1_FB_EDA_.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=green><p style=\"font-size:32px;text-align:center\"><b>Social Network Graph Link Prediction - Facebook Challenge</b></p></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=blue><h3>Problem Statement:</h3></font>\n",
    "\n",
    "Given a directed social graph, have to predict missing links to recommend users (Link Prediction in graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import pickle\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=gren><h4>Reading Data</h4></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "traincsv = pd.read_csv(\"My Work/train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9437519 entries, 0 to 9437518\n",
      "Data columns (total 2 columns):\n",
      " #   Column            Dtype\n",
      "---  ------            -----\n",
      " 0   source_node       int64\n",
      " 1   destination_node  int64\n",
      "dtypes: int64(2)\n",
      "memory usage: 144.0 MB\n"
     ]
    }
   ],
   "source": [
    "traincsv.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [source_node, destination_node]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "#check null values\n",
    "print(traincsv[traincsv.isnull().any(1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "#check duplicate entries\n",
    "print(sum(traincsv.duplicated()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>source_node</th>\n",
       "      <th>destination_node</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>690569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>315892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>189226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>834328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1615927</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   source_node  destination_node\n",
       "0            1            690569\n",
       "1            1            315892\n",
       "2            1            189226\n",
       "3            2            834328\n",
       "4            2           1615927"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traincsv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#save this train.csv file without header and index\n",
    "traincsv.to_csv(\"My Work/train_wo_header.csv\", header=False, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = nx.read_edgelist(\"My Work/train_wo_header.csv\", delimiter=',', create_using=nx.DiGraph(), nodetype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: \n",
      "Type: DiGraph\n",
      "Number of nodes: 1862220\n",
      "Number of edges: 9437519\n",
      "Average in degree:   5.0679\n",
      "Average out degree:   5.0679\n"
     ]
    }
   ],
   "source": [
    "print(nx.info(g))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=gren><h4>Display Subgraph</h4></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: \n",
      "Type: DiGraph\n",
      "Number of nodes: 66\n",
      "Number of edges: 50\n",
      "Average in degree:   0.7576\n",
      "Average out degree:   0.7576\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if not os.path.isfile('My Work/subgraph_sample.csv'):\n",
    "    pd.read_csv(\"My Work/train.csv\", nrows=50).to_csv(\"My Work/subgraph_sample.csv\", header=False, index=False)\n",
    "\n",
    "subgraph = nx.read_edgelist(\"My Work/subgraph_sample.csv\", delimiter=',', create_using=nx.DiGraph(), nodetype=int)\n",
    "\n",
    "pos = nx.spring_layout(subgraph)\n",
    "nx.draw(subgraph, pos, node_color='#A0CBE2', edge_color='#00bb5e', width=1, edge_cmap=plt.cm.Blues, with_labels=True)\n",
    "print(nx.info(subgraph))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=blue><h3>Exploratory Data Analysis</h3></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NUMBER OF UNIQUE PERSONS - 1862220\n"
     ]
    }
   ],
   "source": [
    "#number of unique persons\n",
    "print(\"NUMBER OF UNIQUE PERSONS -\",len(g.nodes()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=gren><h4>Number of Followers</h4></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#number of followers of each person\n",
    "indegree_dist = list(dict(g.in_degree()).values())\n",
    "indegree_dist.sort()\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(indegree_dist)\n",
    "\n",
    "plt.xlabel(\"INDEX NUMBER\")\n",
    "plt.ylabel(\"NUMBER OF FOLLOWERS\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lets zoom in\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(indegree_dist[0:1500000])\n",
    "\n",
    "plt.xlabel(\"INDEX NUMBER\")\n",
    "plt.ylabel(\"NUMBER OF FOLLOWERS\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#boxplot\n",
    "plt.boxplot(indegree_dist)\n",
    "plt.ylabel(\"NUMBER OF FOLLOWERS\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91 PERCENTILE VALUE - 13.0\n",
      "92 PERCENTILE VALUE - 14.0\n",
      "93 PERCENTILE VALUE - 15.0\n",
      "94 PERCENTILE VALUE - 17.0\n",
      "95 PERCENTILE VALUE - 19.0\n",
      "96 PERCENTILE VALUE - 21.0\n",
      "97 PERCENTILE VALUE - 24.0\n",
      "98 PERCENTILE VALUE - 29.0\n",
