{
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  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 首先生成几个针对question.txt的结果，每行一个，存储在txt中；\n",
    "2. 选择哪几个模型？\n",
    "\n",
    "- GPT系列\n",
    "- GLM3\n",
    "- 百度\n",
    "- Qwen1.5-72b-chat \n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "https://github.com/yuyouyu32/LLMQAEvaluate?tab=readme-ov-file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2025-01-08 16:01:01,433] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from transformers import AutoTokenizer,AutoModel\n",
    "import torch\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sentence_transformers import SentenceTransformer\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "device = \"cuda\"\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\" \n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1,0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = \"/home/zhangxj/models/acge_text_embedding\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "# model = AutoModel.from_pretrained(model_name)\n",
    "model = SentenceTransformer(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 1792)\n"
     ]
    }
   ],
   "source": [
    "def embedding(text):\n",
    "    # inputs = tokenizer(text,return_tenors=\"pt\",padding=True,truncation=True,max_length=512)\n",
    "    # with torch.no_grad():\n",
    "    #     outputs = model(**inputs)\n",
    "    \n",
    "    # embeddings = outputs.last_hidden_state.mean(dim=1)\n",
    "    embeddings = model.encode(text,normalize_embeddings=True)\n",
    "    \n",
    "    return embeddings\n",
    "\n",
    "emb1 = embedding([\"你好，这里是中国\",\"欢迎你来到中国！\"])\n",
    "\n",
    "# from numpy.linalg import norm\n",
    "\n",
    "# cos_sim = lambda a,b:(a@b.T)/(norm(a)*norm(b))\n",
    "print(emb1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "def remove_punctuation(text):\n",
    "    # 正则表达式匹配中文标点和英文标点\n",
    "    pstr = r\"＂＃＄＆＇（）＊＋，－／：；＠［＼］＾＿｀｛｜｝～｟｠｢｣､　、〃〈〉《》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏﹑﹔·！？｡。\"\n",
    "    return re.sub(pstr, ' ', text)\n",
    "\n",
    "def get_ans_list(file_path):\n",
    "    answers = []\n",
    "    with open(file_path,\"r\",encoding=\"utf-8\") as file:\n",
    "        for line in file.readlines():\n",
    "            answers.append(line.strip())\n",
    "    results = [remove_punctuation(ans) for ans in answers]\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "answers = get_ans_list(\"/home/zhangxj/WorkFile/LCA-GPT/QA/filters/answers.txt\")\n",
    "answer_rag = get_ans_list(\"/home/zhangxj/WorkFile/LCA-GPT/QA/eval/RAGpred.txt\")\n",
    "answer_qwen72 = get_ans_list(\"/home/zhangxj/WorkFile/LCA-GPT/QA/eval/Qwen72bpred.txt\")\n",
    "answer_glm = get_ans_list(\"/home/zhangxj/WorkFile/LCA-GPT/QA/eval/GLMpred.txt\")\n",
    "answer_baidu = get_ans_list(\"/home/zhangxj/WorkFile/LCA-GPT/QA/eval/ERNIEpred.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>question</th>\n",
       "      <th>answer</th>\n",
       "      <th>子类别</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>什么是生命周期分析（LCA）的主要目标？</td>\n",
       "      <td>生命周期分析旨在评估产品或服务从原材料获取到最终处置的环境影响。</td>\n",
       "      <td>LCA理论与相关知识</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>在LCA中，如何确定研究的范围？</td>\n",
       "      <td>研究范围包括定义系统边界，如输入、输出、功能单位和分析阶段。</td>\n",
       "      <td>研究和试验发展</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>文档中提到的医疗废物如何处理？</td>\n",
       "      <td>文档未直接说明医疗废物的具体处理方法，只提及了与之相关的能源消耗。</td>\n",
       "      <td>卫生和社会工作</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LCA数据清单收集阶段需要哪些信息？</td>\n",
       "      <td>数据清单需收集所有过程的输入输出数据，包括资源消耗、排放和能源使用。</td>\n",
       "      <td>LCA理论与相关知识</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>生命周期影响评价阶段的目标是什么？</td>\n",
       "      <td>该阶段旨在量化每个阶段对环境的各种影响，如气候变化、水耗和土地使用。</td>\n",
       "      <td>生态保护和环境治理业</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               question                              answer         子类别\n",
       "0  什么是生命周期分析（LCA）的主要目标？    生命周期分析旨在评估产品或服务从原材料获取到最终处置的环境影响。  LCA理论与相关知识\n",
       "1      在LCA中，如何确定研究的范围？      研究范围包括定义系统边界，如输入、输出、功能单位和分析阶段。     研究和试验发展\n",
       "2       文档中提到的医疗废物如何处理？   文档未直接说明医疗废物的具体处理方法，只提及了与之相关的能源消耗。     卫生和社会工作\n",
       "3    LCA数据清单收集阶段需要哪些信息？  数据清单需收集所有过程的输入输出数据，包括资源消耗、排放和能源使用。  LCA理论与相关知识\n",
       "4     生命周期影响评价阶段的目标是什么？  该阶段旨在量化每个阶段对环境的各种影响，如气候变化、水耗和土地使用。  生态保护和环境治理业"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取每个类别的答案和文本\n",
    "data_class = pd.read_excel(\"/home/zhangxj/WorkFile/LCA-GPT/QA/classify_new.xlsx\")\n",
    "data_class.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def classify_ans(answer,class_name):\n",
    "    '''从全部的答案列表中筛选出目标类别的答案'''\n",
    "    ans_class = []\n",
    "    for idx,item in data_class.iterrows():\n",
    "        if item['子类别'] == class_name:\n",
    "            ans_class.append(answer[idx])\n",
    "    return ans_class\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "790\n",
      "790\n",
      "754\n",
      "754\n",
      "321\n",
      "321\n",
      "295\n",
      "295\n",
      "183\n",
      "183\n",
      "174\n",
      "174\n",
      "157\n",
      "157\n",
      "131\n",
      "131\n",
      "126\n",
      "126\n",
      "87\n",
      "87\n"
     ]
    }
   ],
   "source": [
    "# 字典存储分类别的内容\n",
    "ans_gold = dict()\n",
    "ans_rag = dict()\n",
    "ans_qwen72 = dict()\n",
    "ans_glm = dict()\n",
    "ans_baidu = dict()\n",
    "\n",
    "class_top10 = [\"LCA理论与相关知识\",\"生态保护和环境治理业\",\"研究和试验发展\",\"建筑业\",\"非金属矿物制品业\",\"化学原料和化学制品制造业\",\"废弃资源综合利用业\",\"农、林、牧、渔业\",\"电力、热力生产和供应业\",\"汽车制造业\"]\n",
    "\n",
    "for clas in class_top10:\n",
    "    ans_gold[clas] = classify_ans(answers,clas)\n",
    "    ans_rag[clas] = classify_ans(answer_rag,clas)\n",
    "    ans_qwen72[clas] = classify_ans(answer_qwen72,clas)\n",
    "    ans_glm[clas] = classify_ans(answer_glm,clas)\n",
    "    ans_baidu[clas] = classify_ans(answer_baidu,clas)\n",
    "\n",
    "    print(len(ans_gold[clas]))\n",
    "    print(len(ans_glm[clas]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 以字典形式获取每个类别的文本向量\n",
    "emb_ans = dict()\n",
    "emb_rag = dict()\n",
    "emb_qwen72 = dict()\n",
    "emb_glm = dict()\n",
    "emb_baidu = dict()\n",
    "\n",
    "for clas in class_top10:\n",
    "    emb_ans[clas] = embedding(ans_gold[clas])\n",
    "    emb_rag[clas] = embedding(ans_rag[clas])\n",
    "    emb_qwen72[clas] = embedding(ans_qwen72[clas])\n",
    "    emb_glm[clas] = embedding(ans_glm[clas])\n",
    "    emb_baidu[clas] = embedding(ans_baidu[clas])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 指标1：余弦相似度,计算所有回答的cos_sim，取平均\n",
    "def cos_sim(target,pred):\n",
    "    ''' ans,pred的数据格式是numpy.narray'''\n",
    "\n",
    "    cos_sim_list = []\n",
    "    for i in range(target.shape[0]):\n",
    "        dot_product = np.dot(target[i],pred[i])\n",
    "        norm_target = np.linalg.norm(target[i])\n",
    "        norm_pred = np.linalg.norm(target[i])\n",
    "\n",
    "        cos = dot_product/(norm_target*norm_pred)\n",
    "        cos_sim_list.append(cos)\n",
    "    avg_cos_sim = np.mean(cos_sim_list)\n",
    "\n",
