data_preprocess.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "from easonsi import utils\n",
    "from collections import defaultdict\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   ID                                               Text         Label\n",
      "0   0   原来是这样啊，又是一个新的道理，简直让我长知识了，七星连珠是很难得的一种现象，这个解析也太到位了  label_878402\n",
      "1   1   老师好厉害，咱就是说，老师，能不能，就是，研究一下能不能穿越🙏 代入感很强，我已经想穿越去...  label_878402\n",
      "2   2  这个七星连珠的现象居然真的存在，我只在小说上面看过，太神奇了吧，至于其他的我也不太懂，科学现...  label_878402\n",
      "3   3        应该听过张老师讲，这一个的人都会觉得特别的清晰吧，而且也明白七星连珠不会引发危害。 ​  label_878402\n",
      "4   4                七星连珠这不是应该是天文知识吗，怎么物理也可以，学霸的世界我果然不懂啊  label_878402\n"
     ]
    }
   ],
   "source": [
    "dpath  = '/home/qsm22/datasets/weibo_topic_recognition/raw_data/train.csv'\n",
    "df = pd.read_csv(dpath,  sep='\\t', encoding=\"utf-8\")\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          ID                                               Text         Label\n",
      "0          0   原来是这样啊，又是一个新的道理，简直让我长知识了，七星连珠是很难得的一种现象，这个解析也太到位了  label_878402\n",
      "1          1   老师好厉害，咱就是说，老师，能不能，就是，研究一下能不能穿越🙏 代入感很强，我已经想穿越去...  label_878402\n",
      "2          2  这个七星连珠的现象居然真的存在，我只在小说上面看过，太神奇了吧，至于其他的我也不太懂，科学现...  label_878402\n",
      "3          3        应该听过张老师讲，这一个的人都会觉得特别的清晰吧，而且也明白七星连珠不会引发危害。 ​  label_878402\n",
      "4          4                七星连珠这不是应该是天文知识吗，怎么物理也可以，学霸的世界我果然不懂啊  label_878402\n",
      "...      ...                                                ...           ...\n",
      "77309  77309  甘肃兰州2022年文化和自然遗产日主场城市活动亮点抢先看甘肃兰州2022年文化和自然遗产日主...   label_82331\n",
      "77310  77310  《焦点访谈》 20220611 让文物活起来 国宝自己会“说话” 《焦点访谈》 202206...   label_82331\n",
      "77311  77311                             甘肃加大农机装备力量投入“三夏”生产网页链接   label_82331\n",
      "77312  77312                             小范书记们，村民都在等荐书 网页链接 ​​​   label_82331\n",
      "77313  77313                      主汛期洪涝、干旱风险高 甘肃坚持“宁可防空，不可空防” ​   label_82331\n",
      "\n",
      "[77314 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transfer_to_json(df,out_path):\n",
    "    fw = open(out_path, 'w', encoding='utf-8')\n",
    "    for i in range(len(df)):\n",
    "        l = {}\n",
    "        l[\"id\"] = int(df.iloc[i,0])\n",
    "        l[\"text\"] = df.iloc[i,1]\n",
    "        l[\"label\"] = df.iloc[i,2].split(\"，\")\n",
    "        l = json.dumps(l, ensure_ascii=False)\n",
    "        fw.write(l + '\\n')\n",
    "    fw.close()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "transfer_to_json(df,\"/home/qsm22/datasets/weibo_topic_recognition/train.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = utils.LoadJsonl(\"/home/qsm22/datasets/weibo_topic_recognition/train.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77314"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "categories = set()\n",
    "for d in data:\n",
    "    for label in d['label']:\n",
    "        categories.add(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1399"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count(data):\n",
    "    text_len_list = []\n",
    "    label_count_list = []\n",
    "    for d in data:\n",
    "        text = d['text']\n",
    "        label = d['label']\n",
    "        text_len_list.append(len(text))\n",
    "        label_count_list.append(len(label))\n",
    "    \n",
    "    return text_len_list,label_count_list\n",
    "    #return sum(text_len_list)/len(text_len_list),max(text_len_list), sum(label_count_list)/len(label_count_list),max(label_count_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_len_list,label_count_list = count(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55.01827612075433 271 1.51751299893939 8\n"
     ]
    }
   ],
   "source": [
    "print(sum(text_len_list)/len(text_len_list),max(text_len_list), sum(label_count_list)/len(label_count_list),max(label_count_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(text_len_list)\n",
    "plt.xlabel('text length')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(label_count_list)\n",
    "plt.xlabel('label_count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "def label_dis(data):\n",
    "    res = defaultdict(int)\n",
    "    for d in data:\n",
    "        for label in d['label']:\n",
    "            res[label] += 1\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_count = label_dis(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('label_1191241', 10235),\n",
       " ('label_1008181', 10023),\n",