      "99 PERCENTILE VALUE - 40.0\n",
      "100 PERCENTILE VALUE - 552.0\n"
     ]
    }
   ],
   "source": [
    "#90 to 100 percentile\n",
    "for i in range(91,101):\n",
    "    print(i,\"PERCENTILE VALUE -\",np.percentile(indegree_dist,i))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "99% of data having 40 followers only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.1 PERCENTILE VALUE - 42.0\n",
      "99.2 PERCENTILE VALUE - 44.0\n",
      "99.3 PERCENTILE VALUE - 47.0\n",
      "99.4 PERCENTILE VALUE - 50.0\n",
      "99.5 PERCENTILE VALUE - 55.0\n",
      "99.6 PERCENTILE VALUE - 61.0\n",
      "99.7 PERCENTILE VALUE - 70.0\n",
      "99.8 PERCENTILE VALUE - 84.0\n",
      "99.9 PERCENTILE VALUE - 112.0\n",
      "100.0 PERCENTILE VALUE - 552.0\n"
     ]
    }
   ],
   "source": [
    "#99 to 100 percentile\n",
    "for i in range(1,11):\n",
    "    print(99+(i/10),\"PERCENTILE VALUE -\",np.percentile(indegree_dist,99+(i/10)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "sns.set_style('ticks')\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(11.7, 8.27)\n",
    "sns.distplot(indegree_dist, color='#16A085')\n",
    "plt.xlabel('PDF OF INDEGREE')\n",
    "sns.despine()\n",
    "\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=gren><h4>Number of Following</h4></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#number of following of each person\n",
    "\n",
    "outdegree_dist = list(dict(g.out_degree()).values())\n",
    "outdegree_dist.sort()\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(outdegree_dist)\n",
    "\n",
    "plt.xlabel(\"INDEX NUMBER\")\n",
    "plt.ylabel(\"NUMBER OF FOLLOWING\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lets zoom in\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(outdegree_dist[0:1500000])\n",
    "\n",
    "plt.xlabel(\"INDEX NUMBER\")\n",
    "plt.ylabel(\"NUMBER OF FOLLOWING\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TUV5eDq1Wy/AglyYaHnv37rVnHURuqa2tDQkJCQPaEhIS0NbW5qCKiEbHFa95/P3vf4dGo0FycjI0Gg0++OADe9VF5BYiIiJQX18/oK2+vh4REREOqohodIiOPN566y28//77WLduHSZOnIivv/4ahYWFMBqNeOihh+xZI5HLys3NRUZGxqBrHgUFBY4ujWhERMOjuroar776Kry9vQEA4eHh2LRpEzIyMhgeRFep/7qGVqtFW1sbIiIiUFBQwOsd5PJEw0Mul1uDo5+fnx/kcrnNiyJyJ2lpaQwLcjtXXIb20sWaAMBgMMBsNtu8KCIicm6i4ZGeno4lS5agtbUVPT09OHz4MJYuXYqHH37YnvUREZETEj1tde+99yIgIAAlJSU4ceIEJkyYAI1GY71fnYiIrl2i4dHR0YFp06Zh2rRp9qyHiIhcgOhpqxUrVli3t27dOuKO+vr6sHz5cqSmpkKtVuPo0aM4duwY0tLSoFarsXbtWlgsFgBAaWkp5s6di9TUVBw8eHDEfRMR0egSHXlcOi1JQ0MDHnvssRF1tH//fly8eBG7du1CQ0MDNm3ahL6+PmRlZSEuLg55eXmoqalBcHAwmpubUV1djZMnT0Kr1TrtsrdERNcq0ZHHaM9hNWnSJJjNZlgsFhgMBnh6eqK1tRWxsbEAgKSkJDQ2NqKlpQUJCQmQyWQIDg6G2WxGd3f3qNZCREQjIzryOHv2LBoaGmCxWHDu3LkBUyxcPlfP1bjuuuvQ3t6Oe++9F2fOnMGWLVvw2WefWUPKz88PPT09MBgMCAwMtL6vvz0oKGjA5+l0Ouh0ugFtnAmYiMg+RMNj8uTJ1oWfIiMj8e6771pfG054bN++HQkJCVi+fDlOnjyJRx55BH19fdbXjUYjAgIC4O/vP+D5EqPRCIVCMejzVCoVVCrVgDa9Xo/k5GTJtRERkTSi4bFhw4ZR7SggIABeXl4AgLFjx+LixYuIjIxEU1MT4uLiUFdXh/j4eISGhqKwsBAZGRno6OiAxWIZNOogIiLHkryS4HDNnz8fra2tUKvVeOSRR7Bs2TLk5eWhpKQEKpUKfX19SElJgVKpRExMDFQqFbRaLfLy8uxVIpFNcCVBckeiI4/R5ufnhxdeeGFQ++uvvz6oTavVQqvV2qMsIpviSoLkrkRHHv/617/sWQeRW7p0JUEvLy/rSoKckp1cnWh4FBYWWrfz8/PtUgyRu+FKguSuRMPj0ocE//vf/9qlGCJ3w5UEyV3Z7SFBomtR/0qCtbW16OvrQ21tLTIyMpCbm+vo0ohGRPSCeWdnJ3Q6HQRBsG73u/z5CiIaGlcSJHclGh6zZ89GV1fXoG0ikoYrCZI7Eg2PJUuWAPhhyo/Tp0/jhhtuGLQsLRERXZtEw+PixYtYv3496urqcMMNN+DUqVO46667sGrVKuuT4kREdG0SvWBeWlqK66+/Hh999BF27dqFf/zjHwgMDBzyQT8iIrq2iIZHU1MTFi9ebN2Xy+VYunQpmpub7VIYERE5L9HwEDs1xVNWREQkGh4+Pj44fvz4gLbjx4/Dx8fH5kURuRNOjEjuSPSC+fLly5GZmYm5c+di4sSJ0Ov1qK6uxrPPPmvP+ohcGidGJHclOvK47bbb8Nprr0EQBDQ0NODChQvYtm0bJk+ebM/6iFwaJ0Ykd3XFKdmvv/56LFy40F61ELkdToxI7spui0ERXYs4MSK5K4YHkQ1xYkRyV1d8wnzv3r0ICAhAfHw8AOD06dPIz8/Hpk2b7FYgkSvjxIjkrkTD48knn4RcLkdXVxeOHDmCkJAQ5Obm4ne/+92wO9u6dSv27t2Lvr4+pKWlITY2FitXroRMJsOtt96KtWvXwsPDA6Wlpdi3bx88PT2Rk5ODqKioYfdJ5GicGJHckWh4HD9+HG+++SZMJhMeeugheHl54bXXXsPPf/7zYXXU1NSEL7/8EpWVlTh//jy2bduGDRs2ICsrC3FxccjLy0NNTQ2Cg4PR3NyM6upqnDx5ElqtFrt37x72FyRytMrKShQUFFhHHrm5uQwTcnmi4eHv7w8A8Pb2hsViwbZt2xAYGDjsjurr6xEeHo7FixfDYDBgxYoVqKqqQmxsLAAgKSkJDQ0NmDRpEhISEiCTyRAcHAyz2Yzu7m4EBQUNu28iR+FzHuSurnirbr/rr79+RMEBAGfOnME333yDLVu2QK/XIzMzE4IgWFcs9PPzQ09PDwwGw4C++tsvDw+dTjdggSrgh+njiZzJpc95ALA+56HVahke5NJEw+PIkSNYvnw5BEGwbvcrKiqS3FFgYCDCwsLg7e2NsLAw+Pj4oKOjw/q60WhEQEAA/P39YTQaB7QrFIpBn6dSqQataKjX65GcnCy5NiJb4XMe5K5Ew+PSO6pSU1NH3NHUqVPx2muvYcGCBTh16hTOnz+PO+64A01NTYiLi0NdXR3i4+MRGhqKwsJCZGRkoKOjAxaLhaesyGX1P+fRP/IA+JwHuYcrXvOIjIwc1P7RRx8Nq6Pp06fjs88+w9y5cyEIAvLy8hASEoI1a9aguLgYYWFhSElJgVwuR0xMDFQqFSwWC/Ly8obVH5Ez6H/O4/JrHpyehFyeIEKj0Vi358+fP2S7szlx4oQQHh4unDhxwtGlEFnt3LlTmDx5suDh4SFMnjxZ2Llzp6NLIhpgOL+doiMPQRCs2xcvXhyynYh+HJ/zIHckOj1J/11QV9omIqJr0xVHHn19fRAEYdA2ERFd20TDo729HTNmzLCGRUpKCgCOPIiI6ArhsXfvXnvWQURELkT0msf333+PV199FR9++CFOnTqFBQsWQKPRoLW11Z71ERGRExINj5UrV+Ls2bNobGxEeno67rvvPixatAj5+fn2rI/I5VVWVkKpVEIul0OpVKKystLRJRGNmOhpq9OnT+PFF18EANx///2YO3cuAODPf/6zfSojcgOcGJHclejIw9Pzf7ly6USFZrPZthURuZFLJ0b08vKyTozIJ8zJ1YmOPDo7O6HT6SAIwoDtU6dO2bM+IpfGiRHJXYmOPGbPno2uri6cPn16wPasWbPsWR+RS+ufGPFSnBiR3IHoyGPJkiX2rIPILXFiRHJXouFx2223YezYsfDy8hr02uV/SRHR0Povimu1WusytAUFBbxYTi5PNDyys7NRV1eHiRMn