{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "maritime-diagram",
   "metadata": {},
   "outputs": [],
   "source": [
    "from library.exercise import Exercise, allTesters\n",
    "from library.dataset import DataSet, TrainTestData\n",
    "from library.interfaces import GanBaseClass, TesterNetworkBaseClass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "changing-enhancement",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "acquired-dover",
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadFailingDataset():\n",
    "    data0 = np.array(range(100)).reshape((25,4))\n",
    "    return DataSet(data0=np.concatenate((data0, [[0,0,0,0]])), data1=data0 * 2)\n",
    "\n",
    "def loadEqualDataset():\n",
    "    data0 = np.array(range(100)).reshape((25,4))\n",
    "    return DataSet(data0=data0, data1=data0 * 2)\n",
    "\n",
    "def loadDataset():\n",
    "    data0 = np.array(range(100)).reshape((25,4))\n",
    "    return DataSet(data0=data0[0:12], data1=data0 * 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "searching-sweet",
   "metadata": {},
   "outputs": [],
   "source": [
    "exercise = Exercise(allTesters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "concrete-range",
   "metadata": {},
   "outputs": [],
   "source": [
    "gan = GanBaseClass()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "binary-biodiversity",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Start exercise for synthetic point generator\n",
      "\n",
      "====== Step {shuffleStep + 1}/{self.numOfShuffles} =======\n",
      "-> Spliting data to slices\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=20, |class 1|=20\n"
     ]
    },
    {
     "data": {
      "image/png": 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mM+tGnDgMY+wTb+wTT5w4JIpw3EZOgcaQJgogbiOnQGNIEwUQn0ZOgSbsxCFROOLTyCnQOJImIhIYQ5qISGAMaSIigTGkiYgExpAmIhIYQ5qISGAMaSIigTGkiSTCLeTkD4Y0kUS4hZz8wZAmkgi3kJM/uC2cSCLcQk7+4EiaiEhgDGkiIoExpImIBMaQJiISGEOaiEhgDGkiIoExpImIBMaQJgoT3EYenRjSRGGC28ijE0OaKExwG3l04rZwojDBbeTRiSNpIiKBMaSJiATGkCYiEhhDmohIYAxpIiKBMaSJiAQmdEg3NjYiLy8PGo0GlZWVMJkm3mk1ODiIsrIyqNVqFBQUoK2tzeP1lpYWbNq0CRqNBgUFBcEunYgoIIQN6dbWVjQ0NKCmpgYtLS2wWq2oqqry2dbhcKCiogIKhQI6nQ6bN29GdXU1enp63G2+/PJLLFu2DJs2bQrVJRBJitvII4Owm1mam5tRXl6OwsJCAEB9fT2WL1+OM2fOQKVSebTt7OyEwWBAe3s7EhISoFKp0Nvbi+bmZmRmZgIAHnnkEQBAW1sbjh49GtqLIZLAN7eRcxNM+BJyJG2329Hf34/s7Gz3sfT0dKSmpqKvr8+rvV6vh1qtRkJCgvtYTk6Oz7ZE0YLbyCODkCNpi8UCp9MJhcLzt79cLofZbPZqbzabfba91j3sG6VQJAb8nMGiVCZJXYJwoqlPViuTsDp77nXbRVOf+EO0/hAypCfL5XKF7LNMplE4naH7vBulVCbBaLRKXYZQ2Cfe2CeepOqPa/1iCMjtji+++AK9vb2BOBUAIDk5GTKZzGskbDabIZfLvdorFAqfbb89uiYiCjcBCemzZ8/ihz/8YSBOBQCIi4tDRkYGuru73ceGh4cxMjICjUbj1V6tVkOv18Nms7mPdXV1+WxLRBROhJw4BICSkhI0NTXh+PHj6O/vh1arRVZWFlQqFfR6PYqKimAwGAAA+fn5SElJgVarxcDAAHQ6HTo6OlBaWuo+n9FoxKlTp3Du3DnY7XacOnUKp06dkuryiIj84tc96cWLFwe7Di8bNmyAyWRCbW0trFYrcnNzsXPnTgCAzWbD4OAgHA4HgKsj78bGRtTU1KC4uBhKpRJ1dXXu5XfA1c0se/fudf957dq1AIDTp0+H7qKIiCYpxuXHrNvdd9+NTZs2ea1P/srIyAh++ctfRsXIlBOH4Yt94o194knEiUO/RtIqlQq333471q1b5/P1/v5+/PKXv7yx6oiIaEJ+3ZO+++67MTQ0NOHrCQkJuPfeewNWFBGFFreQi8uv2x30Nd7uCF/sE29f9ckzbf+LkQs2pM6agd3FoZ+DEoWItzsCtrrj7NmzgToVEYUYt5CLa0o7DsfHx3H06FEcOnQIvb29OHnyZKDqIqIQ4pPIxXVDIf3JJ5/g8OHDaG9vx/j4OO677z689tprga6NiCjq+R3SDocDR48eRUtLCz788EPk5ubiwoULaG9vn3BpHhERTY1fIb1nzx60t7dDoVBg7dq1ePHFF6FUKnHnnXdCJhN20yIRUdjzK6R/+9vf4oknnsCWLVsYykREIeRX4j711FN4++238b3vfQ979uzBmTNngl0XERHBz5H0o48+ikcffRRdXV04fPgw1q9fjzvuuAMulwtffPFFsGskIopak1rdkZ2djezsbJjNZrS2tuKLL77Aww8/jKVLl2LVqlV48MEHg1UnEVFUuqEbzHK5HP/wD/+AP/7xj/j1r3+Nm2++GXV1dYGujYgExW3koTPlx2fl5OQgJyfH57MHiSgy8UnkoePXSPrkyZMoLS2F1eq9p/3SpUsoKyuD0WgMeHFEJCZuIw8dv0K6qakJS5cuRVKS95eA3HzzzcjMzERTU1PAiyMiMeXMV2B38WKOokPAr5Du6+vD8uXLJ3x92bJleP/99wNWFBERXeVXSJ8/fx6zZs2a8PWZM2e6nzdIRESB41dIJyUlXfOrSM+ePevzVggREU2NXyG9ZMkS6HS6CV9/8803cc899wSsKCIiusqvJXg//vGPUVpaiptvvhlPPPEEZs+eDQAwGAx47bXX8Mc//hEHDhwIaqFERNHIr5C+5557UFdXh5/97GdoaWlBYmIiAGB0dBSxsbGora3FkiVLglooEVE08nszy/r165GXl4fW1lYYjUa4XC7MmzcPRUVF7pE1EREFll8hfeHCBWzfvh3vvPMOnE4nNBoNnn/+eaSlpQW7PiKKAO/9xYQjfZ9hteY2rq2eJL8mDl966SX09fXhySefxNNPPw2TyYSamppg10ZEEeKb28hpcvwaSb/zzjt47rnnUFBQAADIz8/H6tWr4XA4EBsbG9QCiSj8rdbc5h5J0+T4