Bonus 1 Data Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "centered-aside",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "from matplotlib import pyplot as plt\n",
    "import re\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "premium-definition",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./sample_data/hyper_advanced.txt',\n",
       " './sample_data/bonus_1.txt',\n",
       " './sample_data/advanced.txt',\n",
       " './sample_data/basic.txt']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the sample data from ./sample_data/\n",
    "data_dir = \"./sample_data/\"\n",
    "data_files = [join(data_dir, f) for f in listdir(data_dir) if isfile(join(data_dir, f))]\n",
    "data_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "weird-bennett",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['hyper_advanced',\n",
       "  'BOMB_COUNT = 0\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 0, score = 0\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 0, score = 0\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 0, score = 0\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 0, score = 0\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 0, score = 0\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 0, score = 0\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 0, score = 0\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 0, score = 0\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 0, score = 0\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 0, score = 0\\nBOMB_COUNT = 40\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 40, score = 40\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 40, score = 40\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 40, score = 38\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 40, score = 40\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 40, score = 40\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 40, score = 40\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 40, score = 40\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 40, score = 40\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 40, score = 40\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 40, score = 40\\nBOMB_COUNT = 80\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 80, score = 79\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 80, score = 78\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 80, score = 77\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 80, score = 78\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 80, score = 79\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 80, score = 78\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 80, score = 78\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 80, score = 79\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 80, score = 79\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 80, score = 80\\nBOMB_COUNT = 120\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 120, score = 100\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 120, score = 104\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 120, score = 103\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 120, score = 112\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 120, score = 99\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 120, score = 110\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 120, score = 107\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 120, score = 112\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 120, score = 104\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 120, score = 103\\nBOMB_COUNT = 160\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 160, score = 125\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 160, score = 116\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 160, score = 120\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 160, score = 118\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 160, score = 118\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 160, score = 127\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 160, score = 125\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 160, score = 128\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 160, score = 108\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 160, score = 112\\nBOMB_COUNT = 200\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 200, score = 125\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 200, score = 142\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 