4v7770dMTIw96yJyG5wYkdyRaHgsWLAACxYswFdffYU9e/agpKQEU6ZMwf3334+wsDB71khERE5G9IJ5v7CwMGRlZWHjxo34+uuv8cADD9ijLiIicmKiIw8AOHv2LN5//328//77AICZM2di3bp19qiLiIicmGh4PProo+jo6MCMGTOQn5+PCRMmjEqH3377LebMmYNt27bB09MTK1euhEwmw6233oq1a9fCw8MDpaWl2LdvHzw9PZGTk4OoqKhR6ZuIiEaHaHgcOXIEAPDGG2/gjTfegEwmgyAIkMlkqKmpGVZnfX19yMvLg6+vLwBgw4YNyMrKQlxcHPLy8lBTU4Pg4GA0NzejuroaJ0+ehFarxe7du4fVHxER2YZdp2TfuHEjUlNT8corrwAAWltbERsbCwBISkpCQ0MDJk2ahISEBMhkMgQHB8NsNqO7uxtBQUGjXg+RPVRWVqKgoMB6q25ubi7vviKXJxoeq1atsm7LZDL4+vril7/8JR544AF4ePzodfZB3nzzTQQFBSExMdEaHv0jGQDw8/NDT08PDAbDgLm0+tsvDw+dTgedTjegzWQySa6LyJYqKyvx+OOPw8/PD4IgwGg04vHHHwfAiRHJtYmGx8yZMwfsf//99/j444/x73//G7m5uZI72r17N2QyGT755BO0tbUhOzsb3d3d1teNRiMCAgLg7+8Po9E4oF2hUAz6PJVKBZVKNaBNr9cjOTlZcm1EtrJixQoYjUacPXsWgiCgvb0dXl5eWLFiBcODXJpoeCQmJg5qS0lJwbx584bV0Y4dO6zbGo0G69atQ2FhIZqamhAXF4e6ujrEx8cjNDQUhYWFyMjIQEdHBywWC09ZkcvS6/UAgJtuugmdnZ0ICgpCZ2cnvv/+ewdXRjQyV7xVdyjDOWUlJjs7G2vWrEFxcTHCwsKQkpICuVyOmJgYqFQqWCwW5OXljVp/RI7g4+ODMWPGQCaTYcyYMfDx8UFvb6+jyyIaEUnh8emnnw4515VUFRUV1u3XX3990OtarRZarXbE/RA5g97eXnz99dcAYP03kasTDY/L1yCQyWSYOHEi/vSnP9m8KCIicm6i4cGZc4mISIxoeLz11luib/rNb35jk2KIiMg1iIbH0aNHB+xbLBb89a9/ha+vL8ODiOgaJxoey5cvt24fO3YMK1euxF133YWcnBy7FEZERM7rR++22rFjB/7yl79g1apVmD59uj1qIiIiJycaHp2dnVi1ahXGjh2L6upqjB071p51ERGRExMNj1mzZsHLywvx8fF4+umnB7xWVFRk88KIiMh5iYbHSy+9ZM86iIjIhYiGR/9U6URERJcbvYmqiIjomsHwICIiyRgeREQkGcODiIgkY3gQEZFkDA8iIpKM4UFERJJJXoZ2uPr6+pCTk4P29naYTCZkZmbilltuwcqVKyGTyXDrrbdi7dq18PDwQGlpKfbt2wdPT0/k5OQgKirKXmUSEdFVsFt47NmzB4GBgSgsLMSZM2fw4IMP4rbbbkNWVhbi4uKQl5eHmpoaBAcHo7m5GdXV1Th58iS0Wi12795trzKJiOgq2C08ZsyYgZSUFOu+XC5Ha2ur9Un2pKQkNDQ0YNKkSUhISIBMJkNwcDDMZjO6u7sRFBRkr1KJiOhH2C08/Pz8AAAGgwFLly5FVlYWNm7cCJlMZn29p6cHBoMBgYGBA97X09MzKDx0Oh10Ot2ANpPJZONvQUREgB3DAwBOnjyJxYsXQ61WY/bs2SgsLLS+ZjQaERAQAH9/fxiNxgHtCoVi0GepVCqoVKoBbXq9HsnJybb7AkREBMCOd1udPn0aCxcuxFNPPYW5c+cCACIjI9HU1AQAqKurQ0xMDKKjo1FfXw+LxYJvvvkGFouFp6yIiJyM3UYeW7ZswXfffYfNmzdj8+bNAIDc3Fzk5+ejuLgYYWFhSElJgVwuR0xMDFQqFSwWC/Ly8uxVIhERXSWZIAiCo4sYLf2nrWpqahASEuLocois1/SG4kb/65GLG85vJx8SJCIiyRgeREQkGcODiIgkY3gQEZFkDA8iIpKM4UFERJIxPIiISDKGBxERScbwICIiyRgeREQkGcODiIgkY3gQEZFkDA8iIpKM4UFERJIxPIiISDKGBxERScbwICIiyey2DK0UFosF69atw3/+8x94e3sjPz8fN998s6PLIiKi/88pRx4fffQRTCYTdDodli9fjmeeecbRJRER0SWcMjxaWlqQmJgIAPjVr36FQ4cOObgiosGUSiVkMtkV/7mSH3uvTCaDUqm007chksYpT1sZDAb4+/tb9+VyOS5evAhPz/+Vq9PpoNPpBrzPZDLZrUZyL4f+4A/ljXJp75kLYG6AbQqyOgGsG3vVRx86ZYZys8GG9RD9wCnDw9/fH0aj0bpvsVgGBAcAqFQqqFSqAW16vR7Jycl2qZHci61+cK80+hAEYdT74ziF7MUpT1tFR0ejrq4OAPDPf/4T4eHhDq6IaHjEAsIWwUFkT0458rjnnnvQ0NCA1NRUCIKA9evXO7okomFjUJA7csrw8PDwwNNPP+3oMoiISIRTnrYiIiLnxvAgIiLJGB5ERCQZw4OIiCRzygvmw2U2mwEAHR0dDq6EiMh19P9m9v+GXg23Co+uri4AQHp6uoMrISJyPV1dXVc9Ca1McKOb0C9cuIBDhw5h/PjxkMulTTVBZGuLFi3Cli1bHF0G0SBmsxldXV1QKpXw9fW9qve41cjD19cXMTExji6DaEje3t4ICQlxdBlEQ5K67AUvmBMRkWQMDyIikozhQUREkjE8iOzk8iUEiFyZW91tRURE9sGRBxERScbwICIiyRgeRHZy4MABaDQaR5dBNCrc6iFBImdVVlaGPXv2YMyYMY4uhWhUcORBZAehoaEoKSlxdBlEo4bhQWQHKSkp8PTkQJ/cB8ODiIgkY3gQEZFkDA8iIpKMT5iqoA12AAAAN0lEQVQTEZFkHHkQEZFkDA8iIpKM4UFERJIxPIiISDKGBxERScbwICIiyRgeREQkGcODiIgk+39/APEDwpcLfwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#boxplot\n",
    "plt.boxplot(outdegree_dist)\n",
    "plt.ylabel(\"NUMBER OF FOLLOWING\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91 PERCENTILE VALUE - 13.0\n",
      "92 PERCENTILE VALUE - 14.0\n",
      "93 PERCENTILE VALUE - 15.0\n",
      "94 PERCENTILE VALUE - 17.0\n",
      "95 PERCENTILE VALUE - 19.0\n",
      "96 PERCENTILE VALUE - 21.0\n",
      "97 PERCENTILE VALUE - 24.0\n",
      "98 PERCENTILE VALUE - 29.0\n",
      "99 PERCENTILE VALUE - 40.0\n",
      "100 PERCENTILE VALUE - 1566.0\n"
     ]
    }
   ],
   "source": [
    "#90 to 100 percentile\n",
    "for i in range(91,101):\n",
    "    print(i,\"PERCENTILE VALUE -\",np.percentile(outdegree_dist,i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.1 PERCENTILE VALUE - 42.0\n",
      "99.2 PERCENTILE VALUE - 45.0\n",
      "99.3 PERCENTILE VALUE - 48.0\n",
      "99.4 PERCENTILE VALUE - 52.0\n",
      "99.5 PERCENTILE VALUE - 56.0\n",
      "99.6 PERCENTILE VALUE - 63.0\n",
      "99.7 PERCENTILE VALUE - 73.0\n",
      "99.8 PERCENTILE VALUE - 90.0\n",
      "99.9 PERCENTILE VALUE - 123.0\n",
      "100.0 PERCENTILE VALUE - 1566.0\n"
     ]
    }
   ],
   "source": [
    "#99 to 100 percentile\n",
    "for i in range(1,11):\n",
    "    print(99+(i/10),\"PERCENTILE VALUE -\",np.percentile(outdegree_dist,99+(i/10)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('ticks')\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(11.7, 8.27)\n",
    "sns.distplot(outdegree_dist, color='#16A085')\n",
    "plt.xlabel('PDF OF OUTDEGREE')\n",
    "sns.despine()\n",
    "\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NUMBER OF PERSONS THOSE ARE NOT FOLLOWING ANYONE - 274512 AND % IS 14.74\n"
     ]
    }
   ],
   "source": [
    "not_following = sum(np.array(outdegree_dist)==0)\n",
    "per_not_following = (not_following*100)/len(outdegree_dist)\n",
    "\n",
    "print(\"NUMBER OF PERSONS THOSE ARE NOT FOLLOWING ANYONE - {} AND % IS {}\".format(not_following, round(per_not_following,2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NUMBER OF PERSONS HAVING ZERO FOLLOWERS ARE 188043 AND % IS 10.1\n"