FdIGgwF33nmn+8933HEHYmNjYTQacfvttwetOCKKDHwa+Y3z63bHlStXvEbMMpkMV65cCUpRRER0ld+rO/7xH//RI6jtdju2bduG+Ph497F9+/YFtjoioijnV0ivW7fO69jq1asDXgwREXnyK6R3794d7DqIiMiHG3p8Vqg0NjYiLy8PGo0GlZWVMJkmflTP4OAgysrKoFarUVBQgLa2No/Xx8fHsXv3bmRlZWHJkiV45pln+BBdIhKesCHd2tqKhoYG1NTUoKWlBVarFVVVVT7bOhwOVFRUQKFQQKfTYfPmzaiurkZPT4+7zauvvoqOjg7827/9G/bv3w+9Xo+dO3eG6nKIiG6IsCHd3NyM8vJyFBYWYuHChaivr0dXVxfOnDnj1bazsxMGgwH19fVQqVR48MEHsWrVKjQ3NwMAnE4nDh48iK1btyInJwdqtRo7duzAkSNHcOnSpVBfGhGR34QMabvdjv7+fmRnZ7uPpaenIzU1FX19fV7t9Xo91Go1EhIS3MdycnLcbYeHh2GxWDzOl5mZCZfLhT//+c9BvBIiuhF8GvnXhAxpi8UCp9MJhcJz8btcLvf5VHKz2eyz7Vf3sL/632+2mTZtGmbOnHnN+9xEJA1uI/+a3+ukReZyuab0+mQoFIkBO1ewKZV8Ws63sU+8idgnpfnzcKjrLP5v9v8JeX2i9YeQIZ2cnAyZTAaTyYT58+e7j5vNZsjlcq/2CoUCQ0NDHse+Obq+5ZZbAFwdUaempgK4uovy4sWLXiPw6zGZRuF0Bi70g0WpTILRaJW6DKGwT7yJ2id3Km9C3QMLASCk9UnVH9f6xSDk7Y64uDhkZGSgu7vbfWx4eBgjIyPQaDRe7dVqNfR6PWw2m/tYV1eXu216ejqSk5M9ztfb24uYmBgsWrQoiFdCRDQ1QoY0AJSUlKCpqQnHjx9Hf38/tFotsrKyoFKpoNfrUVRU5H5CeX5+PlJSUqDVajEwMACdToeOjg6UlpYCuPo9I5s2bcJLL72Erq4u6PV6PPfcc1i9ejVmzpwp5WUSEV2TkLc7AGDDhg0wmUyora2F1WpFbm6ue12zzWbD4OAgHA4HgKsj78bGRtTU1KC4uBhKpRJ1dXXIzMx0n+8nP/kJxsbG8NOf/hQOhwP3338/nn32WUmujYjIXzGuQM6qRQHekw5f7BNv7BNPvCdNRESTwpAmIhIYQzqIrpw4BsejP8SVE8ekLoUoYkX67kSGdBA5Dx6Aa2gIzoMHpC6FKGJF+u5EhnQQyR4uQ8ycOZA9XCZ1KUQRa7XmNqTOmhGxD7kVdgleJJi2rBDTlhVKXQZRRIv0h9xyJE1EJDCGdIhxMpGIJoMhHWKcTCSiyWBIhxgnE4loMjhxGGKcTCSiyeBImohIYAxpIiKBMaQFwVUfRKERbtvIGdKC4KoPotAIt23kDGlBcNUHUWiE2zZyru4QBFd9EIVGuG0j50iaiEhgDGnBcUKRKLoxpAXHCUWi6MaQFhwnFImiGycOBccJRaLoxpE0EZHAGNJERAJjSIcprvogCg2pt5EzpMMUV30QhYbU28gZ0mGKqz6IQkPqbeRc3RGmuOqDKDSk3kbOkTQRkcAY0hHmqwlF2x/+IHUpRBQADOkI89WE4ujr+6QuhYgCgCEdYb6aUEx87MdSl0JEASBsSDc2NiIvLw8ajQaVlZUwma69RnFwcBBlZWVQq9UoKChAW1ubx+stLS3YtGkTNBoNCgoKglm6pKYtK0Tsb97AjJUrpS6FiAJAyJBubW1FQ0MDampq0NLSAqvViqqqqgnbOxwOVFRUQKFQQKfTYfPmzaiurkZPT4+7zZdffolly5Zh06ZNobgEIqKAEHIJXnNzM8rLy1FYeHWJWX19PZYvX44zZ85ApVJ5te/s7ITBYEB7ezsSEhKgUqnQ29uL5uZmZGZmAgAeeeQRAEBbWxuOHj0auosRxJUTx+A8eACyh8u4dI8ojAg3krbb7ejv70d2drb7WHp6OlJTU9HX1+fzPXq9Hmq1GgkJCe5jOTk5E7aPRtyhSBR8wdhCLtxI2mKxwOl0QqHwXDwul8thNpt9