200, score = 123\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 200, score = 132\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 200, score = 115\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 200, score = 133\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 200, score = 138\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 200, score = 132\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 200, score = 130\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 200, score = 118\\nBOMB_COUNT = 240\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 240, score = 119\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 240, score = 112\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 240, score = 106\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 240, score = 109\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 240, score = 132\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 240, score = 118\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 240, score = 133\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 240, score = 132\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 240, score = 141\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 240, score = 120\\nBOMB_COUNT = 280\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 280, score = 137\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 280, score = 125\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 280, score = 122\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 280, score = 134\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 280, score = 123\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 280, score = 113\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 280, score = 124\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 280, score = 136\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 280, score = 151\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 280, score = 127\\nBOMB_COUNT = 320\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 320, score = 114\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 320, score = 127\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 320, score = 122\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 320, score = 150\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 320, score = 97\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 320, score = 86\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 320, score = 136\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 320, score = 132\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 320, score = 114\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 320, score = 106\\nBOMB_COUNT = 360\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 360, score = 94\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 360, score = 93\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 360, score = 90\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 360, score = 80\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 360, score = 75\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 360, score = 68\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 360, score = 91\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 360, score = 82\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 360, score = 92\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 360, score = 70\\nBOMB_COUNT = 400\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 400, score = 0\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 400, score = 0\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 400, score = 0\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 400, score = 0\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 400, score = 0\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 400, score = 0\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 400, score = 0\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 400, score = 0\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 400, score = 0\\n\\tagent = hyper_advanced, dim = 20, bomb_count = 400, score = 0\\n'],\n",
       " ['bonus_1',\n",
       "  'BOMB_COUNT = 0\\n\\tagent = bonus_1, dim = 20, bomb_count = 0, score = 0\\n\\tagent = bonus_1, dim = 20, bomb_count = 0, score = 0\\n\\tagent = bonus_1, dim = 20, bomb_count = 0, score = 0\\n\\tagent = bonus_1, dim = 20, bomb_count = 0, score = 0\\n\\tagent = bonus_1, dim = 20, bomb_count = 0, score = 0\\n\\tagent = bonus_1, dim = 20, bomb_count = 0, score = 0\\n\\tagent = bonus_1, dim = 20, bomb_count = 0, score = 0\\n\\tagent = bonus_1, dim = 20, bomb_count = 0, score = 0\\n\\tagent = bonus_1, dim = 20, bomb_count = 0, score = 0\\n\\tagent = bonus_1, dim = 20, bomb_count = 0, score = 0\\nBOMB_COUNT = 40\\n\\tagent = bonus_1, dim = 20, bomb_count = 40, score = 40\\n\\tagent = bonus_1, dim = 20, bomb_count = 40, score = 40\\n\\tagent = bonus_1, dim = 20, bomb_count = 40, score = 40\\n\\tagent = bonus_1, dim = 20, bomb_count = 40, score = 39\\n\\tagent = bonus_1, dim = 20, bomb_count = 40, score = 40\\n\\tagent = bonus_1, dim = 20, bomb_count = 40, score = 40\\n\\tagent = bonus_1, dim = 20, bomb_count = 40, score = 40\\n\\tagent = bonus_1, dim = 20, bomb_count = 40, score = 40\\n\\tagent = bonus_1, dim = 20, bomb_count = 40, score = 40\\n\\tagent = bonus_1, dim = 20, bomb_count = 40, score = 40\\nBOMB_COUNT = 80\\n\\tagent = bonus_1, dim = 20, bomb_count = 80, score = 77\\n\\tagent = bonus_1, dim = 20, bomb_count = 80, score = 78\\n\\tagent = bonus_1, dim = 20, bomb_count = 80, score = 77\\n\\tagent = bonus_1, dim = 20, bomb_count = 80, score = 74\\n\\tagent = bonus_1, dim = 20, bomb_count = 80, score = 78\\n\\tagent = bonus_1, dim = 20, bomb_count = 80, score = 80\\n\\tagent = bonus_1, dim = 20, bomb_count = 80, score = 79\\n\\tagent = bonus_1, dim = 20, bomb_count = 80, score = 80\\n\\tagent = bonus_1, dim = 20, bomb_count = 80, score = 74\\n\\tagent = bonus_1, dim = 20, bomb_count = 80, score = 80\\nBOMB_COUNT = 120\\n\\tagent = bonus_1, dim = 20, bomb_count = 120, score = 105\\n\\tagent = bonus_1, dim = 20, bomb_count = 120, score = 100\\n\\tagent = bonus_1, dim = 20, bomb_count = 120, score = 103\\n\\tagent = bonus_1, dim = 20, bomb_count = 120, score = 99\\n\\tagent = bonus_1, dim = 20, bomb_count = 120, score = 111\\n\\tagent = bonus_1, dim = 20, bomb_count = 120, score = 102\\n\\tagent = bonus_1, dim = 20, bomb_count = 120, score = 111\\n\\tagent = bonus_1, dim = 20, bomb_count = 120, score = 92\\n\\tagent = bonus_1, dim = 20, bomb_count = 120, score = 109\\n\\tagent = bonus_1, dim = 20, bomb_count = 120, score = 107\\nBOMB_COUNT = 160\\n\\tagent = bonus_1, dim = 20, bomb_count = 160, score = 113\\n\\tagent = bonus_1, dim = 20, bomb_count = 160, score = 119\\n\\tagent = bonus_1, dim = 20, bomb_count = 160, score = 121\\n\\tagent = bonus_1, dim = 20, bomb_count = 160, score = 112\\n\\tagent = bonus_1, dim = 20, bomb_count = 160, score = 113\\n\\tagent = bonus_1, dim = 20, bomb_count = 160, score = 112\\n\\tagent = bonus_1, dim = 20, bomb_count = 160, score = 108\\n\\tagent = bonus_1, dim = 20, bomb_count = 160, score = 108\\n\\tagent = bonus_1, dim = 20, bomb_count = 160, score = 124\\n\\tagent = bonus_1, dim = 20, bomb_count = 160, score = 115\\nBOMB_COUNT = 200\\n\\tagent = bonus_1, dim = 20, bomb_count = 200, score = 121\\n\\tagent = bonus_1, dim = 20, bomb_count = 200, score = 124\\n\\tagent = bonus_1, dim = 20, bomb_count = 200, score = 117\\n\\tagent = bonus_1, dim = 20, bomb_count = 200, score = 109\\n\\tagent = bonus_1, dim = 20, bomb_count = 200, score = 124\\n\\tagent = bonus_1, dim = 20, bomb_count = 200, score = 133\\n\\tagent = bonus_1, dim = 20, bomb_count = 200, score = 124\\n\\tagent = bonus_1, dim = 20, bomb_count = 200, score = 146\\n\\tagent = bonus_1, dim = 20, bomb_count = 200, score = 130\\n\\tagent = bonus_1, dim = 20, bomb_count = 200, score = 106\\nBOMB_COUNT = 240\\n\\tagent = bonus_1, dim = 20, bomb_count = 240, score = 122\\n\\tagent = bonus_1, dim = 20, bomb_count = 240, score = 119\\n\\tagent = bonus_1, dim = 20, bomb_count = 240, score = 111\\n\\tagent = bonus_1, dim = 20, bomb_count = 240, score = 135\\n\\tagent = bonus_1, dim = 20, bomb_count = 240, score = 120\\n\\tagent = bonus_1, dim = 20, bomb_count = 240, score = 117\\n\\tagent = bonus_1, dim = 20, bomb_count = 240, score = 128\\n\\tagent = bonus_1, dim = 20, bomb_count = 240, score = 118\\n\\tagent = bonus_1, dim = 20, bomb_count = 240, score = 128\\n\\tagent = bonus_1, dim = 20, bomb_count = 240, score = 125\\nBOMB_COUNT = 280\\n\\tagent = bonus_1, dim = 20, bomb_count = 280, score = 121\\n\\tagent = bonus_1, dim = 20, bomb_count = 280, score = 113\\n\\tagent = bonus_1, dim = 20, bomb_count = 280, score = 124\\n\\tagent = bonus_1, dim = 20, bomb_count = 280, score = 123\\n\\tagent = bonus_1, dim = 20, bomb_count = 280, score = 156\\n\\tagent = bonus_1, dim = 20, bomb_count = 280, score = 148\\n\\tagent = bonus_1, dim = 20, bomb_count = 280, score = 120\\n\\tagent = bonus_1, dim = 20, bomb_count = 280, score = 143\\n\\tagent = bonus_1, dim = 20, bomb_count = 280, score = 116\\n\\tagent = bonus_1, dim = 20, bomb_count = 280, score = 132\\nBOMB_COUNT = 320\\n\\tagent = bonus_1, dim = 