     ]
    }
   ],
   "source": [
    "zero_follower = sum(np.array(indegree_dist)==0)\n",
    "per_zero_follower = (zero_follower*100)/len(indegree_dist)\n",
    "\n",
    "print(\"NUMBER OF PERSONS HAVING ZERO FOLLOWERS ARE {} AND % IS {}\".format(zero_follower, round(per_zero_follower,2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NUMBER OF PERSONS THOSE ARE NOT FOLLOWING ANYONE AND ALSO NOT HAVING ANY FOLLOWERS ARE 0\n"
     ]
    }
   ],
   "source": [
    "count=0\n",
    "for i in g.nodes():\n",
    "    if len(list(g.predecessors(i)))==0:\n",
    "        if len(list(g.successors(i)))==0:\n",
    "            count+=1\n",
    "            \n",
    "print(\"NUMBER OF PERSONS THOSE ARE NOT FOLLOWING ANYONE AND ALSO NOT HAVING ANY FOLLOWERS ARE\",count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=gren><h4>Number of Followers + Number of Following</h4></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "dict_in = dict(g.in_degree())\n",
    "dict_out = dict(g.out_degree())\n",
    "\n",
    "d = Counter(dict_in) + Counter(dict_out)\n",
    "in_out_degree = np.array(list(d.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "in_out_degree_sort = sorted(in_out_degree)\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(in_out_degree_sort)\n",
    "\n",
    "plt.xlabel(\"INDEX NUMBER\")\n",
    "plt.ylabel(\"FOLLOWING + FOLLOWERS\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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G7yEVhDLLQJaZmXne62PHjg1aMQAAoG5GRrEksohiGchuv/32815ftmxZsGoBAAD14DacZRlp/I7X69atC0YdAACgngy/sow4/MoSAIAwU3OWpdNVIJAspyw5sxIAgNBkDLseRBrLQNavXz/FxMScMyLGHwAAAJxVM2WJSGIZyDZu3BjQhiorKzVp0iTt379fsbGxeuyxx3TxxRcHtA0AAKIBU5aRp9Y1ZH/+85+Vl5enrKws5eXl6Y033mhwQ2+//baqqqr0hz/8QSNHjtQTTzzR4M8CACCaGcPGsJHGcoTs5Zdf1uuvv67CwkKlp6fr888/15w5c1RWVqaf/vSnfjfUsWNHVVdXy+12q7S0VI0aWTYNAABqYWRYQhRhLFPR2rVrtXTpUiUkJEiSOnXqpCeeeEIPPPBAgwJZkyZNtH//ft188806duyYFi1a1PCqAQAIEy9sL9Zj6z4J6C4FpeVVantp44B9HpxnGcji4uK8YcwjOTlZcXFxDWpo2bJl6tWrl8aNG6cDBw7o3nvv1auvvqrExETvPS6XSy6Xy+d9/NoTABDOdu7/WqcqqpX7w4yAfm7/K9oF9PPgLMtA5na7VVZWpuTkZO+10tJSVVdXN6ihZs2aKT4+XpKUmpqqqqqqcz4rJydHOTk5PteKi4uVlZXVoDYBAHCaMUaNE+JUeMvlTpeCEGa5qH/o0KEaNWqUdu3apRMnTujTTz/V6NGjdffddzeoofvuu0+7du1Sbm6u7r33Xv3yl79UkyZNGlw4AADhgF9Eoj4sR8huvvlmNWvWTAsXLtS//vUvtW3bVnl5ebrhhhsa1FBycrKefPLJBhcKAEA4MkaKJZGhDpaB7ODBg7ruuut03XXX2VkPAAARxc0mrqgHyynLCRMmeB8vXrzYlmIAAIg0TFmiPiwD2dk/z92yZYstxQAAEGlq/jolkaF2loGMDecAAAgEo1j+SkUdLNeQHT9+XFu2bJHb7dbXX3+tzZs3e1/r1auXLcUBABDu3G6mLFE3y0B2+eWXa926dZKkzp0767XXXvO+RiADAKB+jIximLJEHSwD2cyZM+2sAwCAiGQMI2Som+UaMgAAcOGMWNKPuhHIAAAIopoRMiIZamcZyP72t7/ZWQcAABHJGMOUJepkGcjmzJnjfVxUVGRLMQAARBo2hkV91Gtj2M8++8yWYgAAiDTG8CtL1I2NYQEACCIjsTEs6mS57cWhQ4fkcrlkjPE+9sjJybGlOAAAwp2bRf2oB8tANnDgQB05cuScxwAAoP5qpiyB2lkGslGjRkmSKioqVFJSotatWyshIcG2wgAAiAScLY76sAxkVVVVmjFjhjZt2qTWrVvr8OHDuv766zV58mTFx8fbWSMAAOHLSLFMWaIOloHsqaeeUqtWrfTWW29Jkqqrq/X000/rySef1COPPGJbgQAA2OHE6UodP1kZ8M8tq6higAx1sgxk27Zt0+rVq73P4+LiNHr0aA0ePNiWwgAAsEu126j3438JSiCTpC4dmgXlcxE5LAOZ1bQk05UAgEhT5Xbr+MlK3dylrW689KKAf/4VaakB/0xEFstAlpiYqC+++EIZGRnea1988YUSExNtKQwAALt49kK/Ii1Vd3ZPd7YYRCXLQDZu3Dg99NBDGjRokNLT01VcXKy1a9fq8ccft7M+AACCzhPI2FEfTrHcqf/SSy/V8uXLZYzRli1bdPr0aS1ZskSXX365nfUBABB0pmZzCs6chGMsR8gkqVWrVho2bJhdtQAA4IgzI2SAMyxHyAAAiBbf5jH2C4NjCGQAgKjnNkxZwlmWgayqqkpvvvmmtm7d6r1WUlKiMWPG2FIYAAB28UxZAk6xXEP2yCOPKC4uTkeOHNGePXuUlpamKVOm6J577rGzPgAAgs+zhowhMjjEMpB98cUXeumll1RRUaGf/vSnio+P1/Lly3XxxRfbWR8AAEHn+ZVlLHkMDrEMZCkpKZKkhIQEud1uLVmyRM2bN7etMAAA7OLmV5ZwWL0W9bdq1YowBgCIWMa7qJ9IBmdYjpDt2bNH48aNkzHG+9hj3rx5thQHAIAdzmx74WgZiGKWgeyJJ57wPh4yZIgtxQAA4ATvrywZIYNDal1D1rlz53Ouv/XWW0EtCAAAu3mnLB2uA9HLcg3ZrFmzvI/vv/9+7+Ply5cHtyIAAGzGABmcZhnIzFm75FVVVZ33OgAAkcDzVxtHJ8EploHs7F+aWD0GACASuJmyhMMs15AZY1RZWSljzDmPAQCIJExZwmmWgWz//v3q16+fN4BlZ2dLurARssWLF2vjxo2qrKzUXXfdpTvvvLPBnwUAQKCcWdRPIoMzLAPZxo0bA9rQtm3b9NFHH2n16tU6deqUlixZEtDPBwCgoYz3LEtn60D0sgxkJ0+elMvlUocOHXTVVVdp4sSJqqqq0qRJk3T55Zf73dDmzZvVqVMnjRw5UqWlpZowYcIFFQ4AiEyfHvxGn5ectLXNktJySayThnMsA9mkSZPUsWNH7du3T3PmzNHw4cPVrl07FRUVafXq1X43dOzYMX355ZdatGiRiouL9dBDD+mNN97w+cPvcrnkcrl83ldRUeF3WwCA8DX0uW36qsyZf/c3bxzvSLuAZSArKSnRggULJEm33HKLBg0aJEn67W9/26CGmjdvrszMTCUkJCgzM1OJiYk6evSoWrVq5b0nJydHOTk5Pu8rLi5WVlZWg9oEAISfkxXVuqNrBz3YK9PWdhMaxeriNsm2tgl4WAayRo3OvHT2weLV1dUNaqhbt25avny57r//fh0+fFinTp3iwHIAwDmMjNqkJKpz+2ZOlwLYxjKQHTp0SC6XS8YYn8eHDx9uUEM33HCD/vrXv2rQoEEyxmjatGmKi4trcOEAgMjkNmJDMEQdy0A2cOBAHTly5JzHAwYMaHBjLOQHANTJsP0Eoo9lIBs1apSddQAAIKlmyjKWPIYoYxnILr30UqWmpio+/txfnGzevDmoRQEAopfbsB8Yoo9lIJs4caI2bdqk9PR03XLLLerevbuddQEAopQxhilLRB3LQHb//ffr/vvv1//+7//qlVde0cKFC3X11VfrlltuUWamvT9FBgBEDyNGyBB9Yuu6ITMzU2PGjNHs2bP1+eef69Zbb7WjLgBAlDKGHfMRfSxHyCTp+PHjev311/X6669Lkvr376/CwkI76gIARKEzh3wD0cUykP3sZz/TwYMH1a9fPxUVFalt27Z21gUAiEIc8o1oZRnI9uzZI0l64YUX9MILLygmJqZmoWVMjDZs2GBbgQCA6PFtHmNRP6KOZSDbuHGjnXUAAOCdsmQfMkQby0A2efJk7+OYmBglJSXpiiuu0K233qrY2Dp/CwAAgN/cTFkiSlkGsv79+/s8P3nypN555x198sknmjJlStALAwBEH/PtpCW/skS0sQxkvXv3Pudadna2Bg8eHNSCAADRi0X9iFZ+zz0yXQkACBZvIGNRP6KMX+lq69at5z3bEgCAQDgzZelwIYDNLKcse/Xq5fM8JiZG6enpeuyxx4JeFAAgOp0ZIQOii2Ug27x5s511AADg3YcsliEyRBnLQPbyyy9bvum2224LSjEAgOjmNkxZIjpZBrK9e/f6PHe73frjH/+opKQkAhkARLBNnx3RB58fdaTt8iq3I+0CTrMMZOPGjfM+3rdvnyZNmqTrr79e+fn5thQGAHDG9Nd26++HTjg2SpUQF6vMNsnONA44xDKQeaxatUq///3vNXnyZN1www121AQAcFCV262fXNlOT+d2dboUIGpYBrJDhw5p8uTJSk1N1dq1a5WammpnXQAAhxjDrxwBu1kGsgEDBig+Pl49evTQf/7nf/q8Nm/evKAXBgBwhhFHFwF2swxkTz/9tJ11AABChDFGseQxwFaWgezaa6+1sw4AQIhwM2UJ2I6DKQEAPowMU5aAzQhkAAAfLOoH7EcgAwD4MIZF/YDdCGQAAB/GGI4uAmxGIAMA+DBiyhKwG4EMAOCjZsrS6SqA6EIgAwD4MDKKJZEBtiKQAQB8uBkhA2xHIAMA+DBGYhUZYC8CGQDgOzg6CbAbgQwA4IMpS8B+BDIAgA9jjGKYsgRsRSADAPgwYoQMsBuBDADgwxix7QVgM9sD2VdffaW+fftq7969djcNAKgHd83PLAHYyNZAVllZqWnTpikpKcnOZgEA/mBRP2C7RnY2Nnv2bA0ZMkTPPvusnc0CgG1e2F6s1/92wOkyLsjJymoW9QM2sy2QvfTSS2rZsqV69+5tGchcLpdcLpfPtYqKCjvKA4CA+MP7X2j3gW/UsU2y06U02OXtm6n3/2vtdBlAVLEtkL344ouKiYnRe++9p927d2vixIl65pln1KZNG+89OTk5ysnJ8XlfcXGxsrKy7CoTAC6I2xhdndFCKx/8odOlAAgjtgWyVatWeR/n5eWpsLDQJ4wBQCRgywgADcG2FwAQQMZIMSQyAH6ydVG/x4oVK5xoFgCCrmaXewDwDyNkABBATFkCaAgCGQAEkDFihAyA3whkABBARoZjhwD4jUAGAAHkdjNlCcB/BDIACKCaUyBJZAD8QyADgAAyxjBCBsBvBDIACLBYAhkAPxHIACCA3MZwMDcAvxHIACCAanbqd7oKAOGGQAYAAWQktr0A4DcCGQAEkGFnWAANQCADgAAijwFoCAIZAARQzVmWRDIA/iGQAUAAGWPY9gKA3whkABBAbqYsATQAgQwAAsjIMGUJwG8EMgAIIBb1A2iIRk4XAAAjn/9Q2z8/5nQZAXH4xGlGyAD4jUAGwHGb/1GiNk0T1S2jhdOlBMTga9KcLgFAmCGQAXCcMUbXXdxKv7q1i9OlAIAjWEMGwHHs3QUg2hHIADiPA7kBRDkCGQDHuY1RDL9NBBDFCGQAHFczZel0FQDgHAIZAMcZI44bAhDVCGQAHMfu9gCiHYEMgOPY3R5AtCOQAXCcMSKRAYhqBDIAjjMyimXKEkAUI5ABcBxTlgCiHYEMgOPY9gJAtCOQAXAcG8MCiHYEMgCOYx8yANGOQAYgNDBnCSCKEcgAOMoYI4lF/QCiG4EMgKO+zWNsewEgqhHIADjK7RkhI48BiGKN7GqosrJS+fn52r9/vyoqKvTQQw8pKyvLruYBhKhvB8iYsgQQ1WwLZK+88oqaN2+uOXPm6NixY7r99tsJZAC8U5aMkAGIZrYFsn79+ik7O9v7PC4uzq6mESVOnK5URZXb6TLgp8pqz5QliQxA9LItkCUnJ0uSSktLNXr0aI0ZM8auphEFPvnyGw1Y+I7cpu57EZoS4ljSCiB62RbIJOnAgQMaOXKkcnNzNXDgwHNed7lccrlcPtcqKirsKg9h7PCJ03Ib6T96d1R6yyZOlwM/xcXG6CdXtHO6DABwjG2BrKSkRMOGDdO0adPUs2fP896Tk5OjnJwcn2vFxcWsNUOdPANj/a9op6szWjhaCwAA/rJtjmDRokX65ptv9Jvf/EZ5eXnKy8vT6dOn7Woekc67MJx1SACA8GPbCNnUqVM1depUu5pDlHGz2zsAIIyxihYRga0TAADhjECGiOBZQ8bxOwCAcEQgQ0TwTFkCABCOCGSICExZAgDCGYEMEcKzqJ9EBgAIPwQyRATPCFksf6IBAGGIv74QETxHJjFCBgAIRwQyRATjmbIkjwEAwhCBDBHBu6jf2TIAAGgQAhkigmfTC45OAgCEIwIZIoIxTFkCAMIXgQwRgSlLAEA4I5AhIngW9XN0EgAgHBHIEBHc7pr/JY8BAMIRgQwRwbuon0lLAEAYIpAhIrCoHwAQzghkiAhntr1wtAwAABqkkdMFhIJqt9H7/zyqimq306Wggf5+8IQk9iEDAIQnApmkDbsP6WcrtjtdBi5QbIyUnBDndBkAAPiNQCbpps7f07qf91J5FSNk4axlcoKaN0lwugwAAPxGIFPNNFeXDqlOlwEAAKIUi/oBAAAcRiADAABwGIEMAADAYQQyAAAAhxHIAAAAHEYgAwAAcBiBDAAAwGEEMgAAAIcRyAAAABxGIAMAAHAYgQwAAMBhIX+WZXV1tSTp4MGDDlcCAABQO09e8eSX+gr5QHbkyBFJ0tChQx2uBAAAoH6OHDmi73//+/W+P8YYY4JYzwUpJaKhAAALuUlEQVQ7ffq0du7cqTZt2iguLi5o7YwYMUKLFi0K2ueHG/rDF/1xBn3hi/44g77wRX/4ipb+qK6u1pEjR9SlSxclJSXV+30hP0KWlJSk7t27B72dhIQEpaWlBb2dcEF/+KI/zqAvfNEfZ9AXvugPX9HUH/6MjHmwqB8AAMBhBDIAAACHEcgAAAAcFldYWFjodBGhokuXLk6XEFLoD1/0xxn0hS/64wz6whf94Yv+sBbyv7IEAACIdExZAgAAOIxABgAA4LCoD2Rut1vTpk1TTk6O8vLytG/fPqdLCojKykqNHz9eubm5GjRokDZs2KB9+/bprrvuUm5urh599FG53W5J0lNPPaVBgwZpyJAh+vjjjyUpIPeGmq+++kp9+/bV3r17o74vFi9erJycHN1xxx1au3ZtVPdHZWWlxo0bpyFDhig3Nzdq/3zs2LFDeXl5kgLznS70Xqed3R+7d+9Wbm6u8vLy9MADD6ikpESStGbNGt1xxx0aPHiw/vKXv0iSjh49qmHDhik3N1djxozRqVOnAnKv087uD49XX31VOTk53ufR1B9BYaLcn//8ZzNx4kRjjDEfffSRGTFihMMVBcYLL7xgioqKjDHGHD161PTt29cMHz7cbN261RhjTEFBgXnzzTfNzp07TV5ennG73Wb//v3mjjvuMMaYC7431FRUVJiHH37Y/PjHPzZ79uyJ6r7YunWrGT58uKmurjalpaVmwYIFUd0f//Vf/2VGjx5tjDFm8+bNZtSoUVHXH88++6wZMGCAufPOO40xF/6dAnGvk77bH0OHDjWffPKJMcaY1atXmxkzZpjDhw+bAQMGmPLycvPNN994Hz/22GPmxRdfNMYYs3jxYrN06dKA3Ouk7/aHMcZ88skn5p577vFei6b+CJaoHyHbvn27evfuLUm66qqrtHPnTocrCox+/frpF7/4hfd5XFycdu3apWuvvVaS1KdPH7377rvavn27evXqpZiYGLVv317V1dU6evToBd8bambPnq0hQ4booosukqSo7ovNmzerU6dOGjlypEaMGKHrr78+qvujY8eOqq6ultvtVmlpqRo1ahR1/ZGRkaGFCxd6n9v5/a3uddJ3+2P+/Pm67LLLJNUci5OYmKiPP/5YV199tRISEtS0aVNlZGTo008/9fk7xfMdA3Gvk77bH8eOHdPcuXOVn5/vvRZN/REsUR/ISktLlZKS4n0eFxenqqoqBysKjOTkZKWkpKi0tFSjR4/WmDFjZIxRTEyM9/UTJ06c8/091y/03lDy0ksvqWXLlt5/0CVFbV9INf8y3blzp5588kn96le/0iOPPBLV/dGkSRPt379fN998swoKCpSXlxd1/ZGdna1Gjc6cpGfn97e610nf7Q/Pf8h9+OGHWrlype677z6VlpaqadOm3nuSk5NVWlrqc/3s73ih9zrp7P6orq7WlClTlJ+fr+TkZO890dQfwRLyZ1kGW0pKisrKyrzP3W63zz+I4ezAgQMaOXKkcnNzNXDgQM2ZM8f7WllZmZo1a3bO9y8rK1PTpk0VGxt7QfeGkhdffFExMTF67733tHv3bk2cONHnv8CjqS8kqXnz5srMzFRCQoIyMzOVmJiogwcPel+Ptv5YtmyZevXqpXHjxunAgQO69957VVlZ6X092vpD0gV/p0DcG2rWr1+vZ555Rs8++6xatmxpWbfnelJSUp3f0Z97Q8WuXbu0b98+FRYWqry8XHv27NH06dPVo0ePqOyPQIr6EbKuXbtq06ZNkqT/+Z//UadOnRyuKDBKSko0bNgwjR8/XoMGDZIkde7cWdu2bZMkbdq0Sd27d1fXrl21efNmud1uffnll3K73WrZsuUF3xtKVq1apZUrV2rFihW67LLLNHv2bPXp0ycq+0KSunXrpnfeeUfGGB06dEinTp1Sz549o7Y/mjVr5v0XfGpqqqqqqqL2nxUPO7+/1b2h5E9/+pP33yHp6emSpCuvvFLbt29XeXm5Tpw4ob1796pTp07q2rWr3n77bUk137Fbt24BuTdUXHnllXrttde0YsUKzZ8/X5dccommTJkStf0RSFG/Mazb7VZhYaE+++wzGWM0Y8YMXXzxxU6XdcGKior0+uuvKzMz03ttypQpKioqUmVlpTIzM1VUVKS4uDgtXLhQmzZtktvt1uTJk9W9e3f985//VEFBwQXdG4ry8vJUWFio2NjYC/5+4dwXjz/+uLZt2yZjjH75y18qLS0tavujrKxM+fn5OnLkiCorK3XPPfeoS5cuUdcfxcXFGjt2rNasWROQ73Sh9zrN0x+rV69Wz5491a5dO++I5jXXXKPRo0drzZo1crlcMsZo+PDhys7OVklJiSZOnKiysjK1aNFC8+bNU5MmTS74Xqed/efD6lo09UcwRH0gAwAAcFrUT1kCAAA4jUAGAADgMAIZAACAwwhkAAAADiOQAQAAOCwydkAFENLO/nn8pEmTVFpaqqeeesr7+nXXXactW7bopZde0oIFC5Seni63262YmBiNHDnSu0/amDFjdMkll3jf16JFCy1YsEDjx49Xx44