    "    return avg_cos_sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "类别： LCA理论与相关知识\n",
      "{'LCA-GPT': 0.7563889, 'GLM-4': 0.72979796, 'ERNIE-3.5-8K': 0.73387533, 'Qwen1.5-72b': 0.7237778}\n",
      "类别： 生态保护和环境治理业\n",
      "{'LCA-GPT': 0.76573056, 'GLM-4': 0.7285658, 'ERNIE-3.5-8K': 0.73159605, 'Qwen1.5-72b': 0.7110586}\n",
      "类别： 研究和试验发展\n",
      "{'LCA-GPT': 0.74416304, 'GLM-4': 0.706629, 'ERNIE-3.5-8K': 0.6975246, 'Qwen1.5-72b': 0.68896115}\n",
      "类别： 建筑业\n",
      "{'LCA-GPT': 0.76548576, 'GLM-4': 0.7384504, 'ERNIE-3.5-8K': 0.7213398, 'Qwen1.5-72b': 0.71236473}\n",
      "类别： 非金属矿物制品业\n",
      "{'LCA-GPT': 0.7918446, 'GLM-4': 0.74560964, 'ERNIE-3.5-8K': 0.7342301, 'Qwen1.5-72b': 0.7142454}\n",
      "类别： 化学原料和化学制品制造业\n",
      "{'LCA-GPT': 0.80663353, 'GLM-4': 0.75003314, 'ERNIE-3.5-8K': 0.73770964, 'Qwen1.5-72b': 0.72282004}\n",
      "类别： 废弃资源综合利用业\n",
      "{'LCA-GPT': 0.78028744, 'GLM-4': 0.73390573, 'ERNIE-3.5-8K': 0.7344841, 'Qwen1.5-72b': 0.716429}\n",
      "类别： 农、林、牧、渔业\n",
      "{'LCA-GPT': 0.7865737, 'GLM-4': 0.7173627, 'ERNIE-3.5-8K': 0.7319357, 'Qwen1.5-72b': 0.69628036}\n",
      "类别： 电力、热力生产和供应业\n",
      "{'LCA-GPT': 0.7891359, 'GLM-4': 0.7410214, 'ERNIE-3.5-8K': 0.7426608, 'Qwen1.5-72b': 0.7154095}\n",
      "类别： 汽车制造业\n",
      "{'LCA-GPT': 0.7961327, 'GLM-4': 0.7537849, 'ERNIE-3.5-8K': 0.7584841, 'Qwen1.5-72b': 0.72462904}\n"
     ]
    }
   ],
   "source": [
    "class_cos = dict()\n",
    "for clas in class_top10:\n",
    "    print(\"类别：\",clas)\n",
    "    cos_dict = dict()\n",
    "    cos_dict['LCA-GPT'] = cos_sim(emb_ans[clas],emb_rag[clas])\n",
    "    cos_dict['GLM-4'] = cos_sim(emb_ans[clas],emb_glm[clas])\n",
    "    cos_dict['ERNIE-3.5-8K'] = cos_sim(emb_ans[clas],emb_baidu[clas])\n",
    "    cos_dict['Qwen1.5-72b'] = cos_sim(emb_ans[clas],emb_qwen72[clas])\n",
    "    class_cos[clas] = cos_dict\n",
    "    print(cos_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cos = pd.DataFrame.from_dict(class_cos,orient='index').T\n",
    "df_cos.to_csv(\"/home/zhangxj/WorkFile/LCA-GPT/LCA_RAG/data/eval/cos.csv\",index=False,encoding=\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "### f1值\n",
    "import jieba\n",
    "import collections\n",
    "\n",
    "def cal_f1(target,pred):\n",
    "    target_token = list(jieba.cut(target,cut_all=False))\n",
    "    pred_token = list(jieba.cut(pred,cut_all=False))\n",
    "\n",
    "    common = collections.Counter(target_token) & collections.Counter(pred_token)\n",
    "    num_same = sum(common.values())\n",
    "    if len(target_token) == 0 or len(pred_token) == 0:\n",
    "        return int(target_token == pred_token)\n",
    "    if num_same == 0:\n",
    "        return 0\n",
    "    precision = 1.0*num_same/len(pred_token)\n",
    "    recall = 1.0*num_same/len(target_token)\n",
    "    f1 = (2.0*recall*precision) /(precision+recall)\n",
    "\n",
    "    return f1\n",
    "\n",
    "def calf1_all(target,pred):\n",
    "    f1s = []\n",
    "    for tar,pre in zip(target,pred):\n",
    "        f1 = cal_f1(tar,pre)\n",
    "        f1s.append(f1)\n",
    "    return np.mean(f1s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "类别： LCA理论与相关知识\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Dumping model to file cache /tmp/jieba.cache\n",
      "Loading model cost 0.679 seconds.\n",
      "Prefix dict has been built successfully.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'LCA-GPT': 0.3437605081212203, 'GLM-4': 0.30454084953051963, 'ERNIE-3.5-8K': 0.30268024101560764, 'Qwen1.5-72b': 0.2682497767547755}\n",
      "类别： 生态保护和环境治理业\n",
      "{'LCA-GPT': 0.3483626691289569, 'GLM-4': 0.2788926668662055, 'ERNIE-3.5-8K': 0.27761325010853644, 'Qwen1.5-72b': 0.2302952772337834}\n",
      "类别： 研究和试验发展\n",
      "{'LCA-GPT': 0.3430343943468575, 'GLM-4': 0.2821541495127059, 'ERNIE-3.5-8K': 0.2729394816189532, 'Qwen1.5-72b': 0.23015648181836218}\n",
      "类别： 建筑业\n",
      "{'LCA-GPT': 0.39371906828313225, 'GLM-4': 0.31389786788066487, 'ERNIE-3.5-8K': 0.28676175803618814, 'Qwen1.5-72b': 0.2204392856411433}\n",
      "类别： 非金属矿物制品业\n",
      "{'LCA-GPT': 0.4130349692057703, 'GLM-4': 0.297081206460411, 'ERNIE-3.5-8K': 0.25563488130229556, 'Qwen1.5-72b': 0.21248638601796016}\n",
      "类别： 化学原料和化学制品制造业\n",
      "{'LCA-GPT': 0.4185145863497029, 'GLM-4': 0.2931829455107586, 'ERNIE-3.5-8K': 0.2694429593634037, 'Qwen1.5-72b': 0.22850507493329048}\n",
      "类别： 废弃资源综合利用业\n",
      "{'LCA-GPT': 0.38042435656104256, 'GLM-4': 0.28198554328062225, 'ERNIE-3.5-8K': 0.2718793531798568, 'Qwen1.5-72b': 0.2066845950860928}\n",
      "类别： 农、林、牧、渔业\n",
      "{'LCA-GPT': 0.39036740247556595, 'GLM-4': 0.27194804107827547, 'ERNIE-3.5-8K': 0.27189044583195743, 'Qwen1.5-72b': 0.20235451922007622}\n",
      "类别： 电力、热力生产和供应业\n",
      "{'LCA-GPT': 0.41114581401949746, 'GLM-4': 0.26988029172611805, 'ERNIE-3.5-8K': 0.2713352793043654, 'Qwen1.5-72b': 0.18898670597783235}\n",
      "类别： 汽车制造业\n",
      "{'LCA-GPT': 0.4130216859020192, 'GLM-4': 0.3179324748633672, 'ERNIE-3.5-8K': 0.3088808476140809, 'Qwen1.5-72b': 0.2327820200502282}\n"
     ]
    }
   ],
   "source": [
    "class_f1 = dict()\n",
    "for clas in class_top10:\n",
    "    print(\"类别：\",clas)\n",
    "    f1_dict = dict()\n",
    "    f1_dict['LCA-GPT'] = calf1_all(ans_gold[clas],ans_rag[clas])\n",
    "    f1_dict['GLM-4'] = calf1_all(ans_gold[clas],ans_glm[clas])\n",
    "    f1_dict['ERNIE-3.5-8K'] = calf1_all(ans_gold[clas],ans_baidu[clas])\n",
    "    f1_dict['Qwen1.5-72b'] = calf1_all(ans_gold[clas],ans_qwen72[clas])\n",
    "    class_f1[clas] = f1_dict\n",
    "    print(f1_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_f1 = pd.DataFrame.from_dict(class_f1,orient='index').T\n",
    "df_f1.to_csv(\"/home/zhangxj/WorkFile/LCA-GPT/LCA_RAG/data/eval/f1.csv\",index=False,encoding=\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 指标2：BLEU 支持中文？\n",
    "import nltk\n",
    "from nltk.translate.bleu_score import sentence_bleu,SmoothingFunction\n",
    "import jieba\n",
    "\n",
    "''' 到时候需要遍历整个文档的每一行进行计算，之后统计平均值'''\n",
    "def Recall(target,pred):\n",
    "    ''' 直接传入文本格式的答案和预测结果'''\n",
    "    # 文本分解为句子\n",
    "    target_list = list(target)\n",
    "    pred_list = list(pred)\n",
    "    \n",
    "    # print(target_list)\n",
    "\n",
    "    smooth = SmoothingFunction()\n",
    "    # 计算bleu\n",
    "    score = sentence_bleu([target_list],pred_list,smoothing_function=smooth.method2)\n",
    "    return score\n",
    "\n",
    "def bleu_mean(target,pred):\n",
    "    ''' 列表'''\n",
    "    bleu = []\n",
    "\n",
    "    for tar,pre in zip(target,pred):\n",
    "        recall = Recall(tar,pre)\n",
    "        bleu.append(recall)\n",
    "    return np.mean(bleu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "类别： LCA理论与相关知识\n",
      "{'LCA-GPT': 0.1805466558991861, 'GLM-4': 0.11554456962082649, 'ERNIE-3.5-8K': 0.12240250499083317, 'Qwen1.5-72b': 0.10443194919613479}\n",
      "类别： 生态保护和环境治理业\n",
      "{'LCA-GPT': 0.1936086630791969, 'GLM-4': 0.10723241319036586, 'ERNIE-3.5-8K': 0.11146362502253516, 'Qwen1.5-72b': 0.08702864761847347}\n",
      "类别： 研究和试验发展\n",