20, bomb_count = 320, score = 120\\n\\tagent = bonus_1, dim = 20, bomb_count = 320, score = 122\\n\\tagent = bonus_1, dim = 20, bomb_count = 320, score = 144\\n\\tagent = bonus_1, dim = 20, bomb_count = 320, score = 116\\n\\tagent = bonus_1, dim = 20, bomb_count = 320, score = 151\\n\\tagent = bonus_1, dim = 20, bomb_count = 320, score = 118\\n\\tagent = bonus_1, dim = 20, bomb_count = 320, score = 132\\n\\tagent = bonus_1, dim = 20, bomb_count = 320, score = 121\\n\\tagent = bonus_1, dim = 20, bomb_count = 320, score = 105\\n\\tagent = bonus_1, dim = 20, bomb_count = 320, score = 133\\nBOMB_COUNT = 360\\n\\tagent = bonus_1, dim = 20, bomb_count = 360, score = 110\\n\\tagent = bonus_1, dim = 20, bomb_count = 360, score = 92\\n\\tagent = bonus_1, dim = 20, bomb_count = 360, score = 127\\n\\tagent = bonus_1, dim = 20, bomb_count = 360, score = 102\\n\\tagent = bonus_1, dim = 20, bomb_count = 360, score = 89\\n\\tagent = bonus_1, dim = 20, bomb_count = 360, score = 65\\n\\tagent = bonus_1, dim = 20, bomb_count = 360, score = 127\\n\\tagent = bonus_1, dim = 20, bomb_count = 360, score = 83\\n\\tagent = bonus_1, dim = 20, bomb_count = 360, score = 84\\n\\tagent = bonus_1, dim = 20, bomb_count = 360, score = 86\\nBOMB_COUNT = 400\\n\\tagent = bonus_1, dim = 20, bomb_count = 400, score = 399\\n\\tagent = bonus_1, dim = 20, bomb_count = 400, score = 399\\n\\tagent = bonus_1, dim = 20, bomb_count = 400, score = 399\\n\\tagent = bonus_1, dim = 20, bomb_count = 400, score = 399\\n\\tagent = bonus_1, dim = 20, bomb_count = 400, score = 399\\n\\tagent = bonus_1, dim = 20, bomb_count = 400, score = 399\\n\\tagent = bonus_1, dim = 20, bomb_count = 400, score = 399\\n\\tagent = bonus_1, dim = 20, bomb_count = 400, score = 399\\n\\tagent = bonus_1, dim = 20, bomb_count = 400, score = 399\\n\\tagent = bonus_1, dim = 20, bomb_count = 400, score = 399\\n'],\n",
       " ['advanced',\n",
       "  'BOMB_COUNT = 0\\n\\tagent = advanced, dim = 20, bomb_count = 0, score = 0\\n\\tagent = advanced, dim = 20, bomb_count = 0, score = 0\\n\\tagent = advanced, dim = 20, bomb_count = 0, score = 0\\n\\tagent = advanced, dim = 20, bomb_count = 0, score = 0\\n\\tagent = advanced, dim = 20, bomb_count = 0, score = 0\\n\\tagent = advanced, dim = 20, bomb_count = 0, score = 0\\n\\tagent = advanced, dim = 20, bomb_count = 0, score = 0\\n\\tagent = advanced, dim = 20, bomb_count = 0, score = 0\\n\\tagent = advanced, dim = 20, bomb_count = 0, score = 0\\n\\tagent = advanced, dim = 20, bomb_count = 0, score = 0\\nBOMB_COUNT = 40\\n\\tagent = advanced, dim = 20, bomb_count = 40, score = 39\\n\\tagent = advanced, dim = 20, bomb_count = 40, score = 40\\n\\tagent = advanced, dim = 20, bomb_count = 40, score = 40\\n\\tagent = advanced, dim = 20, bomb_count = 40, score = 40\\n\\tagent = advanced, dim = 20, bomb_count = 40, score = 40\\n\\tagent = advanced, dim = 20, bomb_count = 40, score = 39\\n\\tagent = advanced, dim = 20, bomb_count = 40, score = 38\\n\\tagent = advanced, dim = 20, bomb_count = 40, score = 40\\n\\tagent = advanced, dim = 20, bomb_count = 40, score = 40\\n\\tagent = advanced, dim = 20, bomb_count = 40, score = 40\\nBOMB_COUNT = 80\\n\\tagent = advanced, dim = 20, bomb_count = 80, score = 77\\n\\tagent = advanced, dim = 20, bomb_count = 80, score = 76\\n\\tagent = advanced, dim = 20, bomb_count = 80, score = 76\\n\\tagent = advanced, dim = 20, bomb_count = 80, score = 78\\n\\tagent = advanced, dim = 20, bomb_count = 80, score = 80\\n\\tagent = advanced, dim = 20, bomb_count = 80, score = 79\\n\\tagent = advanced, dim = 20, bomb_count = 80, score = 77\\n\\tagent = advanced, dim = 20, bomb_count = 80, score = 78\\n\\tagent = advanced, dim = 20, bomb_count = 80, score = 76\\n\\tagent = advanced, dim = 20, bomb_count = 80, score = 78\\nBOMB_COUNT = 120\\n\\tagent = advanced, dim = 20, bomb_count = 120, score = 110\\n\\tagent = advanced, dim = 20, bomb_count = 120, score = 111\\n\\tagent = advanced, dim = 20, bomb_count = 120, score = 102\\n\\tagent = advanced, dim = 20, bomb_count = 120, score = 107\\n\\tagent = advanced, dim = 20, bomb_count = 120, score = 105\\n\\tagent = advanced, dim = 20, bomb_count = 120, score = 106\\n\\tagent = advanced, dim = 20, bomb_count = 120, score = 98\\n\\tagent = advanced, dim = 20, bomb_count = 120, score = 99\\n\\tagent = advanced, dim = 20, bomb_count = 120, score = 97\\n\\tagent = advanced, dim = 20, bomb_count = 120, score = 109\\nBOMB_COUNT = 160\\n\\tagent = advanced, dim = 20, bomb_count = 160, score = 115\\n\\tagent = advanced, dim = 20, bomb_count = 160, score = 110\\n\\tagent = advanced, dim = 20, bomb_count = 160, score = 116\\n\\tagent = advanced, dim = 20, bomb_count = 160, score = 124\\n\\tagent = advanced, dim = 20, bomb_count = 160, score = 109\\n\\tagent = advanced, dim = 20, bomb_count = 160, score = 120\\n\\tagent = advanced, dim = 20, bomb_count = 160, score = 128\\n\\tagent = advanced, dim = 20, bomb_count = 160, score = 117\\n\\tagent = advanced, dim = 20, bomb_count = 160, score = 124\\n\\tagent = advanced, dim = 20, bomb_count = 160, score = 116\\nBOMB_COUNT = 200\\n\\tagent = advanced, dim = 20, bomb_count = 200, score = 128\\n\\tagent = advanced, dim = 20, bomb_count = 200, score = 145\\n\\tagent = advanced, dim = 20, bomb_count = 200, score = 116\\n\\tagent = advanced, dim = 20, bomb_count = 200, score = 114\\n\\tagent = advanced, dim = 20, bomb_count = 200, score = 120\\n\\tagent = advanced, dim = 20, bomb_count = 200, score = 120\\n\\tagent = advanced, dim = 20, bomb_count = 200, score = 135\\n\\tagent = advanced, dim = 20, bomb_count = 200, score = 122\\n\\tagent = advanced, dim = 20, bomb_count = 200, score = 118\\n\\tagent = advanced, dim = 20, bomb_count = 200, score = 119\\nBOMB_COUNT = 240\\n\\tagent = advanced, dim = 20, bomb_count = 240, score = 141\\n\\tagent = advanced, dim = 20, bomb_count = 240, score = 119\\n\\tagent = advanced, dim = 20, bomb_count = 240, score = 123\\n\\tagent = advanced, dim = 20, bomb_count = 240, score = 130\\n\\tagent = advanced, dim = 20, bomb_count = 240, score = 133\\n\\tagent = advanced, dim = 20, bomb_count = 240, score = 111\\n\\tagent = advanced, dim = 20, bomb_count = 240, score = 106\\n\\tagent = advanced, dim = 20, bomb_count = 240, score = 128\\n\\tagent = advanced, dim = 20, bomb_count = 240, score = 130\\n\\tagent = advanced, dim = 20, bomb_count = 240, score = 128\\nBOMB_COUNT = 280\\n\\tagent = advanced, dim = 20, bomb_count = 280, score = 140\\n\\tagent = advanced, dim = 20, bomb_count = 280, score = 136\\n\\tagent = advanced, dim = 20, bomb_count = 280, score = 136\\n\\tagent = advanced, dim = 20, bomb_count = 280, score = 141\\n\\tagent = advanced, dim = 20, bomb_count = 280, score = 131\\n\\tagent = advanced, dim = 20, bomb_count = 280, score = 142\\n\\tagent = advanced, dim = 20, bomb_count = 280, score = 105\\n\\tagent = advanced, dim = 20, bomb_count = 280, score = 112\\n\\tagent = advanced, dim = 20, bomb_count = 280, score = 108\\n\\tagent = advanced, dim = 20, bomb_count = 280, score = 128\\nBOMB_COUNT = 320\\n\\tagent = advanced, dim = 20, bomb_count = 320, score = 121\\n\\tagent = advanced, dim = 20, bomb_count = 320, score = 98\\n\\tagent = advanced, dim = 20, bomb_count = 320, score = 86\\n\\tagent = advanced, dim = 20, bomb_count = 320, score = 94\\n\\tagent = advanced, dim = 20, bomb_count = 320, score = 106\\n\\tagent = advanced, dim = 20, bomb_count = 320, score = 119\\n\\tagent = advanced, dim = 20, bomb_count = 320, score = 122\\n\\tagent = advanced, dim = 20, bomb_count = 320, score = 121\\n\\tagent = advanced, dim = 20, bomb_count = 320, score = 132\\n\\tagent = advanced, dim = 20, bomb_count = 320, score = 125\\nBOMB_COUNT = 360\\n\\tagent = advanced, dim = 20, bomb_count = 360, score = 115\\n\\tagent = advanced, dim = 20, bomb_count = 360, score = 97\\n\\tagent = advanced, dim = 20, bomb_count = 360, score = 101\\n\\tagent = advanced, dim = 20, bomb_count = 360, score = 79\\n\\tagent = advanced, dim = 20, bomb_count = 360, score = 82\\n\\tagent = advanced, dim = 20, bomb_count = 360, score = 84\\n\\tagent = advanced, dim = 20, bomb_count = 360, score = 91\\n\\tagent = advanced, dim = 20, bomb_count = 360, score = 94\\n\\tagent = advanced, dim = 20, bomb_count = 360, score = 89\\n\\tagent = advanced, dim = 20, bomb_count = 360, score = 74\\nBOMB_COUNT = 400\\n\\tagent = advanced, dim = 20, bomb_count = 400, score = 0\\n\\tagent = advanced, dim = 20, bomb_count = 400, score = 0\\n\\tagent = advanced, dim = 20, bomb_count = 400, score = 0\\n\\tagent = advanced, dim = 20, bomb_count = 400, score = 0\\n\\tagent = advanced, dim = 20, bomb_count = 400, score = 0\\n\\tagent = advanced, dim = 20, bomb_count = 400, score = 0\\n\\tagent = advanced, dim = 20, bomb_count = 400, score = 0\\n\\tagent = advanced, dim = 20, bomb_count = 400, score = 0\\n\\tagent = advanced, dim = 20, bomb_count = 400, score = 0\\n\\tagent = advanced, dim = 20, bomb_count = 400, score = 0\\n'],\n",
       " ['basic',\n",