       " ('label_1281707', 4680),\n",
       " ('label_1515062', 4671),\n",
       " ('label_472394', 4671),\n",
       " ('label_742793', 2783),\n",
       " ('label_1411524', 1906),\n",
       " ('label_1265038', 1881),\n",
       " ('label_896157', 1730),\n",
       " ('label_1064693', 1719),\n",
       " ('label_467023', 1641),\n",
       " ('label_1474127', 1421),\n",
       " ('label_1227838', 1066),\n",
       " ('label_1166118', 1066),\n",
       " ('label_19479', 1060),\n",
       " ('label_287908', 828),\n",
       " ('label_753343', 719),\n",
       " ('label_1056127', 716),\n",
       " ('label_529001', 711),\n",
       " ('label_512340', 704),\n",
       " ('label_1438711', 695),\n",
       " ('label_313744', 675),\n",
       " ('label_1517029', 652),\n",
       " ('label_408651', 618),\n",
       " ('label_167080', 607),\n",
       " ('label_727417', 588),\n",
       " ('label_82331', 585),\n",
       " ('label_16631', 491),\n",
       " ('label_480366', 489),\n",
       " ('label_1182442', 486),\n",
       " ('label_197715', 482),\n",
       " ('label_1319820', 477),\n",
       " ('label_432046', 461),\n",
       " ('label_238628', 454),\n",
       " ('label_648491', 437),\n",
       " ('label_1463330', 434),\n",
       " ('label_1055138', 413),\n",
       " ('label_1254017', 413),\n",
       " ('label_635234', 412),\n",
       " ('label_1279907', 412),\n",
       " ('label_1489598', 403),\n",
       " ('label_173876', 403),\n",
       " ('label_249715', 401),\n",
       " ('label_396610', 398),\n",
       " ('label_415546', 388),\n",
       " ('label_1005240', 374),\n",
       " ('label_505902', 341),\n",
       " ('label_1368268', 334),\n",
       " ('label_878402', 330),\n",
       " ('label_1226168', 325),\n",
       " ('label_304', 325),\n",
       " ('label_98029', 325),\n",
       " ('label_1548', 324),\n",
       " ('label_575306', 324),\n",
       " ('label_1313183', 320),\n",
       " ('label_1176064', 320),\n",
       " ('label_24052', 314),\n",
       " ('label_980197', 310),\n",
       " ('label_275722', 305),\n",
       " ('label_654481', 305),\n",
       " ('label_99901', 299),\n",
       " ('label_1418418', 282),\n",
       " ('label_1348452', 268),\n",
       " ('label_563639', 253),\n",
       " ('label_235991', 252),\n",
       " ('label_1377375', 252),\n",
       " ('label_895459', 248),\n",
       " ('label_806142', 246),\n",
       " ('label_811568', 244),\n",
       " ('label_329142', 240),\n",
       " ('label_284405', 237),\n",
       " ('label_117242', 237),\n",
       " ('label_140137', 237),\n",
       " ('label_860179', 237),\n",
       " ('label_70283', 232),\n",
       " ('label_1173572', 226),\n",
       " ('label_924761', 222),\n",
       " ('label_1195225', 213),\n",
       " ('label_139774', 213),\n",
       " ('label_8262', 212),\n",
       " ('label_950164', 211),\n",
       " ('label_1321282', 211),\n",
       " ('label_303782', 209),\n",
       " ('label_895990', 209),\n",
       " ('label_346203', 207),\n",
       " ('label_912660', 206),\n",
       " ('label_336257', 206),\n",
       " ('label_1450990', 203),\n",
       " ('label_960136', 200),\n",
       " ('label_51433', 199),\n",
       " ('label_904501', 195),\n",
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     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(label_count.items(), key=lambda x : x[1],reverse=True)\n"
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  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
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V1tYW6eUCAAALRTxgHnroIS1btkz/8z//oz179uihhx5SWVmZlixZ4swpKyvT4sWLtXz5ctXU1Khv377Kzc3V4cOHnTkFBQWqr69XRUWFNmzYoKqqKs2YMSPSywUAABaKMSe+NBIBP/3pT+XxePSnP/3JOZafn6+kpCQ9/fTTMsbI5/Pptttu0+233y5JCgaD8ng8WrlypaZMmaI9e/YoMzNTW7duVVZWliSpvLxckyZN0ieffCKfz/dP1xEKheR2uxUMBuVyuSJ5iRo696WInu9EHy3Ii9q5AQDo7k73+Tvir8Bccsklqqys1AcffCBJeu+99/TWW29p4sSJkqR9+/YpEAgoJyfHuY/b7VZ2draqq6slSdXV1UpOTnbiRZJycnIUGxurmpqaUz5ue3u7QqFQ2A0AAPRMcZE+4dy5cxUKhTRixAj16tVLx44d0wMPPKCCggJJUiAQkCR5PJ6w+3k8HmcsEAgoNTU1fKFxcUpJSXHmfFVpaanuueeeSF8OAADohiL+Csxzzz2n1atX65lnntH27du1atUqPfLII1q1alWkHypMSUmJgsGgc2tsbIzq4wEAgK4T8VdgZs+erblz52rKlCmSpNGjR+vjjz9WaWmppk2bJq/XK0lqamrS4MGDnfs1NTXp/PPPlyR5vV41NzeHnffo0aNqaWlx7v9VCQkJSkhIiPTlAACAbijir8B88cUXio0NP22vXr3U2dkpScrIyJDX61VlZaUzHgqFVFNTI7/fL0ny+/1qbW1VbW2tM2fjxo3q7OxUdnZ2pJcMAAAsE/FXYK666io98MADSk9P16hRo/Tuu+9q4cKF+u1vfytJiomJ0cyZM3X//fdr2LBhysjI0Lx58+Tz+TR58mRJ0siRIzVhwgRNnz5dy5cvV0dHh4qLizVlypTT+gQSAADo2SIeMEuWLNG8efP0+9//Xs3NzfL5fPrd736n+fPnO3PuuOMOHTp0SDNmzFBra6suu+wylZeXKzEx0ZmzevVqFRcXa9y4cYqNjVV+fr4WL14c6eUCAAALRfx7YLoLvgcGAAD7dNn3wAAAAEQbAQMAAKxDwAAAAOsQMAAAwDoEDAAAsA4BAwAArEPAAAAA6xAwAADAOgQMAACwDgEDAACsQ8AAAADrEDAAAMA6BAwAALAOAQMAAKxDwAAAAOsQMAAAwDoEDAAAsA4BAwAArEPAAAAA6xAwAADAOgQMAACwDgEDAACsQ8AAAADrEDAAAMA6BAwAALAOAQMAAKxDwAAAAOsQMAAAwDoEDAAAsA4BAwAArEPAAAAA6xAwAADAOgQMAACwDgEDAACsQ8AAAADrEDAAAMA6BAwAALAOAQMAAKxDwAAAAOsQMAAAwDpRCZhPP/1Uv/rVrzRw4EAlJSVp9OjR2rZtmzNujNH8+fM1ePBgJSUlKScnR3v37g07R0tLiwoKCuRyuZScnKzCwkK1tbVFY7kAAMAyEQ+Yzz//XJdeeql69+6tV155Rbt379Z///d/a8CAAc6csrIyLV68WMuXL1dNTY369u2r3NxcHT582JlTUFCg+vp6VVRUaMOGDaqqqtKMGTMivVwAAGChGGOMieQJ586dq7fffltvvvnmKceNMfL5fLrtttt0++23S5KCwaA8Ho9WrlypKVOmaM+ePcrMzNTWrVuVlZUlSSovL9ekSZP0ySefyOfz/dN1hEIhud1uBYNBuVyuyF2gpKFzX4ro+U700YK8qJ0bAIDu7nSfvyP+CswLL7ygrKws/eIXv1BqaqrGjBmjJ554whnft2+fAoGAcnJynGNut1vZ2dmqrq6WJFVXVys5OdmJF0nKyclRbGysampqTvm47e3tCoVCYTcAANAzRTxgPvzwQy1btkzDhg3Tq6++qhtvvFE333yzVq1aJUkKBAKSJI/HE3Y/j8fjjAUCAaWmpoaNx8XFKSUlxZnzVaWlpXK73c4tLS0t0pcGAAC6iYgHTGdnpy644AI9+OCDGjNmjGbMmKHp06dr+fLlkX6oMCUlJQoGg86tsbExqo8HAAC6TsQDZvDgwcrMzAw7NnLkSO3fv1+S5PV6JUlNTU1hc5qampwxr9er5ubmsPGjR4+qpaXFmfNVCQkJcrlcYTcAANAzRTxgLr30UjU0NIQd++CDDzRkyBBJUkZGhrxeryorK53xUCikmpoa+f1+SZLf71dra6tqa2udORs3blRnZ6eys7MjvWQAAGCZuEif8NZbb9Ull1yiBx98UNdcc422bNmiFStWaMWKFZKkmJgYzZw5U/fff7+GDRumjIwMzZs3Tz6fT5MnT5b0j1dsJkyY4PzqqaOjQ8XFxZoyZcppfQIJAAD0bBEPmAsvvFDr1q1TSUmJ7r33XmVkZOixxx5TQUGBM+eOO+7QoUOHNGPGDLW2tuqyyy5TeXm5EhMTnTmrV69WcXGxxo0bp9jYWOXn52vx4sWRXi4AALBQxL8Hprvge2AAALBPl30PDAAAQLQRMAAAwDoEDAAAsA4BAwAArEPAAAAA6xAwAADAOgQMAACwDgEDAACsQ8AAAADrEDAAAMA6BAwAALAOAQMAAKxDwAAAAOsQMAAAwDoEDAAAsA4BAwAArEPAAAAA6xAwAADAOgQMAACwDgEDAACsQ8AAAADrEDAAAMA6BAwAALAOAQMAAKxDwAAAAOsQMAAAwDoEDAAAsA4BAwAArEPAAAAA6xAwAADAOgQMAACwDgEDAACsQ8AAAADrEDAAAMA6BAwAALAOAQMAAKxDwAAAAOsQMAAAwDoEDAAAsA4BAwAArBP1gFmwYIFiYmI0c+ZM59jhw4dVVFSkgQMHql+/fsrPz1dTU1PY/fbv36+8vDz16dNHqampmj17to4ePRrt5QIAAAtENWC2bt2qP/7xj/rxj38cdvzWW2/Viy++qLVr12rTpk06cOCArr76amf82LFjysvL05EjR/TOO+9o1apVWrlypebPnx/N5QIAAEtELWDa2tpUUFCgJ554QgMGDHCOB4NB/elPf9LChQv1H//xHxo7dqyeeuopvfPOO9q8ebMk6bXXXtPu3bv19NNP6/zzz9fEiRN13333aenSpTpy5Ei0lgwAACwRtYApKipSXl6ecnJywo7X1taqo6Mj7PiIESOUnp6u6upqSVJ1dbVGjx4tj8fjzMnNzVUoFFJ9fX20lgwAACwRF42TrlmzRtu3b9fWrVtPGgsEAoqPj1dycnLYcY/Ho0Ag4Mw