cheating_contagion.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modules\n",
    "import get_data as gd \n",
    "import cheat_contagion as cc\n",
    "import alt_universe as au\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kills data overview:\n",
      " ['040d4435-9603-4640-b50e-7a3b3077392f', 'account.ba877183bdfd407ab6db337eca604eb9', 'account.f917b3772bf94def84958e6042c7b6ae', datetime.date(2019, 3, 10), datetime.time(0, 24, 33, 868)]\n",
      "Cheaters data overview:\n",
      " {'account.4c2ff8d26c594ca88e78a3ae1afd9bde': [datetime.date(2019, 3, 1), datetime.date(2019, 3, 3)], 'account.e0d28cb829634b81bb61ee434bee1dcc': [datetime.date(2019, 3, 5), datetime.date(2019, 3, 9)]}\n",
      "Number of killings: 516880\n",
      "Number of cheaters: 2295\n"
     ]
    }
   ],
   "source": [
    "# Stage 1. Get data\n",
    "killings = gd.get_killings_data('data/kills.txt', 3, 4) \n",
    "cheaters = gd.get_cheaters_data('data/cheaters.txt', 1, 2)\n",
    "\n",
    "# Data overview\n",
    "print('Kills data overview:\\n', killings[0])\n",
    "print('Cheaters data overview:\\n', dict(list(cheaters.items())[0:2]))\n",
    "print('Number of killings:', len(killings))\n",
    "print('Number of cheaters:', len(cheaters.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of times cheating behavior was observed: 70981\n",
      "Number of observer-cheater motifs: 731\n"
     ]
    }
   ],
   "source": [
    "# Stage 2. Identify observer cheater motifs\n",
    "# As said in docstrings, functions assume that within matches, killings data are sorted by the sequence of killings, \n",
    "# i.e. by time, and that it is organized by match, i.e. observations from the same match are adjacent.\n",
    "\n",
    "# Stage 2.1 Observants of cheaters under both assumptions\n",
    "possible_motifs = cc.observed_cheating(killings, cheaters)\n",
    "print('Number of times cheating behavior was observed:', len(possible_motifs)) \n",
    "\n",
    "# Stage 2.2 Observer-cheater motifs\n",
    "cheater_motifs = cc.obs_cheater_motifs(possible_motifs)\n",
    "print('Number of observer-cheater motifs:', len(cheater_motifs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 file(s) saved!\n",
      "Number of observer-cheater motifs by universe: [731, 729, 747, 717, 723, 745, 744, 739, 735, 744]\n"
     ]
    }
   ],
   "source": [
    "# Stage 3. Generate alternative universes\n",
    "n_randomizations = 10\n",
    "\n",
    "# 3.1 Generate data\n",
    "# Execute randomizations and save files\n",
    "au.randomize_save('data/kills_random_', killings, cheaters, n_randomizations) \n",
    "\n",
    "# Load all universes in a single list\n",
    "all_univ = au.load_data_onelist('data/kills_random_', n_randomizations) \n",
    "\n",
    "# 3.2 List of observer-cheater motifs for each randomization\n",
    "possible_motifs_r10 = [cc.observed_cheating(all_univ[i], cheaters) for i in range(n_randomizations)]\n",
    "cheater_motifs_r10 = [cc.obs_cheater_motifs(possible_motifs_r10[i]) for i in range(n_randomizations)]\n",
    "\n",
    "# 3.3 Observer-cheater motif count by the original data and for each randomization\n",
    "final_data_ran = [(len(cheater_motifs_r10[i])) for i in range(n_randomizations)]\n",
    "print('Number of observer-cheater motifs by universe:', final_data_ran)\n",
    "\n",
    "final_data = len(cheater_motifs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graph\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.hist(final_data_ran, color = 'c', label = 'Alternative universes')  \n",
    "plt.axvline(x = final_data, color = 'r', \n",
    "            label = 'Original data')\n",
    "\n",
    "plt.xlabel('Number of observer-cheater motifs')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Contagion of cheating behavior')\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.0, 1), loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Brief analysis of results\n",
    "The results from this exercise indicate that the expected hypothesis cannot be assured. If we take these results as correct, exposure to possible cheating behavior does not uniquely incite cheating. An underlying mechanism, which could also explain the increase of observer-cheater motifs when randomizing, is that people who have tendencies to cheat, sooner or later start cheating. \n"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}