d9fDDD0uS1q5dq61bt2revHnee7dt26aRI0fq1VdfVbt27SRJc+fOVWZmpu644w5vDR6bNm3S+vXrNWvWLN14443KzMzUb3/7W+/rS5cu1axZs/T3v/9dCxcu1Lp163TRRRepqqpKrVq10qxZs5SSkqIbb7xR7dq189kU1fPT/rO/T1lZmdLS0jR37lwlJCQE+P8BAKGOQAbAdtu3b9fLL7+s22677ZzXBgwYoEceeURSzQbHQ4cO1cqVKyVJPXr00K9//etz3vPoo49q0KBB6tGjhxITE/X8889r1apV59wXHx+vyZMna+nSpd4jfOrr0KFDOnr0qHfT0rffflupqane1++77z7dddddkmrOPnS5XHrggQckSUuWLFFiYqLP523btu2c7zNu3Dht3LhR/fr186s2AOGPKUsAths3bpwWLlzoc2TT+bRu3VrZ2dn67//+71rvS0lJ0eOPP66CggJNnTpVc+bMUZMmTc65r0ePHkpNTT1vWKtLdna23njjDUnS3r17lZGRofj4+PPe+/XXX6tDhw5+fX5FRYUOHz7sE/IARA9GyADY7qKLLtIvfvELTZkyRb/73e9qvbdVq1Y6duyYMjIytHXrVuXl5Xlf69u3rx588EFJNUe6pKamKikpyWda87sKCwt15513qlevXnXWefYo2oABA1RQUKDc3Fy98sorGjhwoDZs2OB9fdmyZVq/fr2OHz+ukydPeqdPJWnYsGHeKcvY2Fj9/ve/lyTv9/nqq68UGxurwYMHq2fPnnXWBSDyEMgAOOKWW27RW2+9peeff77W+7788kt17txZkvWUpSQtX75cHTp00NGjR+VyuZSTk3Pe+1q0aKH8/HxNmjRJXbt29V7/7hTmyZMnfaYZPevODhw4oA8//FBjxozxuf/sKcu1a9dq4sSJWrZsmaTzT1me/X2OHTumYcOGKS0trbauABDBmLIE4JjCwkItWbJEZWVl53398OHD2rBhg/r27Vvr53z88cdyuVx69NFHNXPmTC1atEj/+Mc/LO+/8cYb1bFjR/3xj3/0XktLS9N7773nff7OO+/oiiuu8Hlf//79NWvWLF199dW1rkFr3769Kisra635bC1atNCcOXM0depUHT58uN7vAxA5GCED4JiWLVtq0qRJGjlypPfaunXrtGPHDsXGxsoYo5kzZ6p58+aSdM6UpVTzS8nx48dr3rx5SklJUUpKigoKCjR27FitXbtWSUlJ5217ypQp2rp1q/d5UVGRfvWrX+nXv/613G63rrrqKt16660+7+nXr5+mT5+ul19++ZzP80xZxsXF6fTp08rPz/e+dvaUpSTdc8893oOqPS655BLl5eWpqKhICxYsqKvrAEQYDhcHAABwGFOWAAAADiOQAQAAOIxABgAA4DACGQAAgMMIZAAAAA4jkAEAADiMQAYAAOAwAhkAAIDD/g8DfNS/oxRkHgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lets zoom in\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(in_out_degree_sort[0:1500000])\n",
    "\n",
    "plt.xlabel(\"INDEX NUMBER\")\n",
    "plt.ylabel(\"NUMBER OF FOLLOWING\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#boxplot\n",
    "plt.boxplot(in_out_degree_sort)\n",
    "plt.ylabel(\"NUMBER OF FOLLOWING\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91 PERCENTILE VALUE - 26.0\n",
      "92 PERCENTILE VALUE - 28.0\n",
      "93 PERCENTILE VALUE - 31.0\n",
      "94 PERCENTILE VALUE - 33.0\n",
      "95 PERCENTILE VALUE - 37.0\n",
      "96 PERCENTILE VALUE - 41.0\n",
      "97 PERCENTILE VALUE - 48.0\n",
      "98 PERCENTILE VALUE - 58.0\n",
      "99 PERCENTILE VALUE - 79.0\n",
      "100 PERCENTILE VALUE - 1579.0\n"
     ]
    }
   ],
   "source": [
    "#90 to 100 percentile\n",
    "for i in range(91,101):\n",
    "    print(i,\"PERCENTILE VALUE -\",np.percentile(in_out_degree_sort,i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.1 PERCENTILE VALUE - 83.0\n",
      "99.2 PERCENTILE VALUE - 87.0\n",
      "99.3 PERCENTILE VALUE - 93.0\n",
      "99.4 PERCENTILE VALUE - 99.0\n",
      "99.5 PERCENTILE VALUE - 108.0\n",
      "99.6 PERCENTILE VALUE - 120.0\n",
      "99.7 PERCENTILE VALUE - 138.0\n",
      "99.8 PERCENTILE VALUE - 168.0\n",
      "99.9 PERCENTILE VALUE - 221.0\n",
      "100.0 PERCENTILE VALUE - 1579.0\n"
     ]
    }
   ],
   "source": [
    "#99 to 100 percentile\n",
    "for i in range(1,11):\n",
    "    print(99+(i/10),\"PERCENTILE VALUE -\",np.percentile(in_out_degree_sort,99+(i/10)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MINIMUM NUMBER OF FOLLOWING + FOLLOWERS - 1\n",
      "----------------------------------------------------------------------------------------------------\n",
      "334291 HAVING MINIMUM NUMBER OF FOLLOWING + FOLLOWERS\n"
     ]
    }
   ],
   "source": [
    "print(\"MINIMUM NUMBER OF FOLLOWING + FOLLOWERS -\",min(in_out_degree))\n",
    "print(\"-\"*100)\n",
    "print(sum(in_out_degree==in_out_degree.min()),\"HAVING MINIMUM NUMBER OF FOLLOWING + FOLLOWERS\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAXIMUM NUMBER OF FOLLOWING + FOLLOWERS - 1579\n",
      "----------------------------------------------------------------------------------------------------\n",
      "1 HAVING MAXIMUM NUMBER OF FOLLOWING + FOLLOWERS\n"
     ]
    }
   ],
   "source": [
    "print(\"MAXIMUM NUMBER OF FOLLOWING + FOLLOWERS -\",max(in_out_degree))\n",
    "print(\"-\"*100)\n",
    "print(sum(in_out_degree==in_out_degree.max()),\"HAVING MAXIMUM NUMBER OF FOLLOWING + FOLLOWERS\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1320326 PERSONS HAVING NUMBER OF FOLLOWING + FOLLOWERS LESS THAN 10\n"
     ]
    }
   ],
   "source": [
    "print(sum(in_out_degree<10),\"PERSONS HAVING NUMBER OF FOLLOWING + FOLLOWERS LESS THAN 10\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NUMBER OF WEAKLY CONNECTED COMPONENTS - 45558\n",
      "NUMBER OF WEAKLY CONNECTED COMPONENTS WITH 2 NODES - 32195\n"
     ]
    }
   ],
   "source": [
    "print(\"NUMBER OF WEAKLY CONNECTED COMPONENTS -\",len(list(nx.weakly_connected_components(g))))\n",
    "\n",
    "count = 0\n",
    "for i in list(nx.weakly_connected_components(g)):\n",
    "    if len(i)==2:\n",
    "        count+=1\n",
    "print(\"NUMBER OF WEAKLY CONNECTED COMPONENTS WITH 2 NODES -\",count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=blue><h3>Posing a Problem as a Classification Problem</h3></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<font color=black>Generating some edges which are not present in graph for supervised learning.</font>**\n",
