GenFair Tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6e0579d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, pandas as pd\n",
    "sys.path.insert(0, \"...\") #directory of \"genfair\" folder\n",
    "from genfair import GenFair"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3e1dc1e9",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Job</th>\n",
       "      <th>Housing</th>\n",
       "      <th>Saving accounts</th>\n",
       "      <th>Checking account</th>\n",
       "      <th>Credit amount</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Risk</th>\n",
       "    </tr>\n",
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       "      <th>796</th>\n",
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       "      <td>2</td>\n",
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       "      <td>0</td>\n",
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       "<p>800 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Sex  Job  Housing  Saving accounts  Checking account  Credit amount  \\\n",
       "0      0    0    2        1                0                 1           2394   \n",
       "1      0    1    2        2                0                 1           2323   \n",
       "2      1    1    1        1                0                 0           2579   \n",
       "3      1    0    3        1                0                 1           4795   \n",
       "4      0    1    2        1                1                 1           5595   \n",
       "..   ...  ...  ...      ...              ...               ...            ...   \n",
       "795    1    0    2        1                0                 0            915   \n",
       "796    1    1    2        1                0                 1           3979   \n",
       "797    0    0    1        1                0                 0           1228   \n",
       "798    1    1    3        1                0                 2           1278   \n",
       "799    1    1    2        1                1                 0           2141   \n",
       "\n",
       "     Duration  Purpose  Risk  \n",
       "0          36        5     1  \n",
       "1          36        5     1  \n",
       "2          12        1     0  \n",
       "3          36        5     1  \n",
       "4          72        5     0  \n",
       "..        ...      ...   ...  \n",
       "795        24        1     0  \n",
       "796        48        5     1  \n",
       "797        12        1     0  \n",
       "798        24        5     1  \n",
       "799        12        5     1  \n",
       "\n",
       "[800 rows x 10 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "german = pd.read_json(\"/genfair_german_train.json\")\n",
    "\n",
    "german"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "02818513",
   "metadata": {},
   "outputs": [],
   "source": [
    "sensitive_attributes = ['Age', 'Sex'] #['sex', 'race']\n",
    "class_name = 'Risk'\n",
    "\n",
    "\n",
    "#Experimental, for causal relations. Unused in the paper\n",
    "#multiple independent features are supported, e.g. [[['Job', 'Credit amount'], 'Housing']]\n",
    "causal_reg = [] #regressor\n",
    "causal_class = [] #classifier\n",
    "\n",
    "\n",
    "integer = [] #Integer  Strategy\n",
    "weighted = ['Job','Housing', 'Saving accounts', 'Checking account',\n",
    "       'Credit amount', 'Duration', 'Purpose'] #Weighted Probability  strategy\n",
    "random_l = [] #Random Probability  strategy\n",
    "\n",
    "#if a feature is in none of the above: Float Strategy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6f702476",
   "metadata": {},
   "outputs": [],
   "source": [
    "gf = GenFair(german, sensitive_attributes, class_name, #<- must be included\n",
    "                               causal_reg, causal_class, #<- causal relations\n",
    "                               integer, weighted, random_l) #<- how to handle the various features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8c351f4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "To get the results, run GenFair.fit() and GenFair.balance()\n"
     ]
    }
   ],
   "source": [
    "gf.get_results()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6ae4ccf2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target variable: Risk\n",
      "Sensitive attributes: ['Age', 'Sex']\n",
      "\n",
      "Integer strategy for: []\n",
      "Weighted strategy for: ['Job', 'Housing', 'Saving accounts', 'Checking account', 'Credit amount', 'Duration', 'Purpose']\n",
      "Random strategy for: []\n",
      "\n",
      "Regressor: []\n",
      "Classifier: []\n",
      "Ranking the data...\n",
      "Creating the sensitive dictionary...\n",
      "\n",
      "Analizing Age ...\n",
      "0.0 is discriminated: 0.14182194616977217\n",
      "- DP: 91 · Expected: 109 · To be added: 18\n",
      "- DN: 65 · Expected: 47 · To be removed: 18\n",
      "\n",
      "1.0 is privileged: -0.14182194616977217\n",
      "- PP: 467 · Expected: 449 · To be removed: 18\n",
      "- PN: 177 · Expected: 195 · To be added: 18\n",
      "\n",
      "Analizing Sex ...\n",
      "0.0 is discriminated: 0.054728992492286355\n",
      "- DP: 167 · Expected: 176 · To be added: 9\n",
      "- DN: 86 · Expected: 77 · To be removed: 9\n",
      "\n",
      "1.0 is privileged: -0.054728992492286355\n",
      "- PP: 391 · Expected: 382 · To be removed: 9\n",
      "- PN: 156 · Expected: 165 · To be added: 9\n"
     ]
    }
   ],
   "source": [
    "gf.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e9b4b056",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Removing instances...\n",
      "\n",
      "Instances: removed: 38\n",
      "\n",
      "Current length of dataset:  762\n",
      "GA started, 20 individual(s) will be generated\n",
      "gen\tnevals\tmin       \tavg      \n",
      "0  \t150   \t0.00332572\t0.0430039\n",
      "1  \t92    \t0.000864549\t0.0426   \n",
      "2  \t87    \t0.000864549\t0.0423016\n",
      "3  \t87    \t0.000393121\t0.0426971\n",
      "4  \t82    \t0.000393121\t0.0423799\n",
      "5  \t85    \t0.000393121\t0.0421797\n",
      "6  \t96    \t0.000184672\t0.0420606\n",
      "7  \t91    \t0.000183742\t0.0412433\n",
      "8  \t91    \t0.000183742\t0.0406576\n",
      "9  \t71    \t0.000810559\t0.0405034\n",
      "10 \t80    \t0.000328754\t0.0407208\n",
      "11 \t78    \t0.000328754\t0.0407949\n",
      "12 \t86    \t0.000328754\t0.0409156\n",
      "13 \t83    \t0.000328754\t0.0404287\n",
      "14 \t84    \t0.000328754\t0.0402054\n",
      "15 \t91    \t0.000328754\t0.0406523\n",
      "16 \t95    \t0.000328754\t0.0400504\n",
      "17 \t87    \t0.000323904\t0.0401413\n",
      "18 \t71    \t0.000323904\t0.0406214\n",
      "19 \t89    \t0.000236295\t0.0405253\n",
      "20 \t86    \t2.36572e-05\t0.040851 \n",
      "21 \t91    \t2.36572e-05\t0.0413457\n",
      "22 \t92    \t2.36572e-05\t0.0414116\n",
      "23 \t76    \t2.36572e-05\t0.0423597\n",
      "24 \t69    \t2.36572e-05\t0.042392 \n",
      "25 \t95    \t2.36572e-05\t0.0418915\n",
      "26 \t94    \t0.00048588 \t0.041953 \n",
      "27 \t91    \t0.00048588 \t0.0415895\n",
      "28 \t80    \t0.000377973\t0.041427 \n",
      "29 \t84    \t0.000377973\t0.0417892\n",
      "30 \t92    \t0.000383495\t0.0418077\n",
      "31 \t74    \t0.000383495\t0.0421905\n",
      "32 \t84    \t0.000377973\t0.0423029\n",
      "33 \t70    \t0.000377973\t0.0420897\n",
      "34 \t102   \t0.000383495\t0.0425335\n",
      "35 \t72    \t0.000383495\t0.042288 \n",
      "36 \t82    \t0.000383495\t0.0427437\n",
      "37 \t81    \t0.000383495\t0.0428196\n",
      "38 \t76    \t0.0011483  \t0.0430289\n",
      "39 \t93    \t0.0011483  \t0.0438616\n",
      "40 \t74    \t0.0011483  \t0.0427192\n",
      "41 \t79    \t0.0011483  \t0.0426338\n",
      "42 \t84    \t0.001493   \t0.0420611\n",
      "43 \t98    \t0.000187303\t0.0418243\n",
      "44 \t93    \t0.000187303\t0.0414339\n",
      "45 \t84    \t0.000187303\t0.0408871\n",
      "46 \t80    \t0.00018622 \t0.0408167\n",
      "47 \t91    \t0.00018622 \t0.0406489\n",
      "48 \t95    \t0.00018622 \t0.041036 \n",
      "49 \t89    \t0.00018622 \t0.0413429\n",
      "50 \t76    \t0.00018622 \t0.0410646\n",
      "\n",
      "#1\n",
      "Constraints: ((9, 0), (0, 1.0), (1, 1.0))\n",
      "Individual: [[1.0, 1.0, 2.0, 1.0, 0.0, 0.0, 3108.0, 21.0, 3.0, 0]]\n",
      "Fitness: (2.3657185050263152e-05,)\n",
      "\n",
      "#2\n",
      "Constraints: ((9, 0), (0, 1.0), (1, 1.0))\n",
      "Individual: [[1.0, 1.0, 2.0, 1.0, 0.0, 0.0, 3108.0, 18.0, 3.0, 0]]\n",
      "Fitness: (0.00018374205835208768,)\n",
      "\n",
      "#3\n",
      "Constraints: ((9, 0), (0, 1.0), (1, 1.0))\n",
      "Individual: [[1.0, 1.0, 2.0, 1.0, 0.0, 0.0, 3108.0, 24.0, 3.0, 0]]\n",
      "Fitness: (0.0001846715099850793,)\n",
      "\n",
      "#4\n",
      "Constraints: ((9, 0), (0, 1.0), (1, 1.0))\n",
      "Individual: [[1.0, 1.0, 2.0, 1.0, 0.0, 0.0, 3092.0, 18.0, 3.0, 0]]\n",
      "Fitness: (0.0001862197403768584,)\n",
      "\n",
      "#5\n",
      "Constraints: ((9, 0), (0, 1.0), (1, 1.0))\n",
      "Individual: [[1.0, 1.0, 2.0, 1.0, 0.0, 0.0, 3092.0, 24.0, 3.0, 0]]\n",
      "Fitness: (0.00018730314528336844,)\n",
      "\n",
      "#6\n",
      "Constraints: ((9, 0), (0, 1.0), (1, 1.0))\n",
      "Individual: [[1.0, 1.0, 2.0, 1.0, 0.0, 0.0, 2862.0, 18.0, 3.0, 0]]\n",
      "Fitness: (0.00023629505294026076,)\n",
      "\n",
      "#7\n",
      "Constraints: ((9, 0), (0, 1.0), (1, 1.0))\n",
      "Individual: [[1.0, 1.0, 2.0, 1.0, 0.0, 0.0, 2606.0, 18.0, 3.0, 0]]\n",
      "Fitness: (0.0003239044125303292,)\n",
      "\n",
      "#8\n",
      "Constraints: ((9, 0), (0, 1.0), (1, 1.0))\n",
      "Individual: [[1.0, 1.0, 2.0, 1.0, 0.0, 0.0, 2606.0, 24.0, 3.0, 0]]\n",
      "Fitness: (0.00032875406688126496,)\n",
      "\n",
      "#9\n",
      "Constraints: ((9, 0), (0, 1.0), (1, 1.0))\n",
      "Individual: [[1.0, 1.0, 2.0, 1.0, 0.0, 0.0, 2483.0, 18.0, 3.0, 0]]\n",
      "Fitness: (0.0003779733162989074,)\n",
      "\n",
      "#10\n",
      "Constraints: ((9, 0), (0, 1.0), (1, 1.0))\n",
      "Individual: [[1.0, 1.0, 2.0, 1.0, 0.0, 0.0, 2483.0, 24.0, 3.0, 0]]\n",
      "Fitness: (0.00038349480682753345,)\n",
      "\n",
      "#11\n",
      "Constraints: ((9, 0), (0, 1.0), (1, 1.0))\n",
      "Individual: [[1.0, 1.0, 2.0, 1.0, 0.0, 0.0, 2463.0, 24.0, 3.0, 0]]\n",
      "Fitness: (0.0003931208107798012,)\n",
      "\n",
      "#12\n",
      "Constraints: ((9, 0), (0, 1.0), (1, 1.0))\n",
      "Individual: [[1.0, 1.0, 2.0, 1.0, 0.0, 0.0, 2353.0, 18.0, 3.0, 0]]\n",
      "Fitness: (0.00044358532358312885,)\n",
      "\n",
      "#13\n",
      "Constraints: ((9, 0), (0, 1.0), (1, 1.0))\n",
      "Individual: [[1.0, 1.0, 2.0, 1.0, 0.0, 0.0, 2353.0, 24.0, 3.0, 0]]\n",
      "Fitness: (0.00044969245101195154,)\n",
      "\n",
      "#14\n",
      "Constraints: ((9, 0), (0, 1.0), (1, 1.0))\n",
      "Individual: [[1.0, 1.0, 2.0, 1.0, 0.0, 0.0, 4576.0, 24.0, 3.0, 0]]\n",
      "Fitness: (0.000485880046023901,)\n",
      "\n",
      "#15\n",
      "Constraints: ((9, 0), (0, 1.0), (1, 1.0))\n",
      "Individual: [[1.0, 1.0, 2.0, 1.0, 0.0, 0.0, 2606.0, 15.0, 3.0, 0]]\n",
      "Fitness: (0.0008105591258777967,)\n",
      "\n",
      "#16\n",
      "Constraints: ((9, 0), (0, 1.0), (1, 1.0))\n",
      "Individual: [[1.0, 1.0, 2.0, 1.0, 0.0, 0.0, 1804.0, 18.0, 3.0, 0]]\n",
      "Fitness: (0.0008168638007446383,)\n",
      "\n",
      "#17\n",
      "Constraints: ((9, 0), (0, 1.0), (1, 1.0))\n",
      "Individual: [[1.0, 1.0, 2.0, 1.0, 0.0, 0.0, 1804.0, 24.0, 3.0, 0]]\n",
      "Fitness: (0.0008240249076710282,)\n",
      "\n",
      "#18\n",
      "Constraints: ((9, 0), (0, 1.0), (1, 1.0))\n",
      "Individual: [[1.0, 1.0, 2.0, 1.0, 0.0, 0.0, 5084.0, 24.0, 3.0, 0]]\n",
      "Fitness: (0.0008361969928008528,)\n",
      "\n",
      "#19\n",
      "Constraints: ((9, 0), (0, 1.0), (1, 1.0))\n",
      "Individual: [[1.0, 1.0, 2.0, 1.0, 0.0, 0.0, 1747.0, 18.0, 3.0, 0]]\n",
      "Fitness: (0.0008645493018379358,)\n",
      "\n",