5MV6Ojx8fO5X29na1t7c7P4dCoe9yCQAAoBuL+CswjY2NuuWWW7R69WolJiZG+vRfq7S0VG6327mlpaWdtccGAABnV8QDpra2Vs3NzbrgggsUFxenuLg4bdq0SYsXL1ZcXJw8Ho+OHDmi1tbWsPs1NTXJ6/VKkrxe70mfSjr+8/E5X1VSUqJgMOjcGhsbI31pAACgm4h4wIwbN047d+5UXV2dc8vKylJBQYHz371791ZlZaVzn4aGBu3fv19+v1+S5Pf7tXPnTjU3NztzKioq5HK5lJmZecrHTUhIkMvlCrsBAICeKeLvgenfv7/OPffcsGN9+/bVwIEDneOFhYWaNWuWUlJS5HK5dNNNN8nv9+viiy+WJI0fP16ZmZmaOnWqysrKFAgEdNddd6moqEgJCQmRXjIAALBMVN7E+888+uijio2NVX5+vtrb25Wbm6vHH3/cGe/Vq5c2bNigG2+8UX6/X3379tW0adN07733dsVyAQBANxNjjDFdvYhoCIVCcrvdCgaDEf910tC5L0X0fCf6aEFe1M4NAEB3d7rP3/xbSAAAwDoEDAAAsA4BAwAArEPAAAAA6xAwAADAOgQMAACwDgEDAACsQ8AAAADrEDAAAMA6BAwAALAOAQMAAKxDwAAAAOsQMAAAwDoEDAAAsA4BAwAArEPAAAAA6xAwAADAOgQMAACwDgEDAACsQ8AAAADrEDAAAMA6BAwAALAOAQMAAKxDwAAAAOsQMAAAwDoEDAAAsA4BAwAArEPAAAAA6xAwAADAOgQMAACwDgEDAACsQ8AAAADrEDAAAMA6BAwAALAOAQMAAKxDwAAAAOsQMAAAwDoEDAAAsA4BAwAArEPAAAAA6xAwAADAOhEPmNLSUl144YXq37+/UlNTNXnyZDU0NITNOXz4sIqKijRw4ED169dP+fn5ampqCpuzf/9+5eXlqU+fPkpNTdXs2bN19OjRSC8XAABYKOIBs2nTJhUVFWnz5s2qqKhQR0eHxo8fr0OHDjlzbr31Vr344otau3atNm3apAMHDujqq692xo8dO6a8vDwdOXJE77zzjlatWqWVK1dq/vz5kV4uAACwUIwxxkTzAT777DOlpqZq06ZNuuKKKxQMBvW9731PzzzzjH7+859Lkt5//32NHDlS1dXVuvjii/XKK6/opz/9qQ4cOCCPxyNJWr58uebMmaPPPvtM8fHx//RxQ6GQ3G63gsGgXC5XRK9p6NyXInq+E320IC9q5wYAoLs73efvqL8HJhgMSpJSUlIkSbW1tero6FBOTo4zZ8SIEUpPT1d1dbUkqbq6WqNHj3biRZJyc3MVCoVUX19/ysdpb29XKBQKuwEAgJ4pqgHT2dmpmTNn6tJLL9W5554rSQoEAoqPj1dycnLYXI/Ho0Ag4Mw5MV6Ojx8fO5XS0lK53W7nlpaWFuGrAQAA3UVUA6aoqEi7du3SmjVrovkwkqSSkhIFg0Hn1tjYGPXHBAAAXSMuWicuLi7Whg0bVFVVpXPOOcc57vV6deTIEbW2toa9CtPU1CSv1+vM2bJlS9j5jn9K6ficr0pISFBCQkKErwIAAHRHEX8Fxhij4uJirVu3Ths3blRGRkbY+NixY9W7d29VVlY6xxoaGrR//375/X5Jkt/v186dO9Xc3OzMqaiokMvlUmZmZqSXDAAALBPxV2CKior0zDPP6C9/+Yv69+/vvGfF7XYrKSlJbrdbhYWFmjVrllJSUuRyuXTTTTfJ7/fr4osvliSNHz9emZmZmjp1qsrKyhQIBHTXXXepqKiIV1kAAEDkA2bZsmWSpJ/85Cdhx5966in95je/kSQ9+uijio2NVX5+vtrb25Wbm6vHH3/cmdurVy9t2LBBN954o/x+v/r27atp06bp3nvvjfRyAQCAhaL+PTBdhe+BAQDAPt3me2AAAAAijYABAADWIWAAAIB1CBgAAGAdAgYAAFiHgAEAANYhYAAAgHUIGAAAYB0CBgAAWIeAAQAA1iFgAACAdQgYAABgHQIGAABYh4ABAADWIWAAAIB1CBgAAGAdAgYAAFiHgAEAANYhYAAAgHUIGAAAYB0CBgAAWIeAAQAA1iFgAACAdQgYAABgHQIGAABYh4ABAADWIWAAAIB1CBgAAGAdAgYAAFiHgAEAANYhYAAAgHUIGAAAYB0CBgAAWIeAAQAA1iFgAACAdQgYAABgHQIGAABYh4ABAADWIWAAAIB1CBgAAGCdbh0wS5cu1dChQ5WYmKjs7Gxt2bKlq5cEAAC6gbiuXsDXefbZZzVr1iwtX75c2dnZeuyxx5Sbm6uGhgalpqZ29fKiZujcl6Jy3o8W5EXlvAAAdIVu+wrMwoULNX36dF1//fXKzMzU8uXL1adPHz355JNdvTQAANDFuuUrMEeOHFFtba1KSkqcY7GxscrJyVF1dfUp79Pe3q729nbn52AwKEkKhUIRX19n+xcRP2e0pd+6tquX8K3suie3q5cAADiLjj9vG2O+cV63DJi///3vOnbsmDweT9hxj8ej999//5T3KS0t1T333HPS8bS0tKisEWeH+7GuXgEAoCscPHhQbrf7a8e7ZcB8GyUlJZo1a5bzc2dnp1paWjRw4EDFxMRE7HFCoZDS0tLU2Ngol8sVsfPiH9jf6GOPo4v9jS72N7q6w/4aY3Tw4EH5fL5vnNctA2bQoEHq1auXmpqawo43NTXJ6/We8j4JCQlKSEgIO5acnBytJcrlcvE/TxSxv9HHHkcX+xtd7G90dfX+ftMrL8d1yzfxxsfHa+zYsaqsrHSOdXZ2qrKyUn6/vwtXBgAAuoNu+QqMJM2aNUvTpk1TVlaWLrroIj322GM6dOiQrr/++q5eGgAA6GLdNmB++ctf6rPPPtP8+fMVCAR0/vnnq7y8/KQ39p5tCQkJ+sMf/nDSr6sQGexv9LHH0cX+Rhf7G1027W+M+WefUwIAAOhmuuV7YAAAAL4JAQMAAKxDwAAAAOsQMAAAwDoEzBlaunSphg4dqsTERGVnZ2vLli1dvaRup7S0VBdeeKH69++v1NRUTZ48WQ0NDWFzDh8+rKKiIg0cOFD9+vVTfn7+SV9cuH//fuXl5alPnz5KTU3V7NmzdfTo0bA5b7zxhi644AIlJCToRz/6kVauXBnty+t2FixYoJiYGM2cOdM5xv5+N59++ql+9atfaeDAgUpKStLo0aO1bds2Z9wYo/nz52vw4MFKSkpSTk6O9u7dG3aOlpYWFRQUyOVyKTk5WYWFhWprawubs2PHDl1++eVKTExUWlqaysrKzsr1daVjx45p3rx5ysjIUFJSkn74wx/qvvvuC/t3b9jfM1NVVaWrrrpKPp9PMTExWr9+fdj42dzPtWvXasSIEUpMTNTo0aP18ssvR/x6T7wwnKY1a9aY+Ph48+STT5r6+nozffp0k5ycbJqamrp6ad1Kbm6ueeqpp8yuXbtMXV2dmTRpkklPTzdtbW3OnBtuuMGkpaWZyspKs23bNnPxxRebSy65xBk/evSoOffcc01OTo559913zcsvv2wGDRpkSkpKnDkffvih6dOnj5k1a5bZvXu3WbJkienVq5cpLy8/q9fblbZs2WKGDh1qfvzjH5tbbrnFOc7+fnstLS1myJAh5je/+Y2pqakxH374oXn11VfNX//6V2fOggULjNvtNuvXrzfvvfee+dnPfmYyMjLMl19+6cyZMGGCOe+888zmzZvNm2++aX70ox+Za6+91hkPBoPG4/GYgoICs2vXLvPnP//ZJCUlmT/+8Y9n9XrPtgceeMAMHDjQbNiwwezbt8+sXbvW9OvXzyxatMiZw/6emZdfftnceeed5vnnnzeSzLp168LGz9Z+vv3226ZXr16mrKzM7N6929x1112md+/eZufOnVG5bgLmDFx00UWmqKjI+fnYsWPG5/OZ0tLSLlxV99fc3GwkmU2bNhljjGltbTW9e/c2a9eudebs2bPHSDLV1dXGmH/8DxkbG2sCgYAzZ9myZcblcpn29nZjjDF33HGHGTVqVNhj/fKXvzS5ubnRvqRu4eDBg2bYsGGmoqLC/Pu//7sTMOzvdzNnzhxz2WWXfe14Z2en8Xq95uGHH3aOtba2moSEBPPnP//ZGGPM7t27jSSzdetWZ84rr7xiYmJizKeffmqMMebxxx83AwYMcPb7+GMPHz480pfUreTl5Znf/va3YceuvvpqU1BQYIxhf7+rrwbM2dzPa665xuTl5YWtJzs72/zud7+L6DUex6+QTtORI0dUW1urnJwc51hsbKxycnJUXV3dhSvr/oLBoCQpJSVFklRbW6uOjo6wvRwxYoTS09Odvayurtbo0aPDvrgwNzdXoVBI9fX1zpwTz3F8zr/Kn0dRUZHy8vJO2gP297t54YUXlJWVpV/84hdKTU3VmDFj9MQTTzjj+/btUyAQCNsbt9ut7OzssP1NTk5WVlaWMycnJ0exsbGqqalx5lxxxRWKj4935uTm5qqhoUGff/55tC+zy1xyySWqrKzUBx98IEl677339