    "\n",
    "- Generated Bad links from graph which are not in graph and whose shortest path is greater than 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import csv\n",
    "import random\n",
    "#generating bad edges from given graph\n",
    "if not os.path.isfile(\"My Work/after_eda/missing_edges_final.p\"):\n",
    "    #getting all set of edges\n",
    "    r = csv.reader(open(\"My Work/train_wo_header.csv\", 'r'))\n",
    "    edges = dict()\n",
    "\n",
    "    for edge in r:\n",
    "        edges[(edge[0], edge[1])] = 1\n",
    "\n",
    "    missing_edges = set([])\n",
    "    while(len(missing_edges) < 9437519):\n",
    "        a = random.randint(1, 1862220)\n",
    "        b = random.randint(1, 1862220)\n",
    "        temp = edges.get((a,b),-1)\n",
    "    \n",
    "        if temp==-1 and a!=b:\n",
    "            try:\n",
    "                if nx.shortest_path_length(g,source=a,target=b) > 2:\n",
    "                    missing_edges.add((a,b))\n",
    "                else:\n",
    "                    continue\n",
    "            except:\n",
    "                missing_edges.add((a,b))\n",
    "        else:\n",
    "            pickle.dump(missing_edges, open(\"My Work/after_eda/missing_edges_final.p\", 'wb'))\n",
    "else:\n",
    "    missing_edges = pickle.load(open(\"My Work/after_eda/missing_edges_final.p\",'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9437519"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(missing_edges)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=gren><h4>Train and Test Data Split</h4></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NUMBER OF NODES IN THE GRAPH WITH EDGES - 9437519\n",
      "NUMBER OF NODES IN THE GRAPH WITHOUT EDGES - 9437519\n",
      "----------------------------------------------------------------------------------------------------\n",
      "NUMBER OF NODES IN TRAIN DATA GRAPH WITH EDGES - 7550015\n",
      "NUMBER OF NODES IN TRAIN DATA GRAPH WITHOUT EDGES - 7550015\n",
      "----------------------------------------------------------------------------------------------------\n",
      "NUMBER OF NODES IN TEST DATA GRAPH WITH EDGES - 1887504\n",
      "NUMBER OF NODES IN TEST DATA GRAPH WITHOUT EDGES - 1887504\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "if (not os.path.isfile('My Work/after_eda/train_pos_after_eda.csv')) and (not os.path.isfile('My Work/after_eda/test_pos_after_eda.csv')):\n",
    "    #reading_total_data\n",
    "    data_pos = pd.read_csv(\"My Work/train.csv\")\n",
    "    data_neg = pd.DataFrame(list(missing_edges), columns=['source_node', 'destination_node'])\n",
    "    \n",
    "    print(\"NUMBER OF NODES IN THE GRAPH WITH EDGES -\",data_pos.shape[0])\n",
    "    print(\"NUMBER OF NODES IN THE GRAPH WITHOUT EDGES -\",data_neg.shape[0])\n",
    "    \n",
    "    #train_test_split_(_80_:_20_)\n",
    "    #positive links and negative links seperatly because we need positive training data only for creating graph\n",
    "    #and for feature generation\n",
    "    X_train_pos, X_test_pos, y_train_pos, y_test_pos  = train_test_split(data_pos, np.ones(len(data_pos)), test_size=0.2, random_state=9)\n",
    "    X_train_neg, X_test_neg, y_train_neg, y_test_neg  = train_test_split(data_neg, np.zeros(len(data_neg)), test_size=0.2, random_state=9)\n",
    "    \n",
    "    print(\"-\"*100)\n",
    "    print(\"NUMBER OF NODES IN TRAIN DATA GRAPH WITH EDGES -\",X_train_pos.shape[0])\n",
    "    print(\"NUMBER OF NODES IN TRAIN DATA GRAPH WITHOUT EDGES -\",X_train_neg.shape[0])\n",
    "    print(\"-\"*100)\n",
    "    print(\"NUMBER OF NODES IN TEST DATA GRAPH WITH EDGES -\",X_test_pos.shape[0])\n",
    "    print(\"NUMBER OF NODES IN TEST DATA GRAPH WITHOUT EDGES -\",X_test_neg.shape[0])\n",
    "\n",
    "    #removing header and saving\n",
    "    X_train_pos.to_csv('My Work/after_eda/train_pos_after_eda.csv', header=False, index=False)\n",
    "    X_train_neg.to_csv('My Work/after_eda/train_neg_after_eda.csv', header=False, index=False)\n",
    "    X_test_pos.to_csv('My Work/after_eda/test_pos_after_eda.csv', header=False, index=False)\n",
    "    X_test_neg.to_csv('My Work/after_eda/test_neg_after_eda.csv', header=False, index=False)\n",
    "else:\n",
    "    del missing_edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: \n",
      "Type: DiGraph\n",
      "Number of nodes: 1780722\n",
      "Number of edges: 7550015\n",
      "Average in degree:   4.2399\n",
      "Average out degree:   4.2399\n",
      "Name: \n",
      "Type: DiGraph\n",
      "Number of nodes: 1144623\n",
      "Number of edges: 1887504\n",
      "Average in degree:   1.6490\n",
      "Average out degree:   1.6490\n",
      "NUMBER OF COMMON PEOPLE IN TRAIN AND TEST - 1063125\n",
      "NUMBER OF PEOPLE PRESENT IN TRAIN BUT NOT PRESENT IN TEST - 717597\n",
      "NUMBER OF PEOPLE PRESENT IN TEST BUT NOT PRESENT IN TRAIN - 81498\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "if (not os.path.isfile('My Work/after_eda/train_pos_after_eda.csv')) and (not os.path.isfile('My Work/after_eda/test_pos_after_eda.csv')):\n",