      "#20\n",
      "Constraints: ((9, 0), (0, 1.0), (1, 1.0))\n",
      "Individual: [[1.0, 1.0, 2.0, 1.0, 0.0, 0.0, 5084.0, 18.0, 3.0, 0]]\n",
      "Fitness: (0.000868429103655699,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Y/HbzNyRmJAIAPmv2GZa0XwIzI7PXPj+gagBODzuNDts6IPJRJNpubYsTQ07AzsxOV2+B9IyBR4tkchk+2vsRBAhYFr8M7Wq1w0Cvgfiw/oewM+c3GRHRP8kFObILspGWm4ZrqdcQmxKL2JRYxD2Jw830m5ALcpX2LpYu2NJnCzq5dypV/35V/HB2+Fm0/3d7xKbEos3WNjg66KgW3gmVRww8WmRoYIhtfbZh6ZmluJp1FSdun8CJ2ycw4dAEdPbojAFeA9DbszesTK008nppuWmQyqWoYllFI/0REWmKIAiIfBSJ/df34/Gzx8jKz0JWfhYy8zMV/+ZlIrsgGwKE1/Zhb24PX2df+Dj5wNfZ953+eGzg2ABnh59Fu63tcC31Gtpva48xtcbgmd0zOFo6wt7cHrbmtjAxNCnrW6ZyhoFHywZ6D0Q9aT1Yu1lj7/W92HV1l3I69rebv8HMyAzd63RHcP1gtK3ZVrnQrrRSnqVg3/V92HN1D87dOwcBAho4NkDH2h3RsXZHtK7ZGpYmllp6d0REb5aRl4HtsduxOWozYlNiS/UcC2ML1LWvqww2xf9WsawCiURS5jHVta+LcyPOod3WdriZcRMzMmYAkaptrEysYGduB3sLe9ib26N7ne4YEzAGFsYWZX590g+JIAivj9PvCZlMhujoaDRs2BCGhoZa7/t66nXsvrobO+N34mb6TZX2nvaeaFuzLdrWaos2NdvAqZJTiT6fPH+C/df3Y8/VPTh776zKNK8EEpW/kIwNjNHctTk61u6ITu6dEOASAEMDzb7H8kCb/4f0AuusG+WlzoIg4L8P/ost0Vtw9NZR1LCpgcZVG6NJtSZoXLUxPOw8XhlA5IIcp+6cwuaozdh/fT8KZYUAAFNDU3xY/0P4OPnA1swWlc0qw9b8f//+735ls8owNTLVyfu7//Q+Zh2fhbi/45AvyUdGfgYy8zJfO8vkaOGIL5p/gYmNJ2psZv59oa3PtDr9MvBA94GnmCAIiEmJwe743Th2+xiiHkWV+EbzcvRCm5pt0LZmW2TkZWD31d04ffe0SshpUq0JPmrwEfp79YeliSVO3TmF47eO49jtY7ibdVelP1szW3zg+gF8nHzg5eQFbydv1HOo98bFfhVBefkFIXa6rPPFhxfxU9RPeFb0DAXSAhTICl75r4mhCT7y+ghjGo2BYyVHrY5JVzRRZ7kgR3puOjLzM1HDpoZa3+MPnj7Av2P+jS0xW5CUkfTadrZmtgisGqgMQLVta+NgwkGERYep/OxpWKUhRvmPwmCfwbA1t32n96MtL9daJpfhacFTpOemIyMvA+l56UhMT8SPET/iTtYdAICduR0+bfoppjSdgspmlfX7BioIBp5yQl+B52WZeZk4d+8cTt89jdN3T79x+jewaqAy5NSsXPOVbQRBwK3MWzh+6ziO3z6OU3dO4WnB0xLtDCQG8LDzgLeTN7wcFSGojl0dVLOuBgcLhxKHeJZHDDy6oas6r7u8DlN+nwKpXFrq55gammKg90BMaTIFAVUDtDY2XShNnbPys3Du3jn8nf03Hj17hMfPHr/4N+cRUp6nKOtnKDFEXfu68KviB18nX/g6+8Kvih+qWVVTztDkFeXhQMIBbIneghO3Tyj/+KpkXAkfeX2EAV4DkJqbikt/X8JfyX8h6lEUCmSvv0aVjakNBvsMxqhGo9DIpZGGK6Q5pf1MF8mK8J+4/2DJhSXKmXlrU2tMaTIFnzb7FA4WDroacoXEwFNOlJfA87L03HScvXcWp++cxtl7Z2FqZIq+9fviI6+PUNu2ttpjkcqliEyOROSjSMQ/icfV1KuIS4lDZn7ma59jZGAEF0sXVLWqChcrF1S1rIqqVopb42qN4e3krfY4tIGBRze0XecCaQGm/j4VG65sAAD09uyNVm6tYGpoClMj01f+e+/pPaz+azUuJ19W9tO8enNMaTIFfRv0rZCLT19X57TcNBxMOIh91/fh5O2TKJIXvbUvC2ML5BblvvIxWzNb+Dr7wsXKBYcTDyO7IFv5WJuabTDcbzj6Nuj7ynWAhbJCxD+Jx19//6UMQTfSb6CFawuM8h+F4PrBMDc2f4d3r1vqfqZlchnCr4Vj0blFuJp6FYAiFE4InIBPfD9BA8cGMDY01vawKxwGnnKivAYeXRAEAY+fPcbV1KuIfxKvvN3Nuosnz5+88YgJCSSY/sF0LGy7UGf73F+nvNdZLLRZ50c5j9AvvB/+ePAHJJBgafulmNliZqkWqQqCgIi/I7Dy0kqEXw1XBoEqllUwLmAcRvmPgpmRGXIKc5BdkI2cgpwSX8vkMrSu2RqBVQP1Pqv5zzo/yX2CAwkHsO/6Ppy9exYyQaZsV8+hHuo51IOLpQuqWFZR/lvFsgpcrFzgVMkJxgbGSM5JVh7iHZMSg9iUWCSkJaj0BQBuNm4Y3nA4hvoNfac/qgRB0MiiYl1618+0XJDj54SfsfDcQkQ9jlJuNzU0RcMqDRHgEoDAqoEIqBqABo4NYGTwfh8jxMBTTrzPgedNimRFSHmeguScZCTnJONRziPl17ezbuPM3TMAAB8nH2wP3g5fZ1+9jbUi17ki0VadL/19CR/u/hDJOcmwMbXBzr470bVO13fq6/Gzx1h/eT3WRa7D42eP1X5+Vauq6FW3F3rX6422NdtqPMznS/MR/TgaeUV5kMqlr7wVSgsRlRiFy88u448Hf6j84eFfxR996/dF3wZ9Uc+h3juPo0BagOtp1xHzOAZ3s+6ilVsrtK7ZWu9hT9fK+pkWBAG/J/2OFRErcPHhxVcuGzA3ModfFT8EugSiQ+0O6OzRucKvm1QXA085wcDzbn658QtG/zIaqbmpMDE0waK2i/B588/1chSYmOtcnmijzluit2Dcb+NQKCtEfYf6+Hngz6hjX6fM/RbKCrH/+n6svLQSfzz4A4Bi9461qTWsTKwU/5paKb9+XvQcJ26fwLPCZ8o+rEys0MWjC3p79ka3Ot3eecFtWm4aDt08hF9u/oKjSUfxvEi9q3Y3rdZUGXLeZeaFXk+Tn2m5IMetjFuIfBSJy8mXEfkoEpHJkcgpVL1gqZWJFXp69kT/Bv3RxaPLexF+GHjKCQaed/fk+ROM+XUMfrnxCwDFhfq29tn62oXU2iL2OpcXmqxzkawI049Nx4pLKwAo1uts+3CbVg73zS3Khamh6VvDeIG0AKfunMLPN37GLzd+waNnj5SPGRkYIahGEHycfOBu6w4POw+427mjVuVar5wFSspIwi83fsHPN37GhfsXVI6sdK7kDHsLexgZGL3yZigxROHzQvT2641+DfrB1cZVc8UgFdr+2SEX5EhMT0Tko0j8+eBPHLxxEA+zHyoftzSxRM+6L8JPRVj39C4YeMoJBp6yEQQBYVFhmHZkGp4XPYeViRVWdl2JoX5DdbY//32oc3nwLnUukhUhuyAb2QXZeFrwVPn1t39+q9wtGtI6BPNazytXu1PkghyXky/j54Sf8fONn5ULVF8mgQSuNq5wt3WHu607rEytcPTWUVxLvabSzs/ZD709e6OXZy80cmn0xu8Nfp51R9e1lgtyRDyMQPi1cOy9thcPsh8oH7M0sUSPuj0wsuFIdKjdocKth3oTBp5ygoFHM25l3MLQg0OVuw+C6wdjfY/1Ojlc832qsz69rc7XU69j9V+rceL2CWTlZyG7IBt50rzX9mdpYontH25H73q9tTlsjbiVcQun7pxCUkYSkjKTcCvjFm5l3lLZBfZPhhJDtK7ZWhly1Jn15OdZd/RZa7kgx6W/LyH8ajj2Xt+L+0/vKx/zr+KPmS1mol+DfuVywXOBtAARf0fg5O2TOH//PKxNrRFUIwgta7REI5dGJY5UKw+Bp/xVkSosdzt3nBt+Dsv/uxxfnfkK+6/vx9m7ZzHjgxmY1GQSL3EhUlK5FL/e+BWr/lqFU3dOvbZd8fqZ4purtSsWtVuEBo4NdDjad+du5w53O3eVbYIgIDU3FUkZLwJQ6vNUtKjRAl09upa7k+xR+WIgMUCz6s3QrHozfNPpG1z6+xK2x25HWHQYoh5H4eN9H2Puqbn4ovkXGNFwhF53d8nkMkQ/jsbJOydx8s5JnL93vsQfMz/f+BmAYpF2s+rN0LJGSwTVCEJz1+YwN9T/rjrO8IAzPNoQ9SgKQw4MUe4GsDe3x/QPpmNS40laWaPxvtZZ1/5Z5/T8dGy6sgnrLq9TTssbSAzQ27M3RvqPRA2bGspwY2VixXOTqIGfZ90pj7VOz03H6r9WY0XECqTnpQNQXNZiatOpmNh4otoXTH1Xj3Ie4ecbP+PYrWM4c/dMiXO2OVVyQrta7dC2ZltkF2Tj/P3zuHD/AjLyMlTaGUoM0bBKQ/R16YuZ3WZyl5Y+MfBoh1Quxc64nVh4biESMxIBKE7J/kXzLzC5yWRYm1pr7LXepc6CICAzP1N5qP3f2X8jOScZZkZmGOQzSO0Lub4PpFIpdpzZgeNPjyP8erjyGkkOFg4Y02gMxgeORw2bGnoeZcX3Pv/c0LXyXOvcolyERYXh2z+/VV6qo5JxJYwNGIuOtTuislll2JjZwMbUBpXNKsPC2KLM634eZj/E/uv7sffaXly4f0HllAjWptZo7dYa7Wu1R/va7eHl6FXi9eSCHAlpCbhw/wLO3z+P8/fO497TewAAr8peiJkcw8CjTww82iWVS7ErfhcWnluoPCW7nbkdPm/2OaY0naKR4COVSnH2r7Nw9XBFTlEOsvKzXnlLzU3F3zl/K0NOvjT/lf0ZGRjhw3ofYlLjSWjl1kpUiwfVlVOQg1N3TuHoraM4mnQUt7NuKx9rUq0JJjeejP5e/d+LQ2t1hT83dKci1Foql2LP1T1Y/t/liEmJeW07Q4khbMwU4cfG1AbVraujjl0d1LGvAw87D9Sxq4Pq1tVLHK14/+l97Lu2D3uv71WuwSzWrHoz9KjTA+1rt0dg1cB3Wk/04OkDRDyMgHG6MXq06MHAo08MPLohk8uwK34XFpxboAw+tma2GNFwBOo71kdt29qobVsb1a2rv/GbKrcoF/FP4hHzOAYxKTGIfhyN2JTYEue6KC07cztUs6qmvGTGjfQbKt/0DRwbYELgBAzxHQIbM5t3eo2XFcoKcejmIaTmpuIjr4+0fgHCtNw0xKbEwtbMFo6VHOFo4fjaE+rJBTmiHkUpAs6to/jjwR8q17QyMTDBQO+BmNxkMhpXa6zVcb+v+HNDdypSrQVBwLFbx7Dm8hrcf3ofT/OfIis/C08Lnqqc9uBNTAxNlKdVqFm5JiL+jsClvy+ptGnh2gL9GvRDcP1gjc3YlodFy3oNPAUFBQgNDcWxY8dgZmaGkSNHYuTIka9se+3aNcyfPx83b96Eh4cHQkND4e1d8jpOv//+Oz799FPcuHGj1ONg4NEtmVyG3Vd3Y8HZBbiRXvL/yVBiCLfKbqhVuZYyBBVfWT4mJQY302++9pvb0sQSlc0ql7yZVoatuW2JcONi5fLKmYmYxzFYe3kttsduV54krpJxJXzi+wkmNp74zmeVjnkcg5+if8L22O3KffM2pjb4ovkXmNZsmkZ38wmCgAv3L2Bd5DrsvbZXufupmJWJlTL8OFZyhIOFA/Kl+Thx+wTSctNU2nrYeaCze2d0qNUBdtl2aBHYgp9nLeLPDd0RQ60FQcDzoucqASgzLxP3nt5DUkYSEjMSlQvrX3X9NQkkaOnWEv3qK0JONetqGh/jex94Fi5ciL/++gtLly5FcnIyvvzySyxZsgRdunRRaZebm4tOnTqhZ8+e6NevH3bu3Inff/8dx48fh4WFhbJddnY2unXrhtTUVAaeCkAmlymvD3Q76zbuZN7Bnaw7JX4xv4pTJSf4OfuhYZWG8HP2g7ejN/Ie5qFxo8YarfPT/KfYFrsNa/5ag+tp15XbA1wC0Kx6MzSs0hD+Vfzh5eT12l066bnp+E/cf/BT9E8q19xxsXSBjZkNEtISAChmmmZ+MBOTm0xGJZNKZR7zusvrVM4dU6tyLeRJ85CWm/bWq5BbmVihfe326OzeGZ3cOynP7svPs26wzrrzPtVaJpfhQfYDJKYnIjEjEXcy76C2bW18WP9DVLGsot3XLgeBR2+Hpefm5iI8PBwbN26El5cXvLy8kJiYiB07dpQIPIcPH4apqSlmzlRcSHDu3Lk4d+4cjhw5guDgYGW75cuXw9XVFampqbp+O/QODA0M8ZHXR/jI6yPlNrkgx6OcR7ideRu3M2/jTtYd3M68DZkgg6+TL/yqKELOy9+cMpkM0cnRGh+jjZkNJjeZjEmNJ+HsvbNY89caHEg4oDhl/KNIZTsjAyPUd6ivDED+Lv7ILcrFlugt+PnGz8oQZ2xgjN71emNEwxHo5N4JEkiw5+oehJwNwc30m5h1cha+u/gdZrWYhfGB49U6DDUyORJrL6/FzvidyqtjWxhbYJD3IIwPHI+AqgEAFH8NFq9nSn2eirTcNOXXMkGG1m6t0ax6Mx5VRSQyhgaGqFm5JmpWromO7h31PRyd01vgSUhIgFQqhb+/v3JbQEAA1q1bB7lcDgODF2dcjYmJQUBAgHLhqEQiQaNGjRAdHa0MPJcuXcKlS5cwd+5cjB07VrdvhjTGQGKAatbVUM26Glq6tdT3cJQkEgna1GyDNjXb4FHOI5y+expRj6IQ9Vhxy8jLQNyTOMQ9icO22G0lnt+wSkOMaDgCg3wGlTgR48c+H6O/V3/siN2BBecW4HbmbXx+7HN88+c3mBM0B6MbjYaBxAAZeRlIy01DWm4a0vPSX3ydm47z98/jr+S/lH2+ad2RRCKBrbktbM1tUde+rnYKRkRUzugt8KSmpsLW1hYmJibKbQ4ODigoKEBWVhbs7OxU2np4eKg8397eHomJikOdCwsLMW/ePHz11VcwNuZfpaRdLlYuGOQzCIN8BgFQzJg8zH6I6MfRygAU/TgaBdIC9G/QHyP8R6BhlYZv7NPIwAjDGg7DIJ9B2BK9BQvPLcSD7AeY/PtkfHHsCxTICt46LmMDY/Rr0A8TAicgqEbQe31kGRHRy/QWePLy8lTCDgDl/cLCwlK1LW63evVqeHl5ISgoCBEREe88JplM9s7PfVuf2uibXtB3nataVkVVj6ro5tHtlY+XdlwGMMDIhiMx2HswwqLDsOTCEuUFLCVQzMw4mDvA3sIe9ub2cLBQfO1m44Z+9fvBqZITAEAuL90RG+rSd53fF6yz7rDWuqGtOqvTn94Cj6mpaYlgU3zfzMysVG3NzMxw8+ZN7NmzB7/++muZxxQXF1fmPvTRN70gpjo3N2qOfS334XHeY1ibWMPK2AqGktcvyktOTEYyknUyNjHVuTxjnXWHtdYNfdZZb4HH2dkZmZmZkEqlMDJSDCM1NRVmZmawtrYu0TYtTfUw2bS0NDg5OeHYsWN4+vQpOnZULMAqTnv+/v4IDQ1Fr169Sj0mHx8frRylFRcXp5W+6QXWWTdYZ91gnXWHtdYNbdW5uN/S0FvgqV+/PoyMjBAdHY3AwEAAQGRkJHx8fFQWLAOAn58fNm7cCEEQIJFIIAgCrly5gvHjx6N9+/bo2bOnsm1MTAxmzJiBgwcPwt7eXq0xGRoaau0Dr82+6QXWWTdYZ91gnXWHtdYNfdbZ4O1NtMPc3Bx9+vRBSEgIYmNjceLECYSFhWHo0KEAFLM9+fmK0/536dIF2dnZWLx4MZKSkrB48WLk5eWha9euqFy5Mtzc3JQ3Z2dnAICbmxssLXl1biIiItJj4AGA2bNnw8vLC8OGDUNoaCimTJmCTp06AQCCgoJw+PBhAIClpSXWr1+PyMhIBAcHIyYmBhs2bFA56SARERHR6+htlxagmOVZtmwZli1bVuKxl8+U7OvriwMHDry1z6ZNm6p1lmUiIiISP73O8BARERHpAgMPERERiR4DDxEREYkeAw8RERGJHgMPERERiR4DDxEREYkeAw8RERGJHgMPERERiR4DDxEREYkeAw8RERGJHgMPERERiR4DDxEREYkeAw8RERGJHgMPERERiR4DDxEREYkeAw8RERGJHgMPERERiR4DDxEREYkeAw8RERGJHgMPERERiR4DDxEREYkeAw8RERGJHgMPERERiR4DDxEREYkeAw8RERGJHgMPERERiR4DDxEREYkeAw8RERGJHgMPERERiR4DDxEREYkeAw8RERGJHgMPERERiR4DDxEREYkeAw8RERGJHgMPERERiR4DDxEREYkeAw8RERGJHgMPERERiR4DDxEREYkeAw8RERGJHgMPERERiR4DDxEREYkeAw8RERGJHgMPERERiR4DDxEREYkeAw8RERGJHgMPERERiR4DDxEREYkeAw8RERGJHgMPERERiR4DDxEREYkeAw8RERGJHgMPERERiR4DDxEREYkeAw8RERGJHgMPERERiR4DDxEREYkeAw8RERGJHgMPERERiR4DDxEREYkeAw8RERGJHgMPERERiR4DDxEREYkeAw8RERGJHgMPERERiZ5eA09BQQHmzJmDwMBABAUFISws7LVtr127hv79+8PPzw99+/ZFfHy88jGZTIZvvvkGLVq0gL+/P6ZNm4a0tDRdvAUiIiKqAPQaeJYvX474+Hhs3boV8+fPx6pVq3DkyJES7XJzczF27FgEBgZi//798Pf3x7hx45CbmwsA2LBhAw4fPowffvgB4eHhePr0KWbOnKnrt0NERETllN4CT25uLsLDwzF37lx4eXmhY8eOGD16NHbs2FGi7eHDh2FqaoqZM2fC3d0dc+fORaVKlZThSCaTYfbs2WjcuDE8PDwwZMgQREZG6votERERUTmlt8CTkJAAqVQKf39/5baAgADExMRALpertI2JiUFAQAAkEgkAQCKRoFGjRoiOjgYATJ48GR07dgQApKenIzw8HE2aNNHNGyEiIqJyz0hfL5yamgpbW1uYmJgotzk4OKCgoABZWVmws7NTaevh4aHyfHt7eyQmJqpsW7FiBVavXg0bGxvs3LlT7THJZDK1n1PaPrXRN73AOusG66wbrLPusNa6oa06q9Of3gJPXl6eStgBoLxfWFhYqrYvt+vduzfatm2LTZs2YeTIkTh06BAsLS1LPaa4uDh13oJatNk3vcA66wbrrBuss+6w1rqhzzrrLfCYmpqWCCzF983MzErV9uV2bm5uABSLoVu1aoVjx44hODi41GPy8fGBoaFhqduXhkwmQ1xcnFb6phdYZ91gnXWDddYd1lo3tFXn4n5LQ2+Bx9nZGZmZmZBKpTAyUgwjNTUVZmZmsLa2LtH25cPM09LS4OTkBAA4ffo0GjRoAGdnZwCKgOTq6orMzEy1xmRoaKi1D7w2+6YXWGfdYJ11g3XWHdZaN/RZZ70tWq5fvz6MjIyUC48BIDIyEj4+PjAwUB2Wn58foqKiIAgCAEAQBFy5cgV+fn4AgGXLluHgwYPK9s+ePcPdu3fh7u6u9fdBRERE5Z/eAo+5uTn69OmDkJAQxMbG4sSJEwgLC8PQoUMBKGZ78vPzAQBdunRBdnY2Fi9ejKSkJCxevBh5eXno2rUrAGDw4MHYvHkzzp49i8TERMyYMQM1atRAq1at9PX2iIiIqBzR64kHZ8+eDS8vLwwbNgyhoaGYMmUKOnXqBAAICgrC4cOHAQCWlpZYv349IiMjERwcjJiYGGzYsAEWFhYAFIFn9OjRCAkJQb9+/SCRSLB27doSM0VERET0ftLbGh5AMcuzbNkyLFu2rMRjN27cULnv6+uLAwcOvLIfAwMDjB07FmPHjtXKOImIiKhi4xQIERERiR4DDxEREYkeAw8RERGJHgMPERERiR4DDxEREYkeAw8RERGJHgMPERERiR4DDxEREYkeAw8RERGJHgMPERERiR4DDxEREYkeAw8RERGJHgMPERERiR4DDxEREYkeAw8RERGJ3jsFHplMhjNnzmDLli3Izs5GTEwMcnJyND02IiIiIo0wUvcJjx49wqhRo5CVlYWnT5+iffv22LRpE6KiorB582Z4enpqY5xERERE70ztGZ4FCxYgICAA58+fh4mJCQDgu+++wwcffIBFixZpfIBEREREZaV24Ll8+TJGjhwJQ0ND5TZjY2NMnDgR8fHxGh0cERERkSaoHXjMzMyQnp5eYvudO3dgaWmpkUERERERaZLagWfgwIH46quvcObMGQCKoLNv3z7MmzcP/fr10/T4iIiIiMpM7UXLkyZNgrW1NUJCQpCXl4exY8fC3t4ew4cPx6hRo7QxRiIiIqIyUTvwAMCQIUMwZMgQ5ObmQiaTwcrKStPjIiIiItIYtQPPwYMH3/h4nz593nEoRERERNqhduBZsWKFyn2ZTIb09HQYGRnB19eXgYeIiIjKHbUDz6lTp0pse/78Ob766iuedJCIiIjKJY1cS6tSpUqYMmUKfvrpJ010R0RERKRRGrt4aEJCAuRyuaa6IyIiItIYtXdpDRkyBBKJRGXb8+fPcePGDQwfPlxT4yIiIiLSGLUDT9OmTUtsMzExwfTp09G8eXONDIqIiIhIk9QOPJMnT9bGOIiIiIi0plSBZ/bs2aXucOnSpe88GCIiIiJt0NiiZSIiIqLyqlQzPJy1ISIioopM7TU8giDg5MmTSExMhEwmU24vLCzEtWvXsGnTJo0OkIiIiKis1A48CxcuxN69e9GgQQPExsbC398f9+/fR1paGj7++GNtjJGIiIioTNRew3P48GF888032LVrF2rUqIGQkBCcPn0a3bt3R1FRkTbGSERERFQmageeZ8+ewdvbGwBQt25dxMbGwsjICOPGjcPZs2c1PkAiIiKislI78Li6uuLatWsAgDp16iA2NhaAYm1PTk6OZkdHREREpAFqr+EZOXIkpk+fjiVLlqBbt24IDg6GkZERoqKiEBAQoI0xEhEREZVJqWZ4Nm/ejJSUFABA//79sXHjRri5ucHd3R2rVq1CamoqvL29efg6ERERlUulmuHZs2cPvv32WwQEBKBnz57o3LkzbGxsAAAtW7ZEy5YttTpIIiIiorIo1QzP0aNHsXfvXvj7+2PTpk0ICgrChAkTcPjwYRQUFGh7jERERERlUupFyw0aNMDnn3+OY8eOYefOnXB3d8d3332H5s2bY8aMGTh79qzKiQiJiIiIyot3upaWt7c3pk+fjhMnTmDr1q2oWrUqli1bxl1bREREVC6V6eKhGRkZSEhIQEJCAh49egQ3NzdNjYuIiIhIY9Q+LD0lJQXHjh3DsWPHcOXKFdSpUwfdunXD/PnzUbVqVW2MkYiIiKhMShV47t+/rww58fHxqFatGrp374758+fDw8ND22MkIiIiKpNSBZ5OnTrB0dERXbp0wb/+9S/4+vpqe1xEREREGlOqwPPTTz+hadOmMDAo05IfIiIiIr0oVeBp3ry5tsdBREREpDWcsiEiIiLRY+AhIiIi0dNI4MnIyIAgCJroioiIiEjj1A48KSkp+Oyzz3D9+nUUFBTgk08+QYsWLdCuXTskJCRoY4xEREREZaJ24AkJCUFGRgYqV66M/fv34+bNm9i1axfatWuHhQsXamOMRERERGWi9pmWL168iP3798PFxQUnTpxA+/bt4efnBzs7O/To0UMbYyQiIiIqE7VneExNTVFQUICnT58iIiICbdq0AQA8fPgQNjY2mh4fERERUZmpPcPToUMHfPrppzAzM4ONjQ3atGmDw4cPY8mSJfjwww+1MUYiIiKiMlE78ISEhGD79u34+++/MWDAAJiamqKwsBDjx4/H4MGDtTFGIiIiojJRO/AYGRlh+PDhyvsFBQWoXbs2atWqBYlEosmxEREREWmE2mt4kpKS8NFHH+HKlSvIzs5Gnz598NFHH6FVq1a4ePGiNsZIREREVCZqB57Q0FC4urqiZs2a2Lt3L3JycnDhwgWMHz8ey5Yt08YYiYiIiMpE7cATGxuLTz/9FHZ2djhx4gQ6duwIBwcH9OjRA7dv39bGGImIiIjKRO3AY2VlhbS0NDx69AjR0dHKw9KvX78Oe3t7TY+PiIiIqMzUDjzBwcGYMGECBgwYgOrVqyMoKAg7d+7EjBkzMHToULX6KigowJw5cxAYGIigoCCEhYW9tu21a9fQv39/+Pn5oW/fvoiPj1c+JggCNmzYgHbt2qFRo0YYNmwYkpKS1H1rREREJFJqB57PP/8cCxcuxMiRI/Gf//wHhoaGqFq1Kr777juVo7dKY/ny5YiPj8fWrVsxf/58rFq1CkeOHCnRLjc3F2PHjkVgYCD2798Pf39/jBs3Drm5uQCAXbt2ISwsDPPmzcO+fftQvXp1jBkzBnl5eeq+PSIiIhKhd7paeseOHdGvXz88efIEhYWFCAgIQNu2bdXqIzc3F+Hh4Zg7dy68vLzQsWNHjB49Gjt27CjR9vDhwzA1NcXMmTPh7u6OuXPnolKlSspwdODAAYwcORJt27ZFrVq1EBISgqysLFy5cuVd3h4RERGJjNqBp6CgAP/617/QpEkT9OvXDykpKZg1axZGjRqFp0+flrqfhIQESKVS+Pv7K7cFBAQgJiYGcrlcpW1MTAwCAgKU5/mRSCRo1KgRoqOjAQAzZ85Er169lO0lEgkEQUBOTo66b4+IiIhESO0TD3799ddISkrCgQMHMHDgQADAlClTMHv2bCxatAhff/11qfpJTU2Fra0tTExMlNscHBxQUFCArKws2NnZqbT18PBQeb69vT0SExMBAIGBgSqPhYeHQyqVIiAgQK33JpPJ1GqvTp/a6JteYJ11g3XWDdZZd1hr3dBWndXpT+3Ac+zYMaxevRqenp7KbZ6ensp1PaWVl5enEnYAKO8XFhaWqu3L7QDFbNCyZcswatQoODo6lno8ABAXF6dW+/LSN73AOusG66wbrLPusNa6oc86qx14nj9/DnNz8xLb5XK5Wkmr+Bpc/1R838zMrFRtX24XFRWFMWPGoFWrVpg2bVqpx1LMx8cHhoaGaj/vTWQyGeLi4rTSN73AOusG66wbrLPusNa6oa06F/dbGmoHnnbt2uH7779XOavygwcPsGjRIrRu3brU/Tg7OyMzMxNSqRRGRophpKamwszMDNbW1iXapqWlqWxLS0uDk5OT8n5ERATGjx+PFi1a4Ntvv4WBgfrrsQ0NDbX2gddm3/QC66wbrLNusM66w1rrhj7rrHYq+Oqrr2BgYIAmTZogLy8Pffv2RadOnWBtbY158+aVup/69evDyMhIufAYACIjI+Hj41MirPj5+SEqKgqCIABQnHfnypUr8PPzAwDcvHkTEyZMQMuWLfHDDz/A2NhY3bdFREREIqb2DI+VlRVWrlyJ+/fv4/bt25BKpahVqxbc3d3V6sfc3Bx9+vRBSEgIlixZgidPniAsLAxLly4FoJjtsbKygpmZGbp06YJvv/0WixcvxsCBA7Fr1y7k5eWha9euABQhzMXFBbNnz0ZmZqbKWF/e7UVERETvn3c6Dw+gCCyenp5o0KABzM3NkZycjOTkZLX6mD17Nry8vDBs2DCEhoZiypQp6NSpEwAgKCgIhw8fBgBYWlpi/fr1iIyMRHBwMGJiYrBhwwZYWFggNTUVUVFRSEpKQps2bRAUFKS8FT+fiIiI3m9qz/BcuHABX331FR49eqSyXRAESCQSXL9+vdR9mZubY9myZa+8yvqNGzdU7vv6+uLAgQMl2jk6OpZoS0RERPRPageehQsXwtfXF2vXroWlpaU2xkRERESkUWoHnsePH2PTpk1wdXXVxniIiIiINE7tNTyBgYGIjIzUxliIiIiItELtGZ7GjRsjNDQUZ86cgZubW4lDwCdPnqyxwRERERFpgtqB57///S+8vb2Rnp6O9PR0lceKL+5JREREVJ6oHXiWLVuGKlWqlDg5oEwmQ0JCgsYGRkRERKQpaq/had++PbKyskpsf/jwIQYNGqSJMRERERFpVKlmeMLDw7Fu3ToAivPt9O3bt8QMT3Z2ttpnWyYiIiLShVIFnj59+sDY2BhyuRxz5szBiBEjYGVlpXxcIpHA3NwczZo109pAiYiIiN5VqQKPsbEx+vTpAwCoXr06GjVqpLzCOREREVF5V6rUsmrVKowaNQrm5ua4dOkSLl269Nq2PCydiIiIyptSBZ6IiAgMHToU5ubmiIiIeG07HpZORERE5VGpAs+2bdte+TURERFRRVCqw9IHDx6M7OxslW35+flaGRARERGRppUq8ERGRqKoqEhl2wcffIAHDx5oZVBEREREmqT2iQeLCYKgyXEQERERac07Bx4iIiKiioKBh4iIiESv1GcP/P3332Fpaam8L5fLcfz4cdjZ2am0Kz5BIREREVF5UarAU7VqVYSFhalss7e3x/bt21W2SSQSBh4iIiIqd0oVeE6dOqXtcRARERFpDdfwEBERkegx8BAREZHoMfAQERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwEBERkejpNfAUFBRgzpw5CAwMRFBQEMLCwl7b9tq1a+jfvz/8/PzQt29fxMfHv7Ld2rVrMWvWLG0NmYiIiCogvQae5cuXIz4+Hlu3bsX8+fOxatUqHDlypES73NxcjB07FoGBgdi/fz/8/f0xbtw45ObmqrT77bffsHLlSl0Nn4iIiCoIvQWe3NxchIeHY+7cufDy8kLHjh0xevRo7Nixo0Tbw4cPw9TUFDNnzoS7uzvmzp2LSpUqKcORVCrF/PnzMWfOHLi6uur6rRAREVE5p7fAk5CQAKlUCn9/f+W2gIAAxMTEQC6Xq7SNiYlBQEAAJBIJAEAikaBRo0aIjo4GoAhPN27cwJ49e1T6IyIiIgIAI329cGpqKmxtbWFiYqLc5uDggIKCAmRlZcHOzk6lrYeHh8rz7e3tkZiYCACwtrbGrl27yjwmmUxW5j5e16c2+qYXWGfdYJ11g3XWHdZaN7RVZ3X601vgycvLUwk7AJT3CwsLS9X25XZlFRcXp9H+dNU3vcA66wbrrBuss+6w1rqhzzrrLfCYmpqWCCzF983MzErV9uV2ZeXj4wNDQ0ON9imTyRAXF6eVvukF1lk3WGfdYJ11h7XWDW3Vubjf0tBb4HF2dkZmZiakUimMjBTDSE1NhZmZGaytrUu0TUtLU9mWlpYGJycnjY7J0NBQax94bfZNL7DOusE66wbrrDustW7os856W7Rcv359GBkZKRceA0BkZCR8fHxgYKA6LD8/P0RFRUEQBACAIAi4cuUK/Pz8dDlkIiIiqqD0FnjMzc3Rp08fhISEIDY2FidOnEBYWBiGDh0KQDHbk5+fDwDo0qULsrOzsXjxYiQlJWHx4sXIy8tD165d9TV8IiIiqkD0euLB2bNnw8vLC8OGDUNoaCimTJmCTp06AQCCgoJw+PBhAIClpSXWr1+PyMhIBAcHIyYmBhs2bICFhYU+h09EREQVhN7W8ACKWZ5ly5Zh2bJlJR67ceOGyn1fX18cOHDgrX3+3//9n8bGR0REROLAi4cSERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwaNmffwI3b5rrexhERETvNSN9D0DMpFKgbVsDGBt7ok0bwMVF3yMiIiJ6P3GGR4uMjAB/fyAvzxA//ijR93CIiIjeWww8WjZ7thwAsHatBFlZ+h0LERHR+4qBR8t69ADc3fOQnS3B6tX6Hg0REdH7iYFHywwMgBEjHgEAfvgBeP5cv+MhIiJ6HzHw6ECHDpmoXVtAWhqwcaO+R0NERPT+YeDRASMjYOZMAQDwzTdAQYGeB0RERPSeYeDRkSFDBFSrBvz9N/Dvf+t7NERERO8XBh4dMTUFpk9XfL1smeIcPURERKQbDDw6NGYM4OAA3LoFhIfrezRERETvDwYeHapUCfj0U8XXS5YAcrleh0NERPTeYODRsUmTACsrID4e+O03fY+GiIjo/cDAo2OVKytCDwAsXgwIgl6HQ0RE9F5g4NGDzz4DzMyAS5eAU6f0PRoiIiqL1FTgwAHg8WN9j4TehIFHD5ycFAuYAcVaHiIiqjgEAbhyBVi4EGjWDHB2BoKDgebNgUeP9D06eh0GHj2ZMUNxQsJTp4CLF/U9GiIiepOcHMUszujRQLVqQEAA8NVXQESEIgBVqgTcvQt07Qo8farv0dKrGOl7AO8rV1dg6FAgLEwxy/PLL/oeERFRxRARoTifmSaChSAY4NmzOrC0NIBE8uo2+fnAX38BRUUvtlWqBHTsCHTvrgg5BQXABx8AMTFAnz7AkSOK869R+cHAo0dffgls2QL8+isQGwv4+up7RERE5dfz58C8eYoLMWvugA8JAOtStfTwUASc7t2BVq1KBprffwdatwbOnAGGDAF27gQMDTU1TiorBh49qlsX6N8f2L0bGDVK8dfBm3h4AIMHA3Z2uhlfeSGVAoWFgIWFvkdCRPpy6pRi7ePt24r7n3yiCB5lJZfLcffuXdSsWRMGBq9e5SGRAP7+ip/Zb+LvDxw8CHTpoji5rLMzsGIFXjtzRLrFwKNns2crAs/ly4rb28yYoQhJY8cCQUHi/kYqKADWrQMWLQKysoDevYEJE4B27cT9vonohawsxc+9TZsU911dgfXrFbuRNEEmExAdnYmGDd00MhvTrh2wbRswcCCwahXg4gLMmVP2fqnsGHj0zM8P2LcPiIx8czuZDDh6FIiOBrZvV9zq11cEn6FDxTXrI5MBO3YoFgTeu/di+759ilvdusD48cDw4YCtrd6GSURa9ssvij9ykpMV9ydOBJYuBaxLtwdKbwYMUByi/umnwNy5QJUqwMiR+h4V6TXwFBQUIDQ0FMeOHYOZmRlGjhyJka/5VFy7dg3z58/HzZs34eHhgdDQUHh7eysf/+233/DDDz8gNTUVQUFBWLhwIewqSAoIDlbc3mbpUsUs0Pr1in3D168rzukza5Y4Zn0EQbGeac4c4OpVxbaqVYGQECAwENi4UfGX082bwOefK9oNHKj4gdiokV6HThXY8+eKWdYTJ95+UV9DQ8DRUbGrokoV1X+dnLhIVVOePAGmTlX8vwBAnTqKGZ5WrfQ7LnVMm6Y4RH3ZMsXPZicnoEcPfY/q/SYRBP2d63fhwoX466+/sHTpUiQnJ+PLL7/EkiVL0KVLF5V2ubm56NSpE3r27Il+/fph586d+P3333H8+HFYWFggNjYWQ4YMQWhoKOrVq4fFixfDwsIC69evL9U4ZDIZoqOj0bBhQxhqeIWZtvrOzlbMgqxfrzgqoJi5OfCa3dBqsbUFfHwUC6l9fBS3evUAE5Oy9/0q588rgtsff7x4/dmzgcmTFe+pWE6O4n2vXatY6F2sUSMBTZoko2ZNl9fuh6/oJJK3397EyEgxE2hvr3pTZ22UNr9XdEkQFLOqGzcq/njIydFMv7a2igsEG73lT0lBeHGTy1XvK7YJKCwshImJCRSLat/MwKDkZ+HlbaUdz6vGpOvr/qWnK4KogQEwfbrij55//hzQJG1+pgVBMRP9738rxn/ypOJcPe8jbdVZnX71Fnhyc3PRrFkzbNy4EU2bNgUArFmzBn/++Se2bdum0nbv3r1Yu3YtTpw4AYlEAkEQ0LlzZ4wfPx7BwcGYOXMmDAwM8H//938AgEePHqFt27Y4fvw4XF1d3zqWihh4igmC4nDJDRsUP7hzczX+EkpGRorQUxyEXF1LN5v0pjZyOfCf/wCHDyvum5srpoFnzlRchuN1BEFx/qK1a4E9exTrfejdmJm9CD9VqgDe3or/X19fxW5TM7MXbSt64MnKUgTmjRtV/1Bwd1csgnVwePPzi4oUZ9VNSVHsskhJeXH75yHLVHa+vsDmzYrZXW3S9me6qEix/vD33xV/cCxdqr0/HF/+WVueZvsFQQ4Hh3h06eKlt8Cjt11aCQkJkEql8Pf3V24LCAjAunXrIJfLVf5Kj4mJQUBAACT/+9+TSCRo1KgRoqOjERwcjJiYGIwpPnUxABcXF1StWhUxMTGlCjwVmUQCNGmiuP34o+IHb1kJguKHeVycYhYlLk5xe/pUcdHT+HhFuNIkQ0PFERjz5il2Y72NRKL4S6l5c+D774GffpLj7NkM2NnZQSIR3wzPm2YDim9vU1gIZGQo/nouvkmlinOM/P234hYbCxw79uI5hoaKkFscgLy8ALncBLa2Fetw27t3Fb88w8MV7xdQ7H7q21dxIrnWrcs2MyoIQGam4vsmLa10MyKvmoUp3iaXy5CYeBN169Z96w/xN88Ulf7w7dfNDP3zvq4YGyuC99tmyioCY2PF565dO8XlhMaN0/eI9MUA3t618dIOHJ3S28cpNTUVtra2/5uyVXBwcEBBQQGysrJU1t+kpqbCw8ND5fn29vZITEwEADx58gROTk4lHn+s5oVNZDKZum+j1H1qo++XmZkBbm6a6atmTcUp04sJAvDggSL4xMdLEBcHpKaq/gR81Q/W0vywrVVLwIwZAurUUdxXt1SVKwNTp8rQtu09+PhYV8iZB30QBMWunH8GoIcPJf8LuhLExgJZWRJcvapYU6UIuYYAfPQ88rLx9hYwapSAwYMF5WJ/QVD/c/cyGxvFzdOz7GOUyWQwMcmFj4+sQgVLTdPBj02d/Iw2M1MswP7qKwnu39dOcnz5Z235uzC1gJYtUyGTVdNor+r8v+kt8OTl5amEHQDK+4WFhaVqW9wuPz//jY+XVlxcnFrty0vfulStmuLWubNm+33+XHEEWlmJpc66ZmSkWHjr7Kw4ZT6g+IGZkmKMpCRzJCZaIDHRHImJ5khOrngrc83M5GjTJgsffpgKL69cSCTA/fuKW3nGz7Pu6KLWY8dq/SXKvbi4DL29tt4Cj6mpaYlAUnzf7J+LBt7Qtrjd6x43V3OVm4+Pj1bW8MTFxWmlb3qBddYNRZ2jKmCdJQBs/3cr//h51h3WWje0VefifktDb4HH2dkZmZmZkEqlMPrfjtrU1FSYmZnB+qWTLDg7OyMtLU1lW1pamnI31used3R0VGtMhoaGWvvAa7NveoF11g3WWTdYZ91hrXVDn3XW2+rO+vXrw8jICNH/2I8RGRkJHx+fEocV+/n5ISoqCsUHlAmCgCtXrsDPz0/5eOQ/ztz36NEjPHr0SPk4ERERvd/0FnjMzc3Rp08fhISEIDY2FidOnEBYWBiGDh0KQDHbk/+/wym6dOmC7OxsLF68GElJSVi8eDHy8vLQ9X/nFv/444/x888/Izw8HAkJCZg5cybatGkj+iO0iIiIqHT0evzu7Nmz4eXlhWHDhiE0NBRTpkxBp06dAABBQUE4/L+Ts1haWmL9+vWIjIxUHoa+YcMGWPzvjGn+/v5YsGABVq9ejY8//hg2NjZYunSp3t4XERERlS96PcuBubk5li1bhmXLlpV47MaNGyr3fX19ceDAgdf2FRwcjODSXJ+BiIiI3jviO0MbERER0UsYeIiIiEj0GHiIiIhI9Bh4iIiISPQYeIiIiEj0GHiIiIhI9Bh4iIiISPQYeIiIiEj0GHiIiIhI9PR6puXyoviipDKZTON9F/epjb7pBdZZN1hn3WCddYe11g1t1bm4v+Lf428iEUrTSuQKCwsRFxen72EQERHRO/Dx8YGJickb2zDwAJDL5ZBKpTAwMIBEItH3cIiIiKgUBEGAXC6HkZERDAzevEqHgYeIiIhEj4uWiYiISPQYeIiIiEj0GHiIiIhI9Bh4iIiISPQYeIiIiEj0GHiIiIhI9Bh4iIiISPQYeLSooKAAc+bMQWBgIIKCghAWFqbvIYlKYWEhevTogYiICOW2Bw8eYPjw4WjYsCG6deuGCxcu6HGEFVtKSgqmTp2KJk2aoGXLlli6dCkKCgoAsM6adO/ePYwaNQr+/v5o06YNNm3apHyMddaOsWPHYtasWcr7165dQ//+/eHn54e+ffsiPj5ej6Or+I4fPw5PT0+V29SpUwHot9YMPFq0fPlyxMfHY+vWrZg/fz5WrVqFI0eO6HtYolBQUIDPP/8ciYmJym2CIGDSpElwcHDAvn370Lt3b0yePBnJycl6HGnFJAgCpk6diry8POzYsQPff/89Tp8+jR9++IF11iC5XI6xY8fC1tYWBw4cQGhoKNauXYtff/2VddaSQ4cO4ezZs8r7ubm5GDt2LAIDA7F//374+/tj3LhxyM3N1eMoK7akpCS0bdsWFy5cUN4WLVqk/1oLpBXPnz8XfHx8hIsXLyq3rV69Wvjkk0/0OCpxSExMFHr16iX07NlTqFu3rrLGf/zxh9CwYUPh+fPnyrbDhg0TVqxYoa+hVlhJSUlC3bp1hdTUVOW2X3/9VQgKCmKdNSglJUWYNm2akJOTo9w2adIkYf78+ayzFmRmZgqtWrUS+vbtK3z55ZeCIAhCeHi40K5dO0EulwuCIAhyuVzo2LGjsG/fPn0OtUL74osvhG+//bbEdn3XmjM8WpKQkACpVAp/f3/ltoCAAMTExEAul+txZBXfpUuX0LRpU+zevVtle0xMDBo0aAALCwvltoCAAERHR+t4hBWfo6MjNm3aBAcHB5Xtz549Y501yMnJCT/88AMsLS0hCAIiIyPx119/oUmTJqyzFixbtgy9e/eGh4eHcltMTAwCAgKU11GUSCRo1KgR61wGt27dQs2aNUts13etGXi0JDU1Fba2tipXb3VwcEBBQQGysrL0NzARGDRoEObMmQNzc3OV7ampqXByclLZZm9vj8ePH+tyeKJgbW2Nli1bKu/L5XJs374dzZo1Y521pF27dhg0aBD8/f3RuXNn1lnD/vzzT1y+fBkTJ05U2c46a5YgCLhz5w4uXLiAzp07o0OHDvjmm29QWFio91ob6eRV3kN5eXklLlVffL+wsFAfQxK919Wc9S67r7/+GteuXcPevXuxZcsW1lkLVqxYgbS0NISEhGDp0qX8PGtQQUEB5s+fj6+++gpmZmYqj7HOmpWcnKys6Q8//ICHDx9i0aJFyM/P13utGXi0xNTUtMR/YvH9l7/hSDNMTU1LzJ4VFhay3mX09ddfY+vWrfj+++9Rt25d1llLfHx8ACh+OU+fPh19+/ZFXl6eShvW+d2sWrUK3t7eKrOWxV73s5p1fjfVqlVDREQEbGxsIJFIUL9+fcjlcsyYMQNNmjTRa60ZeLTE2dkZmZmZkEqlMDJSlDk1NRVmZmawtrbW8+jEydnZGUlJSSrb0tLSSkyhUuktXLgQO3fuxNdff43OnTsDYJ01KS0tDdHR0ejQoYNym4eHB4qKiuDo6Ijbt2+XaM86q+/QoUNIS0tTrqks/qV79OhR9OjRA2lpaSrtWeeyqVy5ssp9d3d3FBQUwNHRUa+15hoeLalfvz6MjIxUFmNFRkbCx8cHBgYsuzb4+fnh6tWryM/PV26LjIyEn5+fHkdVca1atQq7du3Cd999h+7duyu3s86a8/DhQ0yePBkpKSnKbfHx8bCzs0NAQADrrCHbtm3Dr7/+ioMHD+LgwYNo164d2rVrh4MHD8LPzw9RUVEQBAGAYg3KlStXWOd3dP78eTRt2lRldvL69euoXLkyAgIC9Fpr/ubVEnNzc/Tp0wchISGIjY3FiRMnEBYWhqFDh+p7aKLVpEkTuLi4YPbs2UhMTMSGDRsQGxuLfv366XtoFc6tW7ewZs0ajBkzBgEBAUhNTVXeWGfN8fHxgZeXF+bMmYOkpCScPXsWX3/9NcaPH886a1C1atXg5uamvFWqVAmVKlWCm5sbunTpguzsbCxevBhJSUlYvHgx8vLy0LVrV30Pu0Ly9/eHqakp/vWvf+H27ds4e/Ysli9fjtGjR+u/1jo5+P09lZubK8ycOVNo2LChEBQUJPz000/6HpLo/PM8PIIgCHfv3hUGDx4seHt7C927dxf++9//6nF0Fdf69euFunXrvvImCKyzJj1+/FiYNGmS0KhRI6FFixbC2rVrlecpYZ2148svv1Seh0cQBCEmJkbo06eP4OPjI/Tr10+4evWqHkdX8d28eVMYPny40LBhQ6FFixbCypUrlZ9pfdZaIgj/m1siIiIiEinu0iIiIiLRY+AhIiIi0WPgISIiItFj4CEiIiLRY+AhIiIi0WPgISIiItFj4CEiIiLRY+AhIp3Izc3FDz/8gC5dusDX1xdNmzbF1KlTkZiYqO+hvdKzZ89w8OBB5f127dph//79+hsQEZUJTzxIRFr3/PlzDBo0CLm5uZg1axbq1auHzMxM7NixA8eOHcPBgwfh6uqq72GqWLVqFSIiIrBt2zYAQEZGBiwsLHgVbaIKildLJyKtW716NdLT03H48GFYW1sDUFzfaOnSpXj06BG2bNmCefPm6XmUql7+W9DOzk5PIyEiTeAuLSLSKrlcjgMHDmDEiBHKsPNPy5cvx4wZMwAAly9fRnBwMHx9fdGzZ08cPXpU2W7WrFlYunQpPv30U/j5+aF169Yqu5wKCwuxaNEiNG3aFE2bNsX06dORlZUFQHFVck9PT6xevRqNGzfGggULIAgC1q1bh3bt2sHb2xtBQUFYtWoVAGD//v1YtWoVLl26BE9PTwCqu7Tkcjk2bdqE9u3bw9fXF0OGDMGNGzeUY/H09MTPP/+MHj16wNvbG4MGDcKDBw80WlciUg8DDxFp1f3795GRkYHAwMBXPu7k5AQzMzOkpqZi3LhxCA4Oxq+//orRo0dj1qxZuHz5srLtjh074OXlhd9++w2dOnXC/PnzkZOTAwD47rvvEB8fj40bN+Lf//43nj17hmnTpqm81pUrV7Bv3z4MHToUBw8exNatW7F48WIcOXIEkyZNwsqVK3H16lV069YNI0eOhL+/Py5cuFBizKtXr0ZYWBjmzJmDAwcOoFq1ahg9ejRyc3OVbVauXIm5c+di//79yMzMxA8//KCBahLRu2LgISKtyszMBADY2Ngot/3xxx/w9/dX3rp3744dO3bggw8+wCeffAI3Nzf07t0bAwYMwNatW5XP8/T0xJgxY+Dq6opp06YhPz8fiYmJyMvLw/bt2xEaGgpfX194enpi+fLluHTpksrMy7Bhw1CjRg3UrFkTLi4uWLp0KZo3b47q1avj448/hqOjIxITE2FmZgYLCwsYGxvD0dFR5f0IgoDt27dj2rRpaN++Pdzd3bFw4UIYGhril19+UbYbMWIEmjdvjrp16+Ljjz9GfHy8tkpMRKXANTxEpFXFu7Gys7OV2/z9/ZW7o44dO4adO3fi9u3bOH36NPz9/ZXtioqKUKtWLeX9mjVrKr+2tLQEAEilUjx48ABFRUUYOHCgymvL5XLcvXsXXl5eABTrhoo1a9YMMTEx+Pbbb3Hr1i1cv34dqampkMvlb3w/6enpyMrKgp+fn3KbsbExvL29cevWLeU2Nzc3lbEWFRW9sV8i0i4GHiLSKjc3N1SuXBlRUVHw9fUFAJibmysDgb29PQBFcOnZsyfGjx+v8nwjoxc/poyNjUv0LwgCZDIZAOA///kPLCwsVB63t7dXruUxNTVVbg8PD8eSJUvQv39/dOrUCV9++SWGDh361vfzzz7+SSaTqYSlV42ViPSHu7SISKuMjIzQt29fbN26Fc+ePSvxeEpKCgCgVq1auHfvHtzc3JS3kydP4tdff33ra7i6usLQ0BBZWVnK51paWmLp0qVIT09/5XN27tyJSZMmYc6cOejTpw9sbW2Rnp6uPDpLIpG88nlWVlZwcHBAdHS0cltRURGuXr2qMhtFROULAw8Rad2UKVPg6OiIgQMH4siRI3jw4AFiY2Mxb948rFixAgEBARg0aBDi4+Px/fff4+7du/j111/x3XffoWrVqm/t39LSEv3790dISAgiIiKQlJSEmTNn4t69e6hevforn2Nra4s///wTd+7cQXx8PD777DMUFRWhsLAQgGIW6smTJ3j48GGJ5w4fPhwrVqzAqVOncOvWLcybNw8FBQXo1q1b2QpFRFrDXVpEpHXm5ubYtm0btm7dijVr1uDevXswMTGBr68vVq5ciQ4dOgAA1q1bh2+++QabN2+Gs7MzZs2ahV69epXqNWbNmoVly5Zh6tSpKCoqQuPGjbFhwwYYGhq+sv2cOXMwZ84c9O7dG/b29ujatSvMzc1x/fp1AEDHjh2xa9cudO/eHadOnVJ57siRI/Hs2TPMmzcPz549g7+/P7Zt28Zz9RCVYzzTMhEREYked2kRERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwEBERkegx8BAREZHoMfAQERGR6DHwEBERkegx8BAREZHo/T9nZOheVOqDSwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GA started, 9 individual(s) will be generated\n",