      "{'LCA-GPT': 0.18237757004342872, 'GLM-4': 0.10627954683010601, 'ERNIE-3.5-8K': 0.1053829450454662, 'Qwen1.5-72b': 0.08501998659280752}\n",
      "类别： 建筑业\n",
      "{'LCA-GPT': 0.22567381216712698, 'GLM-4': 0.1310207093784512, 'ERNIE-3.5-8K': 0.11733150982192744, 'Qwen1.5-72b': 0.08621213867998642}\n",
      "类别： 非金属矿物制品业\n",
      "{'LCA-GPT': 0.26680615981340805, 'GLM-4': 0.13232836478838658, 'ERNIE-3.5-8K': 0.10948499320594085, 'Qwen1.5-72b': 0.08474905129676888}\n",
      "类别： 化学原料和化学制品制造业\n",
      "{'LCA-GPT': 0.26724369675199966, 'GLM-4': 0.12509640511891365, 'ERNIE-3.5-8K': 0.11269336039381148, 'Qwen1.5-72b': 0.09118967633955695}\n",
      "类别： 废弃资源综合利用业\n",
      "{'LCA-GPT': 0.22468697711112326, 'GLM-4': 0.11250649617963052, 'ERNIE-3.5-8K': 0.11120199316111042, 'Qwen1.5-72b': 0.07802856134904246}\n",
      "类别： 农、林、牧、渔业\n",
      "{'LCA-GPT': 0.2464707549734943, 'GLM-4': 0.10604667409601695, 'ERNIE-3.5-8K': 0.1091459446440835, 'Qwen1.5-72b': 0.07117894187328233}\n",
      "类别： 电力、热力生产和供应业\n",
      "{'LCA-GPT': 0.25818318417049985, 'GLM-4': 0.11724440564237175, 'ERNIE-3.5-8K': 0.12456420117921357, 'Qwen1.5-72b': 0.07637114311250687}\n",
      "类别： 汽车制造业\n",
      "{'LCA-GPT': 0.254561556379476, 'GLM-4': 0.1415682454400737, 'ERNIE-3.5-8K': 0.14866059993668507, 'Qwen1.5-72b': 0.09549466792936462}\n"
     ]
    }
   ],
   "source": [
    "for clas in class_top10:\n",
    "    print(\"类别：\",clas)\n",
    "    bleu_dict = dict()\n",
    "    bleu_dict['LCA-GPT'] = bleu_mean(ans_gold[clas],ans_rag[clas])\n",
    "    bleu_dict['GLM-4'] = bleu_mean(ans_gold[clas],ans_glm[clas])\n",
    "    bleu_dict['ERNIE-3.5-8K'] = bleu_mean(ans_gold[clas],ans_baidu[clas])\n",
    "    bleu_dict['Qwen1.5-72b'] = bleu_mean(ans_gold[clas],ans_qwen72[clas])\n",
    "    print(bleu_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 指标3：Rouge-l,Rouge-w\n",
    "from rouge_chinese import Rouge\n",
    "import jieba\n",
    "\n",
    "def calRouge(target,pred):\n",
    "    ''' 传入的是文档列表,越大越好''' \n",
    "    f = 0.0\n",
    "    p = 0.0\n",
    "    r = 0.0\n",
    "    for targ,pre in zip(target,pred):\n",
    "        target_cut = ' '.join(jieba.cut(targ,cut_all=False))\n",
    "        pred_cut = ' '.join(jieba.cut(pre,cut_all=False))\n",
    "\n",
    "        rouger = Rouge()\n",
    "        scores = rouger.get_scores(pred_cut,target_cut)\n",
    "\n",
    "        rougeL = scores[0]['rouge-l']\n",
    "\n",
    "        f += rougeL['f']\n",
    "        p += rougeL['p']\n",
    "        r += rougeL['r']   \n",
    "    length = len(answer_rag)\n",
    "    return f/length,p/length,r/length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 指标3：rouge\n",
    "from rouge_score import rouge_scorer\n",
    "\n",
    "def rouge(predict, target):\n",
    "\n",
    "    scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True)\n",
    "    # Calculate the ROUGE score\n",
    "    score = scorer.score(predict, target)\n",
    "    # Extract the F1 score for ROUGE-1\n",
    "    rouge_score = score['rougeL'].fmeasure\n",
    "    return rouge_score\n",
    "\n",
    "def rouge_all(target,pred):\n",
    "    rouges = []\n",
    "    for tar,pre in zip(target,pred):\n",
    "        score = rouge(pre,tar)\n",
    "        rouges.append(score)\n",
    "    return np.mean(rouges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "类别： LCA理论与相关知识\n",
      "{'LCA-GPT': 0.39498794454490654, 'GLM-4': 0.2213504786974626, 'ERNIE-3.5-8K': 0.30471539483857785, 'Qwen1.5-72b': 0.2724725275577533}\n",
      "类别： 生态保护和环境治理业\n",
      "{'LCA-GPT': 0.33244458401218807, 'GLM-4': 0.1579393502684968, 'ERNIE-3.5-8K': 0.19627007847801298, 'Qwen1.5-72b': 0.1618331729529683}\n",
