = load_data(file_path)
|
||
print(f"加载数据: {len(data)} 条记录")
|
||
|
||
# 评估数据（使用配置文件中的USE_REAL_LLM设置）
|
||
results, total_metrics = evaluate_dataset(data, evaluator, use_real_llm=USE_REAL_LLM)
|
||
|
||
# 保存结果
|
||
base_name = os.path.splitext(filename)[0]
|
||
excel_filename = f"{base_name}_evaluation.xlsx"
|
||
excel_path = save_to_excel(results, total_metrics, output_dir, excel_filename)
|
||
|
||
# 存储结果
|
||
all_results.extend([{**r, 'file': filename} for r in results])
|
||
all_stats[filename] = total_metrics
|
||
|
||
# 打印摘要
|
||
print_summary(total_metrics)
|
||
|
||
# 生成汇总报告
|
||
print(f"\n{'='*60}")
|
||
print("生成汇总报告")
|
||
print(f"{'='*60}")
|
||
|
||
# 创建汇总DataFrame
|
||
summary_data = []
|
||
for filename, stats in all_stats.items():
|
||
summary_data.append({
|
||
'文件名': filename,
|
||
'BLEU分数': stats['bleu_score'],
|
||
'ROUGE-L分数': stats['rouge_l_score'],
|
||
'字符重叠率': stats['character_overlap_rate'],
|
||
'长度相似度': stats['length_similarity'],
|
||
'完全匹配率': stats['exact_match_rate'],
|
||
'关键词重叠率': stats['keyword_overlap_rate'],
|
||
'LLM评分': stats['llm_score']
|
||
})
|
||
|
||
summary_df = pd.DataFrame(summary_data)
|
||
|
||
# 计算所有文件的平均分
|
||
avg_scores = summary_df.select_dtypes(include=[np.number]).mean()
|
||
avg_df = pd.DataFrame([avg_scores])
|
||
avg_df.index = ['平均分']
|
||
|
||
# 保存汇总报告
|
||
summary_path = os.path.join(output_dir, "evaluation_summary.xlsx")
|
||
with pd.ExcelWriter(summary_path, engine='openpyxl') as writer:
|
||
summary_df.to_excel(writer, sheet_name='各文件评分', index=False)
|
||
avg_df.to_excel(writer, sheet_name='平均分')
|
||
|
||
print(f"汇总报告已保存到: {summary_path}")
|
||
|
||
# 打印最终汇总
|
||
print(f"\n{'='*60}")
|
||
print("所有文件评估结果汇总")
|
||
print(f"{'='*60}")
|
||
print(summary_df.to_string(index=False, float_format='%.4f'))
|
||
print(f"{'='*60}")
|
||
print("平均分:")
|
||
print(avg_df.to_string(float_format='%.4f'))
|
||
print(f"{'='*60}")
|
||
|
||
# 保存所有详细结果
|
||
all_results_path = os.path.join(output_dir, "all_detailed_results.xlsx")
|
||
all_results_df = pd.DataFrame(all_results)
|
||
# 重命名列名
|
||
all_column_mapping = {
|
||
'index': '序号',
|
||
'Input': '问题',
|
||
'Answer': '参考答案',
|
||
'Output': '生成答案',
|
||
'bleu_score': 'BLEU',
|
||
'rouge_l_score': 'ROUGE-L',
|
||
'character_overlap_rate': '字符重叠率',
|
||
'length_similarity': '长度相似度',
|
||
'exact_match_rate': '完全匹配率',
|
||
'keyword_overlap_rate': '关键词重叠率',
|
||
'llm_score': 'LLM评分',
|
||
'llm_reason': 'LLM评价理由'
|
||
}
|
||
all_results_df = all_results_df.rename(columns=all_column_mapping)
|
||
all_results_df.to_excel(all_results_path, index=False, engine='openpyxl')
|
||
print(f"\n所有详细结果已保存到: {all_results_path}")
|
||
|
||
|
||
# =============================================================================
|
||
# 测试函数
|
||
# =============================================================================
|
||
|
||
def test_single_evaluation():
|
||
"""测试单个评估"""
|
||
from llm_config import USE_REAL_LLM
|
||
|
||
evaluator = ModelEvaluator()
|
||
|
||
# 测试数据
|
||
question = "什么是合同？"
|
||
reference_answer = "合同是当事人之间设立、变更、终止民事法律关系的协议。"
|
||
candidate_answer = "合同是双方或多方之间达成的协议，用于约定权利和义务。"
|
||
|
||
print("="*60)
|
||
print("LLM评估测试")
|
||
print("="*60)
|
||
print(f"评估模式: {'真实LLM API' if USE_REAL_LLM else '模拟评估'}")
|
||
print(f"问题: {question}")
|
||
print(f"参考答案: {reference_answer}")
|
||
print(f"生成答案: {candidate_answer}")
|
||
print("-"*60)
|
||
|
||
try:
|
||
# 获取LLM评估
|
||
prompt = evaluator.get_llm_evaluation_prompt(reference_answer, candidate_answer, question)
|
||
|
||
if USE_REAL_LLM:
|
||
print("尝试调用真实LLM API...")
|
||
score, reason = evaluator.call_llm_for_evaluation(prompt)
|
||
|
||
# 同时计算传统指标
|
||
print("\n传统指标计算:")
|
||
all_metrics = evaluator.evaluate_all_metrics(reference_answer, candidate_answer)
|
||
for key, value in all_metrics.items():
|
||
print(f" {key}: {value:.4f}")
|
||
else:
|
||
print("使用模拟评估...")
|
||
score, reason = evaluator.get_mock_llm_evaluation(reference_answer, candidate_answer, question)
|
||
|
||
print(f"\n[SUCCESS] 评估成功!")
|
||
print(f"评分: {score}/10")
|
||
print(f"评价理由: {reason}")
|
||
|
||
return True
|
||
|
||
except Exception as e:
|
||
print(f"\n[ERROR] 评估失败: {e}")
|
||
print("\n详细错误信息:")
|
||
import traceback
|
||
traceback.print_exc()
|
||
return False
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main() |