read-two-stage-log.ipynb

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  {
   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['pubmed-twostage.log',\n",
       " 'cora+OC-GCN.log',\n",
       " 'cora-twostage.log',\n",
       " 'citeseer-twostage.log']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loglist=os.listdir('./log/')\n",
    "loglist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets=['cora','citeseer','pubmed']\n",
    "seeds=['52','58','666','1122']\n",
    "name=['dataset','datamode','ad-method','emb-method','AUC','seed']"
   ]
  },
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   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
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    "txt=[]\n",
    "dm,ad,emb,val_auc,auc=None,None,None,None,None\n",
    "for dataset in datasets:\n",
    "    for seed in seeds:\n",
    "        path=f'./log/{dataset}-twostage-{seed}.log'\n",
    "        with open(path, 'r') as f:\n",
    "            #print(path)\n",
    "            text=f.readlines()\n",
    "            for t in text:\n",
    "                #print(t)\n",
    "                if name[1] in t:\n",
    "                    idx=t.find(name[1])\n",
    "                    dm=t[idx+9:idx+11]\n",
    "                    #print(dm)\n",
    "                if name[2] in t:\n",
    "                    idx=t.find(name[2])\n",
    "                    ad=t[idx+10:idx+13]\n",
    "\n",
    "                if name[3] in t:\n",
    "                    idx=t.find(name[3])\n",
    "                    emb=t[idx+11:idx+16]\n",
    "                    #print(emb)\n",
    "                if name[4] in t and val_auc==None:\n",
    "                    idx=t.find(name[4])\n",
    "                    val_auc=t[idx+4:idx+9]\n",
    "                    continue\n",
    "                if name[4] in t:\n",
    "                    idx=t.find(name[4])\n",
    "                    auc=t[idx+4:idx+9]                \n",
    "                    #print(auc)\n",
    "\n",
    "                if dm and ad and emb and auc:    \n",
    "                    dicts={name[0]:dataset,name[1]:dm,name[2]:ad,name[3]:emb,name[4]:auc,name[5]:seed}\n",
    "                    dm,ad,emb,val_auc,auc=None,None,None,None,None\n",
    "                    #print(dicts)\n",
    "                    txt.append(dicts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "with open(\"./log.csv\",'w',newline='') as t:#numline是来控制空的行数的\n",
    "    writer=csv.DictWriter(t,name)#这一步是创建一个csv的写入器（个人理解）\n",
    "    writer.writeheader()#写入标签\n",
    "    writer.writerows(txt)#写入样本数据 按行写"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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