    "    train_graph = nx.read_edgelist(\"D:/Applied_Ai/Case Studies/Facebook/My Work/after_eda/train_pos_after_eda.csv\", delimiter=',', create_using=nx.DiGraph(), nodetype=int)\n",
    "    test_graph = nx.read_edgelist(\"D:/Applied_Ai/Case Studies/Facebook/My Work/after_eda/test_pos_after_eda.csv\", delimiter=',', create_using=nx.DiGraph(), nodetype=int)\n",
    "\n",
    "    print(nx.info(train_graph))\n",
    "    print(nx.info(test_graph))\n",
    "\n",
    "    #finding the unique nodes in the both train and test graphs\n",
    "    train_node_pos = set(train_graph.nodes())\n",
    "    test_node_pos = set(test_graph.nodes())\n",
    "\n",
    "    common_tr_te = len(train_node_pos.intersection(test_node_pos)) #common_in_train_and_test\n",
    "    trY_teN = len(train_node_pos - test_node_pos) #present in train, but not in test\n",
    "    trN_teY = len(test_node_pos - train_node_pos) #present in test, but not in train\n",
    "\n",
    "    print(\"NUMBER OF COMMON PEOPLE IN TRAIN AND TEST -\",common_tr_te)\n",
    "    print(\"NUMBER OF PEOPLE PRESENT IN TRAIN BUT NOT PRESENT IN TEST -\",trY_teN)\n",
    "    print(\"NUMBER OF PEOPLE PRESENT IN TEST BUT NOT PRESENT IN TRAIN -\",trN_teY)\n",
    "    print(\"-\"*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.12% PEOPLE ARE NOT THERE IN TRAIN BUT EXIST IN TEST DATA\n"
     ]
    }
   ],
   "source": [
    "print(\"{}% PEOPLE ARE NOT THERE IN TRAIN BUT EXIST IN TEST DATA\".format(round((trN_teY*100)/len(test_node_pos),2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=red>We have a cold start problem here.</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.\n",
      "NUMBER OF NODES IN TRAIN DATA GRAPH WITH EDGES - 7550015\n",
      "NUMBER OF NODES IN TRAIN DATA GRAPH WITHOUT EDGES - 7550015\n",
      "-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.\n",
      "NUMBER OF NODES IN TEST DATA GRAPH WITH EDGES - 1887504\n",
      "NUMBER OF NODES IN TEST DATA GRAPH WITHOUT EDGES - 1887504\n"
     ]
    }
   ],
   "source": [
    "#final train and test dataset\n",
    "if (not os.path.isfile('My Work/after_eda/train_after_eda.csv')) and \\\n",
    "(not os.path.isfile('My Work/after_eda/test_after_eda.csv')) and \\\n",
    "(not os.path.isfile('My Work/train_y.csv')) and \\\n",
    "(not os.path.isfile('My Work/test_y.csv')) and \\\n",
    "(os.path.isfile('My Work/after_eda/train_pos_after_eda.csv')) and \\\n",
    "(os.path.isfile('My Work/after_eda/test_pos_after_eda.csv')) and \\\n",
    "(os.path.isfile('My Work/after_eda/train_neg_after_eda.csv')) and \\\n",
    "(os.path.isfile('My Work/after_eda/test_neg_after_eda.csv')):\n",
    "    \n",
    "    X_train_pos = pd.read_csv(\"My Work/after_eda/train_pos_after_eda.csv\", names=['source_node', 'destination_node'])\n",
    "    X_test_pos = pd.read_csv(\"My Work/after_eda/test_pos_after_eda.csv\", names=['source_node', 'destination_node'])\n",
    "    X_train_neg = pd.read_csv(\"My Work/after_eda/train_neg_after_eda.csv\", names=['source_node', 'destination_node'])\n",
    "    X_test_neg = pd.read_csv(\"My Work/after_eda/test_neg_after_eda.csv\", names=['source_node', 'destination_node'])\n",
    "\n",
    "    print(\"-.-.\"*20)\n",
    "    print(\"NUMBER OF NODES IN TRAIN DATA GRAPH WITH EDGES -\",X_train_pos.shape[0])\n",
    "    print(\"NUMBER OF NODES IN TRAIN DATA GRAPH WITHOUT EDGES -\",X_train_neg.shape[0])\n",
    "    print(\"-.-.\"*20)\n",
    "    print(\"NUMBER OF NODES IN TEST DATA GRAPH WITH EDGES -\",X_test_pos.shape[0])\n",
    "    print(\"NUMBER OF NODES IN TEST DATA GRAPH WITHOUT EDGES -\",X_test_neg.shape[0])\n",
    "\n",
    "    X_train = X_train_pos.append(X_train_neg, ignore_index=True)\n",
    "    y_train = np.concatenate((y_train_pos, y_train_neg))\n",
    "\n",
    "    X_test = X_test_pos.append(X_test_neg, ignore_index=True)\n",
    "    y_test = np.concatenate((y_test_pos, y_test_neg))\n",
    "    #------\n",
    "    X_train.to_csv(\"My Work/after_eda/train_after_eda.csv\", header=False, index=False)\n",
    "    X_test.to_csv(\"My Work/after_eda/test_after_eda.csv\", header=False, index=False)\n",
    "\n",
    "    pd.DataFrame(y_train.astype(int)).to_csv(\"My Work/train_y.csv\", header=False, index=False)\n",
    "    pd.DataFrame(y_test.astype(int)).to_csv(\"My Work/test_y.csv\", header=False, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DATA POINTS IN TRAIN DATA - (15100030, 2)\n",
      "DATA POINTS IN TEST DATA - (3775008, 2)\n",
      "-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.\n",
      "SHAPE OF TARGET VARIABLE IN TRAIN - (15100030,)\n",
      "SHAPE OF TARGET VARIABLE IN TEST - (3775008,)\n"
     ]
    }
   ],
   "source": [
    "print(\"DATA POINTS IN TRAIN DATA -\",X_train.shape)\n",
    "print(\"DATA POINTS IN TEST DATA -\",X_test.shape)\n",
    "print(\"-.-.\"*20)\n",
    "print(\"SHAPE OF TARGET VARIABLE IN TRAIN -\",y_train.shape)\n",
    "print(\"SHAPE OF TARGET VARIABLE IN TEST -\",y_test.shape)"
   ]
  }
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