NZbb2nixImS2N9IO5v7ebb/ziBgTtPf//53HTt27KRvAvZ4PAoEAl20qu6vs7NTM2fO1KWXXqpzzz1XkhQIBBQfH3/SP7Z54l4GAoFT7vXxsW+aEwqF9OWXX0bjcrqNNWvWaPv27SotLT1pjP39bj788EMtW7ZMw4YN06uvvqobb7xRN998s1atWiXp/+/PN/1dEAgElJqaGjYeFxenlJSUM/oz6Inmzp2rKVOmaMSIEerdu7fGjBmjmTNnqqCgQBL7G2lncz+/bk609rvb/lMC6BmKioq0a9cuvfXWW129lB6jsbFRt9xyiyoqKpSYmNjVy+lxOjs7lZWVpQcffFCSNGbMGO3atUvLly/XtGnTunh19nvuuee0evVqPfPMMxo1apTq6uo0c+ZM+Xw+9hdnhFdgTtOgQYPUq1evkz7J0dTUJK/X20Wr6t6Ki4u1YcMGvf766zrnnHOc416vV0eOHFFra2vY/BP30uv1nnKvj4990xyXy6WkpKRIX063UVtbq+bmZl1wwQWKi4tTXFycNm3apMWLFysuLk4ej4f9/Q4GDx6szMzMsGMjR47U/v37Jf3//fmmvwu8Xq+am5vDxo8ePaqWlpYz+jPoiWbPnu28CjN69GhNnTpVt956q/NqIvsbWWdzP79uTrT2m4A5TfHx8Ro7dqwqKyudY52dnaqsrJTf7+/ClXU/xhgVFxdr3bp12rhxozIyMsLGx44dq969e4ftZUNDg/bv3+/spd/v186dO8P+p6qoqJDL5XKeXPx+f9g5js/p6X8e48aN086dO1VXV+fcsrKyVFBQ4Pw3+/vtXXrppSd97P+DDz7QkCFDJEkZGRnyer1hexMKhVRTUxO2v62traqtrXXmbNy4UZ2dncrOznbmVFVVqaOjw5lTUVGh4cOHa8CAAVG7vq72xRdfKDY2/KmnV69e6uzslMT+RtrZ3M+z/ndGVN4a3EOtWbPGJCQkmJUrV5rdu3ebGTNmmOTk5LBPcsCYG2+80bjdbvPGG2+Yv/3tb87tiy++cObccMMNJj093WzcuNFs27bN+P1+4/f7nfHjH/MdP368qaurM+Xl5eZ73/veKT/mO3v2bLNnzx6zdOnSf4mP+Z7KiZ9CMob9/S62bNli4uLizAMPPGD27t1rVq9ebfr06WOefvppZ86CBQtMcnKy+ctf/mJ27Nhh/vM///OUH0sdM2aMqampMW+99ZYZNmxY2MdSW1tbjcfjMVOnTjW7du0ya9asMX369OmRH/M90bRp08z3v/9952PUzz//vBk0aJC54447nDns75k5ePCgeffdd827775rJJmFCxead99913z88cfGmLO3n2+//baJi4szjzzyiNmzZ4/5wx/+wMeou5MlS5aY9PR0Ex8fby666CKzefPmrl5StyPplLennnrKmfPll1+a3//+92bAgAGmT58+5r/+67/M3/72t7DzfPTRR2bixIkmKSnJDBo0yNx2222mo6MjbM7rr79uzj//fBMfH29+8IMfhD3Gv5KvBgz7+928+OKL5txzzzUJCQlmxIgRZsWKFWHjnZ2dZt68ecbj8ZiEhAQzbtw409DQEDbn//7v/8y1115r+vXrZ1wul7n++uvNwYMHw+a899575rLLLjMJCQnm+9//vlmwYEHUr62rhUIhc8stt5j09HSTmJhofvCDH5g777wz7OO57O+Zef3110/5d+60adOMMWd3P5977jnzb//2byY+Pt6MGjXKvPTSS1G77hhjTvj6QwAAAAvwHhgAAGAdAgYAAFiHgAEAANYhYAAAgHUIGAAAYB0CBgAAWIeAAQAA1iFgAACAdQgYAABgHQIGAABYh4ABAADWIWAAAIB1/h/zH0PWBGntFAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(label_count.values(),bins=20)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.0 ('weibo_topic')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.15"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "f4ebd6e7441cb75761eefbf97f50f22bf8f737306b2521ee0718b55fda814bdd"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}