      "类别： 研究和试验发展\n",
      "{'LCA-GPT': 0.32609547773099173, 'GLM-4': 0.17042631995903024, 'ERNIE-3.5-8K': 0.20333817273069607, 'Qwen1.5-72b': 0.1632025416542098}\n",
      "类别： 建筑业\n",
      "{'LCA-GPT': 0.29661873962970675, 'GLM-4': 0.11762091016328305, 'ERNIE-3.5-8K': 0.1457537942283705, 'Qwen1.5-72b': 0.09701429611918969}\n",
      "类别： 非金属矿物制品业\n",
      "{'LCA-GPT': 0.40538367218695087, 'GLM-4': 0.13737843819811033, 'ERNIE-3.5-8K': 0.16863152027086453, 'Qwen1.5-72b': 0.11709287878162374}\n",
      "类别： 化学原料和化学制品制造业\n",
      "{'LCA-GPT': 0.3627170721998308, 'GLM-4': 0.08316912972085386, 'ERNIE-3.5-8K': 0.13428285147352084, 'Qwen1.5-72b': 0.08392393366531298}\n",
      "类别： 废弃资源综合利用业\n",
      "{'LCA-GPT': 0.2940372790691262, 'GLM-4': 0.10792134263471842, 'ERNIE-3.5-8K': 0.13910282923021774, 'Qwen1.5-72b': 0.10455685503456204}\n",
      "类别： 农、林、牧、渔业\n",
      "{'LCA-GPT': 0.348491457651763, 'GLM-4': 0.1357506361323155, 'ERNIE-3.5-8K': 0.17828388744419277, 'Qwen1.5-72b': 0.131476254623987}\n",
      "类别： 电力、热力生产和供应业\n",
      "{'LCA-GPT': 0.32386475957904526, 'GLM-4': 0.06020408163265306, 'ERNIE-3.5-8K': 0.10913031879418433, 'Qwen1.5-72b': 0.0647123664280527}\n",
      "类别： 汽车制造业\n",
      "{'LCA-GPT': 0.3455275567344533, 'GLM-4': 0.09315818281335522, 'ERNIE-3.5-8K': 0.1705906011713634, 'Qwen1.5-72b': 0.12014164935057432}\n"
     ]
    }
   ],
   "source": [
    "class_roug = dict()\n",
    "for clas in class_top10:\n",
    "    print(\"类别：\",clas)\n",
    "    rou_dict = dict()\n",
    "    rou_dict['LCA-GPT'] = rouge_all(ans_gold[clas],ans_rag[clas])\n",
    "    rou_dict['GLM-4'] = rouge_all(ans_gold[clas],ans_glm[clas])\n",
    "    rou_dict['ERNIE-3.5-8K'] = rouge_all(ans_gold[clas],ans_baidu[clas])\n",
    "    rou_dict['Qwen1.5-72b'] = rouge_all(ans_gold[clas],ans_qwen72[clas])\n",
    "    class_roug[clas] = rou_dict\n",
    "    print(rou_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rouge = pd.DataFrame.from_dict(class_roug,orient='index').T\n",
    "df_rouge.to_csv(\"/home/zhangxj/WorkFile/LCA-GPT/LCA_RAG/data/eval/rouge.csv\",index=False,encoding=\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zhangxj/miniconda3/envs/Qwen/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  return self.fget.__get__(instance, owner)()\n"
     ]
    }
   ],
   "source": [
    "## 计算BERTscore\n",
    "from transformers import BertTokenizer, BertModel\n",
    "tokenizer = BertTokenizer.from_pretrained(\"/home/zhangxj/models/bert/bert-base-chinese\")\n",
    "model2 = BertModel.from_pretrained(\"/home/zhangxj/models/bert/bert-base-chinese\")\n",
    "\n",
    "def cosine_similarity(a, b):\n",
    "    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))\n",
    "\n",
    "def bert_score(reference, candidate, return_similarity_matrix=False):\n",
    "    # 计算余弦相似度\n",
    "    cosine_similarities = np.zeros((reference.shape[0], candidate.shape[0]))\n",
    "    for i, c in enumerate(candidate):\n",
    "        for j, r in enumerate(reference):\n",
    "            cosine_similarities[i, j] = cosine_similarity(c, r)\n",
    "    # 取每一行数据的最大余弦相似度\n",
    "    max_similarities = cosine_similarities.max(axis=1)\n",
    "    # 取所有余弦相似度的均值\n",
    "    bertscore = max_similarities.mean()\n",
    "    if return_similarity_matrix:\n",
    "        return bertscore, cosine_similarities\n",
    "    else:\n",
    "        return bertscore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "类别： LCA理论与相关知识\n",
      "{'LCA-GPT': 0.8285459900958628, 'GLM-4': 0.8031236508979073, 'ERNIE-3.5-8K': 0.8069283152682871, 'Qwen1.5-72b': 0.80836640302139}\n",