vsdsNvtsf7372DdCoUgM+DmDRalMcv+7bfPjGH19HxIf+zFmfON4tFFG8bVPhH3iaSr98Yf/dwqfXbqMP5z8K1Znzw1IPSEN6e3bt+Ott96a8PV169Zh69atkz6vy+WaSlmTYjKNwukM3efdKKUyCUaj9esD9+YB9+ZhFMDoN49HEa8+IfbJt0y1P1YuSsGRvs+wclHKpM5zrV8MIQ1prVaLp556asLX4+Pj8Z3vfAcymQwmkwnz5893v2Y2myGXy32+T6FQYGhoyOOYr9E1EVEwBWMLeUhDOikpCUlJ1/+rREZGBrq7u92TfsPDwxgZGYFGo/HZXq1WY//+/bDZbJgxYwYAoKura8L2REThQriJQwAoKSlBU1MTjh8/jv7+fmi1WmRlZblXduj1ehQVFcFgMAAA8vPzkZKSAq1Wi4GBAeh0OnR0dKC0tNR9TqPRiFOnTuHcuXOw2+04deoUTp06Jcn1iYTfS00kNuEmDgFgw4YNMJlMqK2thdVqRW5uLnbu3Ol+3WazYXBwEA6HAwAQFxeHxsZG1NTUoLi4GEqlEnV1de6ROHB1M8vevXvdf167di0A4PTp06G5KEF9c9UHl+YRiSfGFcpZtwgQthOHE4im9dOcJPPGPvEkVX8IM3FI4uH3UhOJTch70kREdBVDmrxwMpFIHAxp8sIt5ETiYEiTFz7klkgcnDgkL5xMJBIHR9JERAJjSBMRCYwhTX7jqg+i0GNIk9+46oMo9BjS5Deu+iAKPa7uIL9x1QdR6HEkTUQkMIY0TRknFImChyFNU8YJRaLgYUjTlHFCkSh4OHFIU8YJRaLg4UiaiEhgDGkiIoExpClouOqDaOoY0hQ0XPVBNHUMaQoarvogmjqu7qCg4aoPoqnjSJqISGAMaQo5TigS+Y8hTSHHCUUi/zGkKeQ4oUjkP04cUshxQpHIfxxJExEJjCFNRCQwhjQJg6s+iLwxpEkYXPVB5E3YkG5sbEReXh40Gg0qKythMpmu2X5wcBBlZWVQq9UoKChAW1ub+zWHw4E9e/Zg5cqV0Gg0KCgowCuvvILx8fFgXwZNAld9EHkTcnVHa2srGhoa8POf/xxpaWmor69HVVUVfvvb3/ps73A4UFFRgUWLFkGn06Gvrw/V1dVIS0tDZmYmLl++jIGBAVRVVUGlUuGTTz7BP//zP0Mmk2HLli0hvjqaCFd9EHkTMqSbm5tRXl6OwsKr/8HW19dj+fLlOHPmDFQqlVf7zs5OGAwGtLe3IyEhASqVCr29vWhubkZmZiaSkpLw+uuvu9vPmTMH5eXl6OjoYEgTkdCEu91ht9vR39+P7Oxs97H09HSkpqair6/P53v0ej3UajUSEhLcx3JyciZsDwAWiwVJSUmBK5yChhOKFM2EG0lbLBY4nU4oFAqP43K5HGaz2ed7zGazz/YT3cc+d+4cDh8+jB07dky6PoUicdLvkYpSGRm/hIxvHoRr+Cymvfl7KDcWT+lckdIngcQ+8SRaf4Q0pLdv34633nprwtfXrVuHrVu3Tvq8LpfL77YXL15ERUUFli1bhjVr1kz6s0ymUTid/n+eVJTKJBiNVqnLCIgrDz4M18EDuPLgpildUyT1SaCwTzxJ1R/X+sUQ0pDWarV46qmnJnw9Pj4e3/nOdyCTyWAymTB//nz3a2azGXK53Of7FAoFhoaGPI75Gl2PjY3h8ccfR1paGnbt2jWFK6FQ4oQiRbOQhnRSUpJf94EzMjLQ3d2NzMxMAMDw8DBGRkag0Wh8tler1di/fz9sNhtmzJgBAOjq6vJof/nyZTzxxBOIj4/Hyy+/jOnThbvTQ0TkRbiJQwAoKSlBU1MTjh8/jv7+fmi1WmRlZblXduj1ehQVFcFgMAAA8vPzkZKSAq1Wi4GBAeh0OnR0dKC0tBTA1SV6Tz75JMxmM+rq6nDx4kUYjUYYjUbJrpGIyB9CDic3bNgAk8mE2tpaWK1W5ObmYufOne7XbTYbBgcH4XA4AABxcXFobGxETU0NiouLoVQqUVdX5x6JGwwGdHZ2AgBWrFjh8VmnT58O0VVRIF05cQzOgwcge7iMt0IoosW4JjPrRpw4FITj0R/CNTSEmDlzEPubN/x6T6T3yY1gn3gSceJQyNsdRNfDLeQULYS83UF0PVzxQdGCI2kiIoExpCmicAs5RRqGNEUUfic1RRqGNEUUTihSpOHEIUUUTihSpOFImohIYAxpigpXThyDcf0GTihS2GFIU1RwHjyA8Y8HOaFIYYchTVFB9nAZpn/3u5xQpLDDiUOKCtOWFUK5sZjfU0FhhyNpIiKBMaSJiATGkKaoxm3kJDqGNEU1biMn0TGkKapxGzmJjqs7KKpxGzmJjiNpIiKBMaSJfOCEIomCIU3kAycUSRQMaSIfOKFIouDEIZEPnFAkUXAkTUQkMIY0EZHAGNJERAJjSBMRCYwhTUQkMIY0EZHAGNJERAJjSBMRCYwhTUQkMIY0EZHAuC18kmSyGKlL8Fs41Roq7BNv7BNPovVHjMvlckldBBER+cbbHUREAmNIExEJjCFNRCQwhjQRkcAY0kREAmNIExEJjCFNRCQwhjQRkcAY0kREAmNIExEJjCEd4V577TWsWbMGd999N773ve9h165dGBsbk7qskGpsbEReXh40Gg0qKythMpmkLkky/Hm4vp/85CdYsGABuru7pS4FAEM64n344Yd47LHH0NbWhhdeeAHvvvsudu3aJXVZIdPa2oqGhgbU1NSgpaUFVqsVVVVVUpclmWj/ebie9vZ22Gw2qcvw5KKo8vbbb7uWLl0qdRkhs3btWtfLL7/s/vPZs2ddKpXKdfr0aQmrEke0/Txcy/nz513f//73XSMjIy6VSuXq6uqSuiSXy+VycSQdZSwWC5KSkqQuIyTsdjv6+/uRnZ3tPpaeno7U1FT09fVJWJk4ounn4Xq0Wi0qKipw++23S12KB4Z0FLFardi3bx/Wr18vdSkhYbFY4HQ6oVAoPI7L5XKYzWaJqhJHtP08XEtLSwvGx8exceNGqUvxwi/9D1Pbt2/HW2+9NeHr69atw7/8y7+4/2y32/Hkk08iPT0djz/+eChKJIHx5+Fr586dw969e9HS0iJ1KT4xpMOUVqvFU089NeHr8fHx7n8fHx/H1q1bMTY2hv3792P69Oj4vz05ORkymQwmkwnz5893HzebzZDL5RJWJq1o/XmYyMmTJ/H5559jxYoVHsd/9KMfYd26daivr5eosqui+/+dMJaUlOTXvUSn04lt27bh7NmzOHDgAG666aYQVCeGuLg4ZGRkoLu7G5mZmQCA4eFhjIyMQKPRSFydNKL552Ei2dnZOHLkiMexBx54ALt27UJeXp5EVX2NIR3hnn32WXR3d+PXv/41HA4HjEYjgKv3ZadNmyZxdcFXUlKC+vp6LFy4EGlpaaivr0dWVhZUKpXUpUki2n8efElMTPT585CWlobZs2dLUJEnPuMwwi1YsMDn8RMnTiAtLS3E1UijsbERBw4cgNVqRW5uLnbu3IlbbrlF6rIkwZ8H/yxYsABvvPEGsrKypC6FIU1EJDIuwSMiEhhDmohIYAxpIiKBMaSJiATGkCYiEhhDmohIYAxpIiKBMaSJvmX79u1YsGABFixYgEWLFuG+++5DdXU1LBaLu82HH36ILVu2IDc3F3fddReWL1+Of/qnf8Kf//xnr/P95je/wcKFC7F7926fnzc6OoodO3YgKysLd999Nx577DGcPXs2aNdH4YUhTeTDvffei3fffRd/+tOfoNVqcfToUWzbtg3A1ae9lJSUYPr06Xj++efx9ttv46WXXkJqaiqee+45r3MdPnwYFRUVaG9vh91u93r96aefxnvvvYeXX34ZBw8ehMvlQnl5OS5fvhz066QwIOkjB4gEtG3bNtcjjzzicezVV191ZWRkuM6fP+9avHix69lnn/X53gsXLnj8+b333nPl5OS4HA6Ha9WqVa4jR454vP7xxx+7VCqV65133vE4x5133ulqbW0NzAVRWONImsgP8fHxcDqd0Ol0sNvtqKys9Nlu5syZHn8+dOgQHnjgAUyfPh1r167F4cOHPV7/4IMPEBsbi5ycHI9zqNVqvP/++4G/EAo7DGmi6/joo4/wu9/9DhqNBkajEYmJibj11luv+z6z2Yxjx45h3bp1AIA1a9bggw8+wMcff+xuYzQaMWvWLK9voLvlllvc31BH0Y0hTeRDT08P7rnnHqjVavzgBz9Aeno6XnjhBbgm8X1kra2tmD9/PjIyMgAAKSkpyM3NxZtvvunX+2NiYm6odoos/D5pIh/UajX27NmDadOmISUlBXFxcQCAefPmYXR0FOfPn7/maNrlckGn02FoaAiLFi1yH3c6nfif//kfbN26FXFxcVAqlbhw4QKuXLniMZo2mUyYO3du0K6PwgdH0kQ+xMfHY86cOUhLS3MHNACsXLkScXFxePXVV32+7+LFiwCArq4uDA8P4/e//z3a29s9/hkfH8exY8cAAEuWLIHD4UBXV5f7HJcuXUJfXx/+9m//NohXSOGCI2miSZg9ezaqq6tRXV0Nq9WKhx56COnp6bh48SJOnDiB7u5u/O53v0NLSwuWLl2Ke+65x+scBQUFOHToEP7+7/8e8+bNw7Jly1BbW4vnnnsOSUlJePHFFzF79mysWrVKgisk0XAkTTRJDz74IA4cOIAvv/wSVVVVKCoqwk9/+lN8+umn2LFjB0wmE06cOIGVK1f6fP+qVavQ09ODTz75BADw85//HJmZmdiyZQs2btwIp9OJffv2eTxMmKIXn8xCRCQwjqSJiATGkCYiEhhDmohIYAxpIiKBMaSJiATGkCYiEhhDmohIYAxpIiKBMaSJiAT2/wGDrNQyooZcHAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [5 0 5 0]\n",
      "f1 score_KNN: 0.0\n",
      "balanced accuracy_KNN: 0.5\n",
      "confusion matrix_KNN\n",
      "[[5 0]\n",
      " [5 0]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=20, |class 1|=20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [5 0 5 0]\n",
      "f1 score_KNN: 0.0\n",
      "balanced accuracy_KNN: 0.5\n",
      "confusion matrix_KNN\n",
      "[[5 0]\n",
      " [5 0]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=20, |class 1|=20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [5 0 5 0]\n",
      "f1 score_KNN: 0.0\n",
      "balanced accuracy_KNN: 0.5\n",
      "confusion matrix_KNN\n",
      "[[5 0]\n",
      " [5 0]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=20, |class 1|=20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_KNN: 1.0\n",
      "balanced accuracy_KNN: 1.0\n",
      "confusion matrix_KNN\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=20, |class 1|=20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [1 4 0 5]\n",
      "f1 score_KNN: 0.7142857142857143\n",
      "balanced accuracy_KNN: 0.6\n",
      "confusion matrix_KNN\n",
      "[[1 4]\n",
      " [0 5]]\n",
      "-> check results\n",
      "\n",
      "====== Step {shuffleStep + 1}/{self.numOfShuffles} =======\n",
      "-> