3_compare_with_ground_truth.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "from collections import defaultdict\n",
    "import random\n",
    "import copy\n",
    "import math\n",
    "from scipy.optimize import linprog\n",
    "import time\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams.update({'font.size': 14})\n",
    "# mpl.rc('text', usetex = False)\n",
    "mpl.rc('font', family = 'serif')\n",
    "import networkx as nx\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Load input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "lines = 500000\n",
    "random.seed(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load episode set $E$ with the users that retweeted each original tweet in the trace. \n",
    "\n",
    "Each episode $E_{s}$ includes the users that retweeted s, ordered chronologically, as they appear in the trace. The first user in each episode is the user that originally tweeted the tweet, and is denoted by $r_{s}$. Subsequent users in $E_{s}$ are users that retweeted s, either directly from user $r_{s}$ or from another user that appears in Es before them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "D = pickle.load(open(\"./Extracted/D\"+ str(lines) + \".p\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "E = pickle.load(open(\"./Extracted/E\"+ str(lines) + \".p\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "for tweet in E:\n",
    "    E[tweet] = list(dict.fromkeys(E[tweet]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the set of original tweets denoted by $S$. \n",
    "\n",
    "The set of original tweets is denoted by $S$, where $S$ = S is the total number of original tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "S = pickle.load(open(\"./Extracted/S\"+ str(lines) + \".p\", \"rb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load $U$ set xith unique users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "U = pickle.load(open(\"./Extracted/U\"+ str(lines) + \".p\", \"rb\"))\n",
    "U = list(U)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load $M_{ij}$ variables that count number of episodes where the ordered pair (i,j) appears"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "M = pickle.load(open(\"./Extracted/M_d\"+ str(lines) + \".p\", \"rb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load $Q_{ij}$ results for the ordered pair (i,j) derived from the consrained algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q = pickle.load(open(\"./Extracted/AlgorithmResults/Q_constrained_\"+ str(lines) + \".p\", \"rb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load $s_{ij}$ derived from the consrained algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pickle.load(open(\"./Extracted/AlgorithmResults/s_constrained_\"+ str(lines) + \".p\", \"rb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Create ground truth Graph\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File exists\n"
     ]
    }
   ],
   "source": [
    "# Read back\n",
    "fullfollowers = {}\n",
    "if os.path.isfile(\"fullfollowers.txt\"):\n",
    "    # File exists\n",
    "    print('File exists')\n",
    "else:\n",
    "    print('No file')\n",
    "            \n",
    "with open(\"./fullfollowers.txt\") as f2:\n",
    "        for line in f2:\n",
    "            line = (line.strip().split(' ', 1))\n",
    "            key = line[0]\n",
    "            values = line[1]\n",
    "            values = values.strip('][').split(', ') \n",
    "            fullfollowers[int(key)] = values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4134426622"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(4134426622)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q_gt = defaultdict(dict)\n",
    "for i in fullfollowers:\n",
    "    for j in fullfollowers[i]:\n",
    "        if j!=\"''\":\n",
    "            j = j.strip(\"''''\")\n",
    "            j = int(j)\n",
    "            Q_gt[j][i] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101988 28437405\n"
     ]
    }
   ],
   "source": [
    "s1=0\n",
    "s2=0\n",
    "for i in s:\n",
    "    for j in s[i]:\n",
    "        if s[i][j] >= 0.5:\n",
    "            s1 +=1\n",
    "        else:\n",
    "            s2+=1\n",
    "print(s1,s2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "True\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "for i in s:\n",
    "    for j in s[i]:\n",
    "        if s[i][j] > 0.5:\n",
    "            # does i follow j? i should be in followers of j\n",
    "            flag = False\n",
    "            followers = []\n",
    "            if j in fullfollowers:\n",
    "                for follower in fullfollowers[j]:\n",
    "                    if follower!=\"''\":\n",
    "                        follower = follower.strip(\"''''\")\n",
    "                        follower = int(follower)\n",
    "                        if follower == i: \n",
    "                            flag=True\n",
    "                print(flag)\n",
    "                        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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