      "类别： 生态保护和环境治理业\n",
      "{'LCA-GPT': 0.8278908556430978, 'GLM-4': 0.7980815338993579, 'ERNIE-3.5-8K': 0.7964145340400919, 'Qwen1.5-72b': 0.7882782395543724}\n",
      "类别： 研究和试验发展\n",
      "{'LCA-GPT': 0.8031791591570013, 'GLM-4': 0.762518234991953, 'ERNIE-3.5-8K': 0.7619536520907441, 'Qwen1.5-72b': 0.757611659642692}\n",
      "类别： 建筑业\n",
      "{'LCA-GPT': 0.8203105013249284, 'GLM-4': 0.7842578558598534, 'ERNIE-3.5-8K': 0.7759484621427827, 'Qwen1.5-72b': 0.7679492297819105}\n",
      "类别： 非金属矿物制品业\n",
      "{'LCA-GPT': 0.825442724214877, 'GLM-4': 0.7794850773172952, 'ERNIE-3.5-8K': 0.7705819658894356, 'Qwen1.5-72b': 0.760956196511378}\n",
      "类别： 化学原料和化学制品制造业\n",
      "{'LCA-GPT': 0.8266749505338997, 'GLM-4': 0.7717763445843225, 'ERNIE-3.5-8K': 0.764714789459075, 'Qwen1.5-72b': 0.7494578895897701}\n",
      "类别： 废弃资源综合利用业\n",
      "{'LCA-GPT': 0.8150948574588557, 'GLM-4': 0.761772907653432, 'ERNIE-3.5-8K': 0.7655670039213387, 'Qwen1.5-72b': 0.7485851030440847}\n",
      "类别： 农、林、牧、渔业\n",
      "{'LCA-GPT': 0.8184339215282266, 'GLM-4': 0.7447150918363615, 'ERNIE-3.5-8K': 0.75926776605708, 'Qwen1.5-72b': 0.7276846668647445}\n",
      "类别： 电力、热力生产和供应业\n",
      "{'LCA-GPT': 0.811075790060891, 'GLM-4': 0.7617944805395036, 'ERNIE-3.5-8K': 0.769578997104887, 'Qwen1.5-72b': 0.7415060230663845}\n",
      "类别： 汽车制造业\n",
      "{'LCA-GPT': 0.8514739521618547, 'GLM-4': 0.798203267585272, 'ERNIE-3.5-8K': 0.8130694065970936, 'Qwen1.5-72b': 0.7754989156777832}\n"
     ]
    }
   ],
   "source": [
    "class_bert = dict()\n",
    "for clas in class_top10:\n",
    "    print(\"类别：\",clas)\n",
    "    bert_dict = dict()\n",
    "    bert_dict['LCA-GPT'] = bert_score(emb_ans[clas],emb_rag[clas])\n",
    "    bert_dict['GLM-4'] = bert_score(emb_ans[clas],emb_glm[clas])\n",
    "    bert_dict['ERNIE-3.5-8K'] = bert_score(emb_ans[clas],emb_baidu[clas])\n",
    "    bert_dict['Qwen1.5-72b'] = bert_score(emb_ans[clas],emb_qwen72[clas])\n",
    "    class_bert[clas] = bert_dict\n",
    "    print(bert_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bert = pd.DataFrame.from_dict(class_bert,orient='index').T\n",
    "df_bert.to_csv(\"/home/zhangxj/WorkFile/LCA-GPT/LCA_RAG/data/eval/bert.csv\",index=False,encoding=\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建一个字典，将中文标签映射为英文\n",
    "# x_labels = {\n",
    "#     \"LCA理论与相关知识\": \"LCA Theory and Related Knowledge\",\n",
    "#     \"生态保护和环境治理业\": \"Ecological Protection and Environmental Management\",\n",
    "#     \"研究和试验发展\": \"Research and Experimental Development\",\n",
    "#     \"建筑业\": \"Construction Industry\",\n",
    "#     \"非金属矿物制品业\": \"Non-metallic Mineral Products Industry\",\n",
    "#     \"化学原料和化学制品制造业\": \"Chemical Raw Materials and Products Manufacturing\",\n",
    "#     \"废弃资源综合利用业\": \"Waste Resource Recycling Industry\",\n",
    "#     \"农、林、牧、渔业\": \"Agriculture, Forestry, Animal Husbandry, and Fishery\",\n",
    "#     \"电力、热力生产和供应业\": \"Electricity and Heat Production and Supply\",\n",
    "#     \"汽车制造业\": \"Automobile Manufacturing Industry\"\n",
    "# }\n",
    "\n",
    "x_labels_abbr = {\n",
    "    \"LCA理论与相关知识\": \"LCA Theory\",\n",
    "    \"生态保护和环境治理业\": \"Ecological Protection\",\n",
    "    \"研究和试验发展\": \"R&D\",\n",
    "    \"建筑业\": \"Construction\",\n",
    "    \"非金属矿物制品业\": \"Non-metallic Products\",\n",
    "    \"化学原料和化学制品制造业\": \"Chemicals Manufacturing\",\n",
    "    \"废弃资源综合利用业\": \"Waste Recycling\",\n",
    "    \"农、林、牧、渔业\": \"Agriculture & Fisheries\",\n",
