ipynbs/cbdb_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import time\n",
    "import json\n",
    "from collections import defaultdict\n",
    "\n",
    "import scipy as sc\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "BASE_DIR = os.path.dirname(os.path.abspath('.'))\n",
    "DATA_DIR = os.path.join(BASE_DIR, 'datas')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cbdb_edgelist_fpath = os.path.join(DATA_DIR, 'cbdb_edgelist.tsv')\n",
    "cbdb_edgeinfo_fpath = os.path.join(DATA_DIR, 'cbdb_edgeinfo.tsv')\n",
    "cbdb_nodeinfo_fpath = os.path.join(DATA_DIR, 'cbdb_nodeinfo.tsv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "edge_df = pd.read_csv(cbdb_edgelist_fpath, sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PersonIdA</th>\n",
       "      <th>PersonIdB</th>\n",
       "      <th>Sign</th>\n",
       "      <th>Time</th>\n",
       "      <th>Note</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>449689</td>\n",
       "      <td>13575</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40763</td>\n",
       "      <td>10173</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40763</td>\n",
       "      <td>10173</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40763</td>\n",
       "      <td>10173</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40763</td>\n",
       "      <td>10173</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149380</th>\n",
       "      <td>93201</td>\n",
       "      <td>176408</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149381</th>\n",
       "      <td>93201</td>\n",
       "      <td>445999</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149382</th>\n",
       "      <td>93201</td>\n",
       "      <td>448917</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149383</th>\n",
       "      <td>93201</td>\n",
       "      <td>93832</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149384</th>\n",
       "      <td>54424</td>\n",
       "      <td>54414</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149385 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        PersonIdA  PersonIdB  Sign  Time  Note\n",
       "0          449689      13575     1    -1   438\n",
       "1           40763      10173     1    -1   146\n",
       "2           40763      10173     1    -1   146\n",
       "3           40763      10173     1    -1   444\n",
       "4           40763      10173     1    -1   444\n",
       "...           ...        ...   ...   ...   ...\n",
       "149380      93201     176408     1    -1   437\n",
       "149381      93201     445999     1    -1   437\n",
       "149382      93201     448917     1    -1   437\n",
       "149383      93201      93832     1    -1   438\n",
       "149384      54424      54414     1    -1    32\n",
       "\n",
       "[149385 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edge_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "edgeinfo_df = pd.read_csv(cbdb_edgeinfo_fpath, sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Note</th>\n",
       "      <th>Info</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>437</td>\n",
       "      <td>{\"Info\": \"\\u8d08\\u8a69\\u3001\\u6587\", \"Sign\": 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>438</td>\n",
       "      <td>{\"Info\": \"\\u6536\\u5230Y\\u7684\\u8d08\\u8a69\\u300...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>43</td>\n",
       "      <td>{\"Info\": \"\\u5893\\u8a8c\\u9298\\u7531Y\\u6240\\u4f5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>{\"Info\": \"\\u70baY\\u4f5c\\u5893\\u8a8c\\u9298\", \"S...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>{\"Info\": \"\\u66f8\\u5e8f\\u7531Y\\u6240\\u4f5c\", \"S...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>547</td>\n",
       "      <td>{\"Info\": \"\\u6c42\\u4ed6\\u4eba\\uff08\\u7b2c\\u4e09...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>248</td>\n",
       "      <td>{\"Info\": \"\\u7fa9\\u838a\\u898f\\u77e9\\u5e8f\\u7531...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>548</td>\n",
       "      <td>{\"Info\": \"\\u795e\\u9053\\u7891\\u4fc2\\u7531Y\\u541...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>189</td>\n",
       "      <td>{\"Info\": \"\\u53cd\\u5c0d\\u8d66\\u514d\", \"Sign\": -1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>508</td>\n",
       "      <td>{\"Info\": \"\\u88abY\\u8831\\u60d1\", \"Sign\": -1}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>451 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Note                                               Info\n",
       "0     437    {\"Info\": \"\\u8d08\\u8a69\\u3001\\u6587\", \"Sign\": 1}\n",
       "1     438  {\"Info\": \"\\u6536\\u5230Y\\u7684\\u8d08\\u8a69\\u300...\n",
       "2      43  {\"Info\": \"\\u5893\\u8a8c\\u9298\\u7531Y\\u6240\\u4f5...\n",
       "3      44  {\"Info\": \"\\u70baY\\u4f5c\\u5893\\u8a8c\\u9298\", \"S...\n",
       "4      33  {\"Info\": \"\\u66f8\\u5e8f\\u7531Y\\u6240\\u4f5c\", \"S...\n",
       "..    ...                                                ...\n",
       "446   547  {\"Info\": \"\\u6c42\\u4ed6\\u4eba\\uff08\\u7b2c\\u4e09...\n",
       "447   248  {\"Info\": \"\\u7fa9\\u838a\\u898f\\u77e9\\u5e8f\\u7531...\n",
       "448   548  {\"Info\": \"\\u795e\\u9053\\u7891\\u4fc2\\u7531Y\\u541...\n",
       "449   189   {\"Info\": \"\\u53cd\\u5c0d\\u8d66\\u514d\", \"Sign\": -1}\n",
       "450   508        {\"Info\": \"\\u88abY\\u8831\\u60d1\", \"Sign\": -1}\n",
       "\n",
       "[451 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edgeinfo_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Info': '贈詩、文', 'Sign': 1}\n"
     ]
    }
   ],
   "source": [