       "  'BOMB_COUNT = 0\\n\\tagent = basic, dim = 20, bomb_count = 0, score = 0\\n\\tagent = basic, dim = 20, bomb_count = 0, score = 0\\n\\tagent = basic, dim = 20, bomb_count = 0, score = 0\\n\\tagent = basic, dim = 20, bomb_count = 0, score = 0\\n\\tagent = basic, dim = 20, bomb_count = 0, score = 0\\n\\tagent = basic, dim = 20, bomb_count = 0, score = 0\\n\\tagent = basic, dim = 20, bomb_count = 0, score = 0\\n\\tagent = basic, dim = 20, bomb_count = 0, score = 0\\n\\tagent = basic, dim = 20, bomb_count = 0, score = 0\\n\\tagent = basic, dim = 20, bomb_count = 0, score = 0\\nBOMB_COUNT = 40\\n\\tagent = basic, dim = 20, bomb_count = 40, score = 38\\n\\tagent = basic, dim = 20, bomb_count = 40, score = 40\\n\\tagent = basic, dim = 20, bomb_count = 40, score = 40\\n\\tagent = basic, dim = 20, bomb_count = 40, score = 39\\n\\tagent = basic, dim = 20, bomb_count = 40, score = 40\\n\\tagent = basic, dim = 20, bomb_count = 40, score = 38\\n\\tagent = basic, dim = 20, bomb_count = 40, score = 40\\n\\tagent = basic, dim = 20, bomb_count = 40, score = 40\\n\\tagent = basic, dim = 20, bomb_count = 40, score = 38\\n\\tagent = basic, dim = 20, bomb_count = 40, score = 40\\nBOMB_COUNT = 80\\n\\tagent = basic, dim = 20, bomb_count = 80, score = 78\\n\\tagent = basic, dim = 20, bomb_count = 80, score = 74\\n\\tagent = basic, dim = 20, bomb_count = 80, score = 77\\n\\tagent = basic, dim = 20, bomb_count = 80, score = 69\\n\\tagent = basic, dim = 20, bomb_count = 80, score = 78\\n\\tagent = basic, dim = 20, bomb_count = 80, score = 78\\n\\tagent = basic, dim = 20, bomb_count = 80, score = 73\\n\\tagent = basic, dim = 20, bomb_count = 80, score = 76\\n\\tagent = basic, dim = 20, bomb_count = 80, score = 80\\n\\tagent = basic, dim = 20, bomb_count = 80, score = 75\\nBOMB_COUNT = 120\\n\\tagent = basic, dim = 20, bomb_count = 120, score = 85\\n\\tagent = basic, dim = 20, bomb_count = 120, score = 87\\n\\tagent = basic, dim = 20, bomb_count = 120, score = 89\\n\\tagent = basic, dim = 20, bomb_count = 120, score = 89\\n\\tagent = basic, dim = 20, bomb_count = 120, score = 97\\n\\tagent = basic, dim = 20, bomb_count = 120, score = 95\\n\\tagent = basic, dim = 20, bomb_count = 120, score = 86\\n\\tagent = basic, dim = 20, bomb_count = 120, score = 97\\n\\tagent = basic, dim = 20, bomb_count = 120, score = 92\\n\\tagent = basic, dim = 20, bomb_count = 120, score = 92\\nBOMB_COUNT = 160\\n\\tagent = basic, dim = 20, bomb_count = 160, score = 115\\n\\tagent = basic, dim = 20, bomb_count = 160, score = 92\\n\\tagent = basic, dim = 20, bomb_count = 160, score = 97\\n\\tagent = basic, dim = 20, bomb_count = 160, score = 113\\n\\tagent = basic, dim = 20, bomb_count = 160, score = 100\\n\\tagent = basic, dim = 20, bomb_count = 160, score = 102\\n\\tagent = basic, dim = 20, bomb_count = 160, score = 109\\n\\tagent = basic, dim = 20, bomb_count = 160, score = 93\\n\\tagent = basic, dim = 20, bomb_count = 160, score = 105\\n\\tagent = basic, dim = 20, bomb_count = 160, score = 108\\nBOMB_COUNT = 200\\n\\tagent = basic, dim = 20, bomb_count = 200, score = 106\\n\\tagent = basic, dim = 20, bomb_count = 200, score = 106\\n\\tagent = basic, dim = 20, bomb_count = 200, score = 116\\n\\tagent = basic, dim = 20, bomb_count = 200, score = 106\\n\\tagent = basic, dim = 20, bomb_count = 200, score = 127\\n\\tagent = basic, dim = 20, bomb_count = 200, score = 87\\n\\tagent = basic, dim = 20, bomb_count = 200, score = 102\\n\\tagent = basic, dim = 20, bomb_count = 200, score = 113\\n\\tagent = basic, dim = 20, bomb_count = 200, score = 108\\n\\tagent = basic, dim = 20, bomb_count = 200, score = 118\\nBOMB_COUNT = 240\\n\\tagent = basic, dim = 20, bomb_count = 240, score = 110\\n\\tagent = basic, dim = 20, bomb_count = 240, score = 101\\n\\tagent = basic, dim = 20, bomb_count = 240, score = 109\\n\\tagent = basic, dim = 20, bomb_count = 240, score = 115\\n\\tagent = basic, dim = 20, bomb_count = 240, score = 109\\n\\tagent = basic, dim = 20, bomb_count = 240, score = 113\\n\\tagent = basic, dim = 20, bomb_count = 240, score = 139\\n\\tagent = basic, dim = 20, bomb_count = 240, score = 128\\n\\tagent = basic, dim = 20, bomb_count = 240, score = 112\\n\\tagent = basic, dim = 20, bomb_count = 240, score = 129\\nBOMB_COUNT = 280\\n\\tagent = basic, dim = 20, bomb_count = 280, score = 107\\n\\tagent = basic, dim = 20, bomb_count = 280, score = 114\\n\\tagent = basic, dim = 20, bomb_count = 280, score = 122\\n\\tagent = basic, dim = 20, bomb_count = 280, score = 134\\n\\tagent = basic, dim = 20, bomb_count = 280, score = 106\\n\\tagent = basic, dim = 20, bomb_count = 280, score = 109\\n\\tagent = basic, dim = 20, bomb_count = 280, score = 119\\n\\tagent = basic, dim = 20, bomb_count = 280, score = 125\\n\\tagent = basic, dim = 20, bomb_count = 280, score = 140\\n\\tagent = basic, dim = 20, bomb_count = 280, score = 121\\nBOMB_COUNT = 320\\n\\tagent = basic, dim = 20, bomb_count = 320, score = 121\\n\\tagent = basic, dim = 20, bomb_count = 320, score = 110\\n\\tagent = basic, dim = 20, bomb_count = 320, score = 106\\n\\tagent = basic, dim = 20, bomb_count = 320, score = 108\\n\\tagent = basic, dim = 20, bomb_count = 320, score = 115\\n\\tagent = basic, dim = 20, bomb_count = 320, score = 115\\n\\tagent = basic, dim = 20, bomb_count = 320, score = 95\\n\\tagent = basic, dim = 20, bomb_count = 320, score = 125\\n\\tagent = basic, dim = 20, bomb_count = 320, score = 118\\n\\tagent = basic, dim = 20, bomb_count = 320, score = 130\\nBOMB_COUNT = 360\\n\\tagent = basic, dim = 20, bomb_count = 360, score = 108\\n\\tagent = basic, dim = 20, bomb_count = 360, score = 65\\n\\tagent = basic, dim = 20, bomb_count = 360, score = 99\\n\\tagent = basic, dim = 20, bomb_count = 360, score = 99\\n\\tagent = basic, dim = 20, bomb_count = 360, score = 69\\n\\tagent = basic, dim = 20, bomb_count = 360, score = 87\\n\\tagent = basic, dim = 20, bomb_count = 360, score = 92\\n\\tagent = basic, dim = 20, bomb_count = 360, score = 78\\n\\tagent = basic, dim = 20, bomb_count = 360, score = 65\\n\\tagent = basic, dim = 20, bomb_count = 360, score = 91\\nBOMB_COUNT = 400\\n\\tagent = basic, dim = 20, bomb_count = 400, score = 0\\n\\tagent = basic, dim = 20, bomb_count = 400, score = 0\\n\\tagent = basic, dim = 20, bomb_count = 400, score = 0\\n\\tagent = basic, dim = 20, bomb_count = 400, score = 0\\n\\tagent = basic, dim = 20, bomb_count = 400, score = 0\\n\\tagent = basic, dim = 20, bomb_count = 400, score = 0\\n\\tagent = basic, dim = 20, bomb_count = 400, score = 0\\n\\tagent = basic, dim = 20, bomb_count = 400, score = 0\\n\\tagent = basic, dim = 20, bomb_count = 400, score = 0\\n\\tagent = basic, dim = 20, bomb_count = 400, score = 0\\n']]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_contents = [] # list of lists (agent name, data string)\n",
    "for data_file in data_files:\n",
    "    f = open(data_file, mode=\"r\")\n",
    "    contents = f.read()\n",
    "    agent_name = data_file[data_file.find(\"a/\")+2:-4]\n",
    "    raw_contents.append([agent_name, contents])\n",
    "raw_contents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fitting-syria",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'hyper_advanced': {0: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "  40: [40, 40, 38, 40, 40, 40, 40, 40, 40, 40],\n",
       "  80: [79, 78, 77, 78, 79, 78, 78, 79, 79, 80],\n",
       "  120: [100, 104, 103, 112, 99, 110, 107, 112, 104, 103],\n",
       "  160: [125, 116, 120, 118, 118, 127, 125, 128, 108, 112],\n",
       "  200: [125, 142, 123, 132, 115, 133, 138, 132, 130, 118],\n",
       "  240: [119, 112, 106, 109, 132, 118, 133, 132, 141, 120],\n",
       "  280: [137, 125, 122, 134, 123, 113, 124, 136, 151, 127],\n",
       "  320: [114, 127, 122, 150, 97, 86, 136, 132, 114, 106],\n",
       "  360: [94, 93, 90, 80, 75, 68, 91, 82, 92, 70],\n",
       "  400: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]},\n",
       " 'bonus_1': {0: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "  40: [40, 40, 40, 39, 40, 40, 40, 40, 40, 40],\n",
       "  80: [77, 78, 77, 74, 78, 80, 79, 80, 74, 80],\n",
       "  120: [105, 100, 103, 99, 111, 102, 111, 92, 109, 107],\n",
       "  160: [113, 119, 121, 112, 113, 112, 108, 108, 124, 115],\n",
       "  200: [121, 124, 117, 109, 124, 133, 124, 146, 130, 106],\n",
       "  240: [122, 119, 111, 135, 120, 117, 128, 118, 128, 125],\n",
       "  280: [121, 113, 124, 123, 156, 148, 120, 143, 116, 132],\n",
       "  320: [120, 122, 144, 116, 151, 118, 132, 121, 105, 133],\n",
       "  360: [110, 92, 127, 102, 89, 65, 127, 83, 84, 86],\n",
       "  400: [399, 399, 399, 399, 399, 399, 399, 399, 399, 399]},\n",
       " 'advanced': {0: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "  40: [39, 40, 40, 40, 40, 39, 38, 40, 40, 40],\n",
       "  80: [77, 76, 76, 78, 80, 79, 77, 78, 76, 78],\n",
       "  120: [110, 111, 102, 107, 105, 106, 98, 99, 97, 109],\n",
       "  160: [115, 110, 116, 124, 109, 120, 128, 117, 124, 116],\n",
       "  200: [128, 145, 116, 114, 120, 120, 135, 122, 118, 119],\n",
       "  240: [141, 119, 123, 130, 133, 111, 106, 128, 130, 128],\n",
       "  280: [140, 136, 136, 141, 131, 142, 105, 112, 108, 128],\n",
       "  320: [121, 98, 86, 94, 106, 119, 122, 121, 132, 125],\n",
       "  360: [115, 97, 101, 79, 82, 84, 91, 94, 89, 74],\n",
       "  400: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]},\n",
       " 'basic': {0: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "  40: [38, 40, 40, 39, 40, 38, 40, 40, 38, 40],\n",