    "    \"电力、热力生产和供应业\": \"Energy Production\",\n",
    "    \"汽车制造业\": \"Automobile Manufacturing\"\n",
    "}\n",
    "\n",
    "\n",
    "category_scores_list = [class_cos,class_bert,class_f1,class_roug]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['Times New Roman']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "plt.rcParams['font.size'] = 14  # 设置全局字体大小为14\n",
    "\n",
    "title_list = [\"Similarity\", \"BERTScore\", \"F-1\", \"Rouge\"]\n",
    "# 定义颜色\n",
    "colors_list = [\n",
    "    ['#AEEEEE', '#FFB3BA', '#FFDFD3', '#D6EAF8'],  # 第一子图的颜色\n",
    "    ['#D5F5E3', '#FAD7A0', '#A9DFBF', '#D7BDE2'],  # 第二子图的颜色\n",
    "    ['#F5C1C1', '#D2F5A9', '#A9D2F5', '#F6D8C1'],  # 第三子图的颜色\n",
    "    ['#A3E4D7', '#F7DC6F', '#F5B7B1', '#A9F5BC']   # 第四子图的颜色\n",
    "]\n",
    "\n",
    "# 设置图形布局为4 行 1 列\n",
    "fig, axs = plt.subplots(4, 1, figsize=(12, 10))\n",
    "\n",
    "# 遍历每个category_scores字典并绘制柱状图\n",
    "for idx, category_scores in enumerate(category_scores_list):\n",
    "    ax = axs[idx]  # 确定子图的位置\n",
    "    categories = list(category_scores.keys())\n",
    "    categories_english = [x_labels_abbr[category] for category in categories]\n",
    "    models = list(next(iter(category_scores.values())).keys())\n",
    "    values = [list(category_scores[category].values()) for category in categories]\n",
    "    \n",
    "    colors = colors_list[idx]\n",
    "\n",
    "    for i, model in enumerate(models):\n",
    "        ax.bar(np.arange(len(categories)) + i * 0.2, [row[i] for row in values], 0.2, label=model, color=colors[i])\n",
    "    \n",
    "    # ax.set_title(title_list[idx], fontsize=18)  # 设置标题字体大小\n",
    "    ax.set_ylabel(title_list[idx], fontsize=16)  # 设置y轴标签字体大小\n",
    "    ax.legend(loc='upper left', bbox_to_anchor=(1, 1), borderaxespad=0., fontsize=12)  # 设置图例字体大小\n",
    "\n",
    "    if idx == len(category_scores_list) - 1:\n",
    "        ax.set_xticks(np.arange(len(categories)) + 0.3)\n",
    "        ax.set_xticklabels(categories_english, rotation=30, ha='right', fontsize=14)  # 设置x轴标签字体大小\n",
    "    else:\n",
    "        ax.set_xticks([])\n",
    "\n",
    "# 调整布局\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "plt.savefig('evaluate.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LCA theory and related knowledge</td>\n",
       "      <td>790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ecological protection and environmental govern...</td>\n",
       "      <td>754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Research and experimental development</td>\n",
       "      <td>321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Construction industry</td>\n",
       "      <td>295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Non-metallic mineral products industry</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                name  number\n",
       "0                   LCA theory and related knowledge     790\n",
       "1  Ecological protection and environmental govern...     754\n",
       "2              Research and experimental development     321\n",
       "3                              Construction industry     295\n",
       "4             Non-metallic mineral products industry     183"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "name = \"rouge\"\n",
    "df = pd.read_csv(\"/home/zhangxj/WorkFile/LCA-GPT/LCA_RAG/data/eval/\"+name+\".csv\",encoding=\"utf-8\")\n",
    "df.to_excel(\"/home/zhangxj/WorkFile/LCA-GPT/LCA_RAG/data/eval/\"+name+\".xlsx\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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