    "print(json.loads(edgeinfo_df.iloc[0]['Info']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodeinfo_df = pd.read_csv(cbdb_nodeinfo_fpath, sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>PersonId</th>\n",
       "      <th>Info</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>449689</td>\n",
       "      <td>{\"PersonId\": \"449689\", \"EngName\": \"Liu Jing\", ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>436212</td>\n",
       "      <td>{\"PersonId\": \"436212\", \"EngName\": \"Huang Zhuoh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>281900</td>\n",
       "      <td>{\"PersonId\": \"281900\", \"EngName\": \"Wu You\", \"C...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>328918</td>\n",
       "      <td>{\"PersonId\": \"328918\", \"EngName\": \"Yang Yugao\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>395541</td>\n",
       "      <td>{\"PersonId\": \"395541\", \"EngName\": \"Tang Xuzu\",...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463354</th>\n",
       "      <td>357829</td>\n",
       "      <td>{\"PersonId\": \"357829\", \"EngName\": \"Li Ru\", \"Ch...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463355</th>\n",
       "      <td>442762</td>\n",
       "      <td>{\"PersonId\": \"442762\", \"EngName\": \"Yao Ying\", ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463356</th>\n",
       "      <td>497925</td>\n",
       "      <td>{\"PersonId\": \"497925\", \"EngName\": \"Wang Lanbi\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463357</th>\n",
       "      <td>389134</td>\n",
       "      <td>{\"PersonId\": \"389134\", \"EngName\": \"Yu Shiling\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463358</th>\n",
       "      <td>15579</td>\n",
       "      <td>{\"PersonId\": \"15579\", \"EngName\": \"Wang Gongzhi...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>463359 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        PersonId                                               Info\n",
       "0         449689  {\"PersonId\": \"449689\", \"EngName\": \"Liu Jing\", ...\n",
       "1         436212  {\"PersonId\": \"436212\", \"EngName\": \"Huang Zhuoh...\n",
       "2         281900  {\"PersonId\": \"281900\", \"EngName\": \"Wu You\", \"C...\n",
       "3         328918  {\"PersonId\": \"328918\", \"EngName\": \"Yang Yugao\"...\n",
       "4         395541  {\"PersonId\": \"395541\", \"EngName\": \"Tang Xuzu\",...\n",
       "...          ...                                                ...\n",
       "463354    357829  {\"PersonId\": \"357829\", \"EngName\": \"Li Ru\", \"Ch...\n",
       "463355    442762  {\"PersonId\": \"442762\", \"EngName\": \"Yao Ying\", ...\n",
       "463356    497925  {\"PersonId\": \"497925\", \"EngName\": \"Wang Lanbi\"...\n",
       "463357    389134  {\"PersonId\": \"389134\", \"EngName\": \"Yu Shiling\"...\n",
       "463358     15579  {\"PersonId\": \"15579\", \"EngName\": \"Wang Gongzhi...\n",
       "\n",
       "[463359 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodeinfo_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'PersonId': '449689', 'EngName': 'Liu Jing', 'ChName': '劉競', 'IndexYear': '', 'Gender': '', 'YearBirth': '', 'DynastyBirth': '', 'EraBirth': '', 'EraYearBirth': '', 'YearDeath': '', 'DynastyDeath': '', 'EraDeath': '', 'EraYearDeath': '', 'YearsLived': '', 'Dynasty': '唐', 'JunWang': '', 'Notes': '', 'url': 'https://cbdb.fas.harvard.edu/cbdbapi/person.php?id=449689&o=json'}\n"
     ]
    }
   ],
   "source": [
    "print(json.loads(nodeinfo_df.iloc[0]['Info']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time Span"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    30877.000000\n",
      "mean      1180.076627\n",
      "std        135.731470\n",
      "min        117.000000\n",
      "25%       1095.000000\n",
      "50%       1177.000000\n",
      "75%       1235.000000\n",
      "max       1950.000000\n",
      "Name: Time, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(edge_df['Time'].replace(-1, np.nan).describe())\n",
    "\n",
    "edge_df['Time'].replace(-1, np.nan).hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Negative Link  Ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Edge Num 149385\n",
      "Pos Edge Num 144184\n",
      "Neg Edge Num 4875\n"
     ]
    }
   ],
   "source": [
    "edge_num = len(edge_df)\n",
    "pos_edge_num = len(edge_df[edge_df['Sign'] > 0])\n",
    "neg_edge_num = len(edge_df[edge_df['Sign'] < 0])\n",
    "\n",
    "print('Edge Num', edge_num)\n",
    "print('Pos Edge Num', pos_edge_num)\n",
    "print('Neg Edge Num', neg_edge_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Negative Link Ratio with Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 33 artists>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_span = 10\n",
    "edge_df_time = edge_df['Time'].replace(-1, np.nan).dropna().astype(int)\n",
    "time_max, time_min = edge_df_time.max(), edge_df_time.min()\n",
    "time_min = 960\n",
    "time_max = 1300\n",
    "i = time_min\n",
    "x = []\n",
    "y = []\n",
    "for j in range(time_min + time_span, time_max, time_span):\n",
    "    edge_tmp = edge_df[(edge_df['Time'] < j) & (edge_df['Time'] >= i)]\n",
    "    edge_num = len(edge_tmp)\n",
    "    pos_edge_num = len(edge_tmp[edge_tmp['Sign'] > 0])\n",
    "    neg_edge_num = len(edge_tmp[edge_tmp['Sign'] < 0])\n",
    "    \n",
    "    x.append((i + j)/2)\n",
    "    ratio = -1 if edge_num == 0 else neg_edge_num / edge_num\n",
    "    y.append(  ratio)\n",
    "    i = j\n",
    "x = np.array(x)\n",
    "y = np.array(y)\n",
    "plt.bar(x, y, width=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}