       "  80: [78, 74, 77, 69, 78, 78, 73, 76, 80, 75],\n",
       "  120: [85, 87, 89, 89, 97, 95, 86, 97, 92, 92],\n",
       "  160: [115, 92, 97, 113, 100, 102, 109, 93, 105, 108],\n",
       "  200: [106, 106, 116, 106, 127, 87, 102, 113, 108, 118],\n",
       "  240: [110, 101, 109, 115, 109, 113, 139, 128, 112, 129],\n",
       "  280: [107, 114, 122, 134, 106, 109, 119, 125, 140, 121],\n",
       "  320: [121, 110, 106, 108, 115, 115, 95, 125, 118, 130],\n",
       "  360: [108, 65, 99, 99, 69, 87, 92, 78, 65, 91],\n",
       "  400: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Regex match\n",
    "parsed_data = {} # key = agent name, value = parsed data\n",
    "for raw_content in raw_contents:\n",
    "    agent_name, lines = raw_content\n",
    "    lines = lines.split(\"\\n\")\n",
    "    agent_data = {}\n",
    "    curr_bomb_count = -1\n",
    "    for line in lines:\n",
    "        # extract bomb count #\n",
    "        matches = re.findall(r\"BOMB_COUNT = (\\d+)\", line)\n",
    "        if len(matches) != 0:\n",
    "            curr_bomb_count = int(matches[0])\n",
    "            agent_data[curr_bomb_count] = []\n",
    "            continue\n",
    "        \n",
    "        \n",
    "        # extract score sample\n",
    "        matches = re.findall(r\".+?(?=bomb_count)bomb_count = (\\d+), score = (\\d+)\", line)\n",
    "        if len(matches) > 0:\n",
    "            max_score, actual_score = matches[0]\n",
    "            agent_data[curr_bomb_count].append(int(actual_score))\n",
    "                \n",
    "    # add agent data to parsed data\n",
    "    parsed_data[agent_name] = agent_data\n",
    "\n",
    "parsed_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "lesser-finland",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'hyper_advanced': [1.0,\n",
       "  0.995,\n",
       "  0.98125,\n",
       "  0.8783333333333333,\n",
       "  0.748125,\n",
       "  0.644,\n",
       "  0.5091666666666667,\n",
       "  0.4614285714285714,\n",
       "  0.37,\n",
       "  0.23194444444444445,\n",
       "  0.0],\n",
       " 'bonus_1': [1.0,\n",
       "  0.9975,\n",
       "  0.97125,\n",
       "  0.8658333333333333,\n",
       "  0.715625,\n",
       "  0.617,\n",
       "  0.5095833333333334,\n",
       "  0.46285714285714286,\n",
       "  0.394375,\n",
       "  0.26805555555555555,\n",
       "  0.9975],\n",
       " 'advanced': [1.0,\n",
       "  0.99,\n",
       "  0.96875,\n",
       "  0.87,\n",
       "  0.736875,\n",
       "  0.6185,\n",
       "  0.5204166666666666,\n",
       "  0.4567857142857143,\n",
       "  0.35125,\n",
       "  0.25166666666666665,\n",
       "  0.0],\n",
       " 'basic': [1.0,\n",
       "  0.9825,\n",
       "  0.9475,\n",
       "  0.7575,\n",
       "  0.64625,\n",
       "  0.5445,\n",
       "  0.48541666666666666,\n",
       "  0.4275,\n",
       "  0.3571875,\n",
       "  0.23694444444444446,\n",
       "  0.0]}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now that we have the actual scores for each bomb_count for each agent, we need to convert make a dict,\n",
    "# matching agent name to a list of (total_score / total_max_score)\n",
    "score_percentages = {}\n",
    "for agent_name in parsed_data.keys():\n",
    "    curr_percentages = []\n",
    "    for max_score in parsed_data[agent_name].keys():\n",
    "        if max_score == 0:\n",
    "            curr_percentages.append(1.0)\n",
    "        else:\n",
    "            scores = parsed_data[agent_name][max_score]\n",
    "            total_score = sum(scores)\n",
    "            total_max_score = max_score * len(scores)\n",
    "            curr_percentages.append(total_score / total_max_score)\n",
    "    score_percentages[agent_name] = curr_percentages\n",
    "\n",
    "score_percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "liberal-latvia",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fe4b702d7f0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the data.\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.rcParams[\"font.size\"] = \"20\"\n",
    "plt.xlabel(\"Bomb Density\")\n",
    "plt.ylabel(\"Average Score / Max Score\")\n",
    "for name, y_axis in score_percentages.items():\n",
    "    densities = [x / max(parsed_data[agent_name].keys()) for x in parsed_data[agent_name].keys()]\n",
    "    plt.plot(densities, y_axis, label=name)\n",
    "\n",
    "plt.title(\"Bomb Density vs Score\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "parallel-motel",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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