      "gen\tnevals\tmin        \tavg      \n",
      "0  \t150   \t0.000380675\t0.0374592\n",
      "1  \t85    \t0.000380675\t0.0374895\n",
      "2  \t97    \t0.000380675\t0.03739  \n",
      "3  \t95    \t9.20917e-05\t0.037684 \n",
      "4  \t100   \t1.22571e-05\t0.0362589\n",
      "5  \t89    \t1.22571e-05\t0.0363643\n",
      "6  \t71    \t1.22571e-05\t0.0366248\n",
      "7  \t85    \t9.20917e-05\t0.0367572\n",
      "8  \t92    \t9.20917e-05\t0.0370898\n",
      "9  \t88    \t5.51128e-05\t0.0367666\n",
      "10 \t94    \t5.51128e-05\t0.0364032\n",
      "11 \t65    \t5.51128e-05\t0.0368633\n",
      "12 \t87    \t6.37486e-07\t0.0366738\n",
      "13 \t82    \t6.37486e-07\t0.0369005\n",
      "14 \t97    \t6.37486e-07\t0.0369704\n",
      "15 \t81    \t6.37486e-07\t0.0372756\n",
      "16 \t96    \t2.00957e-05\t0.0373712\n",
      "17 \t94    \t2.00957e-05\t0.0374341\n",
      "18 \t77    \t2.00957e-05\t0.0377524\n",
      "19 \t85    \t2.00957e-05\t0.0375957\n",
      "20 \t89    \t2.00957e-05\t0.0375099\n",
      "21 \t87    \t2.00957e-05\t0.0376837\n",
      "22 \t104   \t2.00957e-05\t0.0375789\n",
      "23 \t86    \t2.00957e-05\t0.0378318\n",
      "24 \t88    \t2.00957e-05\t0.0374635\n",
      "25 \t95    \t9.57321e-06\t0.0377111\n",
      "26 \t73    \t9.57321e-06\t0.0378242\n",
      "27 \t89    \t2.00957e-05\t0.0380966\n",
      "28 \t89    \t3.56267e-08\t0.0377213\n",
      "29 \t85    \t3.56267e-08\t0.0374922\n",
      "30 \t83    \t3.56267e-08\t0.0379952\n",
      "31 \t83    \t2.00957e-05\t0.038067 \n",
      "32 \t85    \t2.00957e-05\t0.0378038\n",
      "33 \t85    \t2.00957e-05\t0.0374594\n",
      "34 \t72    \t2.00957e-05\t0.0375175\n",
      "35 \t77    \t2.00957e-05\t0.0375911\n",
      "36 \t86    \t2.00957e-05\t0.0370972\n",
      "37 \t82    \t9.57321e-06\t0.0373832\n",
      "38 \t86    \t9.57321e-06\t0.0377466\n",
      "39 \t83    \t9.57321e-06\t0.0382078\n",
      "40 \t87    \t9.57321e-06\t0.0381134\n",
      "41 \t91    \t9.57321e-06\t0.0386327\n",
      "42 \t92    \t3.21626e-05\t0.0386683\n",
      "43 \t92    \t0.000191474\t0.0386481\n",
      "44 \t84    \t0.000191474\t0.038978 \n",
      "45 \t83    \t0.000191474\t0.037865 \n",
      "46 \t90    \t0.000310472\t0.0374023\n",
      "47 \t81    \t8.78093e-06\t0.0377716\n",
      "48 \t71    \t8.78093e-06\t0.0380837\n",
      "49 \t82    \t8.78093e-06\t0.0385518\n",
      "50 \t92    \t2.10797e-10\t0.0389107\n",
      "\n",
      "#1\n",
      "Constraints: ((9, 1), (0, 0.0), (1, 1.0))\n",
      "Individual: [[0.0, 1.0, 2.0, 1.0, 0.0, 1.0, 3104.0, 24.0, 5.0, 1]]\n",
      "Fitness: (2.1079682444025138e-10,)\n",
      "\n",
      "#2\n",
      "Constraints: ((9, 1), (0, 0.0), (1, 1.0))\n",
      "Individual: [[0.0, 1.0, 2.0, 1.0, 0.0, 1.0, 3092.0, 24.0, 5.0, 1]]\n",
      "Fitness: (3.5626738559280113e-08,)\n",
      "\n",
      "#3\n",
      "Constraints: ((9, 1), (0, 0.0), (1, 1.0))\n",
      "Individual: [[0.0, 1.0, 2.0, 1.0, 0.0, 1.0, 3160.0, 24.0, 5.0, 1]]\n",
      "Fitness: (6.374862023261763e-07,)\n",
      "\n",
      "#4\n",
      "Constraints: ((9, 1), (0, 0.0), (1, 1.0))\n",
      "Individual: [[0.0, 1.0, 2.0, 1.0, 0.0, 1.0, 2901.0, 24.0, 5.0, 1]]\n",
      "Fitness: (8.78093095579846e-06,)\n",
      "\n",
      "#5\n",
      "Constraints: ((9, 1), (0, 0.0), (1, 1.0))\n",
      "Individual: [[0.0, 1.0, 2.0, 1.0, 0.0, 1.0, 2892.0, 24.0, 5.0, 1]]\n",
      "Fitness: (9.573205051682798e-06,)\n",
      "\n",
      "#6\n",
      "Constraints: ((9, 1), (0, 0.0), (1, 1.0))\n",
      "Individual: [[0.0, 1.0, 2.0, 1.0, 0.0, 1.0, 2864.0, 24.0, 5.0, 1]]\n",
      "Fitness: (1.225710000779534e-05,)\n",
      "\n",
      "#7\n",
      "Constraints: ((9, 1), (0, 0.0), (1, 1.0))\n",
      "Individual: [[0.0, 1.0, 2.0, 1.0, 0.0, 1.0, 3414.0, 24.0, 5.0, 1]]\n",
      "Fitness: (2.0095668210484696e-05,)\n",
      "\n",
      "#8\n",
      "Constraints: ((9, 1), (0, 0.0), (1, 1.0))\n",
      "Individual: [[0.0, 1.0, 2.0, 1.0, 0.0, 1.0, 3496.0, 24.0, 5.0, 1]]\n",
      "Fitness: (3.216257255739263e-05,)\n",
      "\n",
      "#9\n",
      "Constraints: ((9, 1), (0, 0.0), (1, 1.0))\n",
      "Individual: [[0.0, 1.0, 2.0, 1.0, 0.0, 1.0, 3617.0, 24.0, 5.0, 1]]\n",
      "Fitness: (5.511284020204288e-05,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GA started, 9 individual(s) will be generated\n",
      "gen\tnevals\tmin       \tavg      \n",
      "0  \t150   \t0.00466179\t0.0705898\n",
      "1  \t77    \t0.00163063\t0.0689534\n",
      "2  \t90    \t0.000295281\t0.0692228\n",
      "3  \t100   \t5.21048e-06\t0.0703538\n",
      "4  \t88    \t5.21048e-06\t0.0697982\n",
      "5  \t89    \t0.00156799 \t0.0702526\n",
      "6  \t80    \t0.00156799 \t0.0706711\n",
      "7  \t93    \t0.00156799 \t0.0694522\n",
      "8  \t92    \t0.000201373\t0.069224 \n",
      "9  \t83    \t0.000201373\t0.0698183\n",
      "10 \t84    \t0.000201373\t0.0691503\n",
      "11 \t103   \t0.000201373\t0.069741 \n",
      "12 \t85    \t0.000201373\t0.0713301\n",
      "13 \t84    \t0.000201373\t0.0716171\n",
      "14 \t78    \t0.000201373\t0.0705982\n",
      "15 \t91    \t7.97076e-05\t0.0717529\n",
      "16 \t73    \t7.97076e-05\t0.0722809\n",
      "17 \t78    \t2.97588e-05\t0.0719325\n",
      "18 \t99    \t3.9815e-05 \t0.0715032\n",
      "19 \t94    \t0.000201373\t0.0716722\n",
      "20 \t79    \t0.000102841\t0.0706862\n",
      "21 \t88    \t0.000102841\t0.0705881\n",
      "22 \t79    \t0.000102841\t0.0704872\n",
      "23 \t81    \t1.13431e-05\t0.0707242\n",
      "24 \t81    \t1.13431e-05\t0.0715213\n",
      "25 \t78    \t1.13431e-05\t0.0712814\n",
      "26 \t77    \t1.13431e-05\t0.0727359\n",
      "27 \t82    \t1.13431e-05\t0.0735536\n",
      "28 \t89    \t1.13431e-05\t0.0738906\n",
      "29 \t77    \t1.13431e-05\t0.0746747\n",
      "30 \t81    \t1.13431e-05\t0.0746063\n",
      "31 \t89    \t4.91278e-05\t0.0735427\n",
      "32 \t85    \t4.91278e-05\t0.0737435\n",
      "33 \t95    \t4.91278e-05\t0.0733987\n",
      "34 \t82    \t4.91278e-05\t0.0741053\n",
      "35 \t90    \t4.91278e-05\t0.0743717\n",
      "36 \t92    \t2.97588e-05\t0.0744028\n",
      "37 \t76    \t2.97588e-05\t0.0734303\n",
      "38 \t78    \t2.97588e-05\t0.0726245\n",
      "39 \t83    \t2.97588e-05\t0.0717383\n",
      "40 \t64    \t2.97588e-05\t0.0722207\n",
      "41 \t89    \t2.97588e-05\t0.0725772\n",
      "42 \t84    \t2.97588e-05\t0.0723627\n",
      "43 \t88    \t2.23587e-05\t0.0729066\n",
      "44 \t98    \t2.23587e-05\t0.0713239\n",
      "45 \t82    \t2.23587e-05\t0.0697758\n",
      "46 \t81    \t2.23587e-05\t0.0699272\n",
      "47 \t79    \t2.23587e-05\t0.07019  \n",
      "48 \t82    \t0.000175248\t0.069996 \n",
      "49 \t94    \t0.000175248\t0.0700458\n",
      "50 \t78    \t0.000175248\t0.0699952\n",
      "\n",
      "#1\n",
      "Constraints: ((9, 1), (0, 0.0), (1, 0.0))\n",
      "Individual: [[0.0, 0.0, 2.0, 2.0, 0.0, 0.0, 1572.0, 15.0, 4.0, 1]]\n",
      "Fitness: (5.210483628337315e-06,)\n",
      "\n",
      "#2\n",
      "Constraints: ((9, 1), (0, 0.0), (1, 0.0))\n",
      "Individual: [[0.0, 0.0, 2.0, 2.0, 0.0, 0.0, 1228.0, 15.0, 4.0, 1]]\n",
      "Fitness: (1.134305927918966e-05,)\n",
      "\n",
      "#3\n",
      "Constraints: ((9, 1), (0, 0.0), (1, 0.0))\n",
      "Individual: [[0.0, 0.0, 2.0, 2.0, 0.0, 0.0, 1721.0, 15.0, 4.0, 1]]\n",
      "Fitness: (2.2358653506304726e-05,)\n",
      "\n",
      "#4\n",
      "Constraints: ((9, 1), (0, 0.0), (1, 0.0))\n",
      "Individual: [[0.0, 0.0, 2.0, 2.0, 0.0, 0.0, 1101.0, 15.0, 4.0, 1]]\n",
      "Fitness: (2.9758822062520007e-05,)\n",
      "\n",
      "#5\n",
      "Constraints: ((9, 1), (0, 0.0), (1, 0.0))\n",
      "Individual: [[0.0, 0.0, 2.0, 2.0, 0.0, 0.0, 1049.0, 15.0, 4.0, 1]]\n",
      "Fitness: (3.981499708755054e-05,)\n",
      "\n",
      "#6\n",
      "Constraints: ((9, 1), (0, 0.0), (1, 0.0))\n",
      "Individual: [[0.0, 0.0, 2.0, 2.0, 0.0, 0.0, 1860.0, 15.0, 4.0, 1]]\n",
      "Fitness: (4.912779234039766e-05,)\n",
      "\n",
      "#7\n",
      "Constraints: ((9, 1), (0, 0.0), (1, 0.0))\n",
      "Individual: [[0.0, 0.0, 2.0, 2.0, 0.0, 0.0, 1977.0, 15.0, 4.0, 1]]\n",
      "Fitness: (7.970757564429443e-05,)\n",
      "\n",
      "#8\n",
      "Constraints: ((9, 1), (0, 0.0), (1, 0.0))\n",
      "Individual: [[0.0, 0.0, 2.0, 2.0, 0.0, 0.0, 2051.0, 15.0, 4.0, 1]]\n",
      "Fitness: (0.0001028410463585483,)\n",
      "\n",
      "#9\n",
      "Constraints: ((9, 1), (0, 0.0), (1, 0.0))\n",
      "Individual: [[0.0, 0.0, 2.0, 2.0, 0.0, 0.0, 2181.0, 15.0, 4.0, 1]]\n",
      "Fitness: (0.00015058657842287326,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== NEW DATASET ===\n",
      "\n",
      "Analizing Age ...\n",
      "\n",
      "0.0 is privileged: -0.0015129797738493433\n",
      "- PP: 109 · Expected: 109 · To be removed: 0\n",
      "- PN: 47 · Expected: 47 · To be added: 0\n",
      "1.0 is discriminated: 0.0015129797738493433\n",
      "- DP: 449 · Expected: 449 · To be added: 0\n",
      "- DN: 195 · Expected: 195 · To be removed: 0\n",
      "\n",
      "Analizing Sex ...\n",
      "0.0 is discriminated: 0.0027024878785469886\n",
      "- DP: 176 · Expected: 176 · To be added: 0\n",
      "- DN: 77 · Expected: 77 · To be removed: 0\n",
      "\n",
      "1.0 is privileged: -0.0027024878785469886\n",
      "- PP: 382 · Expected: 382 · To be removed: 0\n",
      "- PN: 165 · Expected: 165 · To be added: 0\n",
      "\n",
      "OG dataset length: 800\n",
      "Instances generated: 38\n",
      "New dataset length: 800\n"
     ]
    }
   ],
   "source": [
    "gf.balance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b6bb3d8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "fair_df, new_df = gf.get_results() #balanced dataset, only syntethic instances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2862626f",
   "metadata": {},
   "outputs": [],
   "source": [
    "gf = GenFair(fair_df, sensitive_attributes, class_name, #<- must be included\n",
    "                               causal_reg, causal_class, #<- causal relations\n",
    "                               integer, weighted, random_l) #<- how to handle the various features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ba1b4ee2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target variable: Risk\n",
      "Sensitive attributes: ['Age', 'Sex']\n",
      "\n",
      "Integer strategy for: []\n",
      "Weighted strategy for: ['Job', 'Housing', 'Saving accounts', 'Checking account', 'Credit amount', 'Duration', 'Purpose']\n",
      "Random strategy for: []\n",
      "\n",
      "Regressor: []\n",
      "Classifier: []\n",
      "Ranking the data...\n",
      "Creating the sensitive dictionary...\n",
      "\n",
      "Analizing Age ...\n",
      "\n",
      "0.0 is privileged: -0.0015129797738493433\n",
      "- PP: 109 · Expected: 109 · To be removed: 0\n",
      "- PN: 47 · Expected: 47 · To be added: 0\n",
      "1.0 is discriminated: 0.0015129797738493433\n",
      "- DP: 449 · Expected: 449 · To be added: 0\n",
      "- DN: 195 · Expected: 195 · To be removed: 0\n",
      "\n",
      "Analizing Sex ...\n",
      "0.0 is discriminated: 0.0027024878785469886\n",
      "- DP: 176 · Expected: 176 · To be added: 0\n",
      "- DN: 77 · Expected: 77 · To be removed: 0\n",
      "\n",
      "1.0 is privileged: -0.0027024878785469886\n",
      "- PP: 382 · Expected: 382 · To be removed: 0\n",
      "- PN: 165 · Expected: 165 · To be added: 0\n"
     ]
    }
   ],
   "source": [
    "gf.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a3b552d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Removing instances...\n",
      "\n",
      "No instance removed! The dataset is already balanced\n"
     ]
    }
   ],
   "source": [
    "gf.balance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6728e8cc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.0"
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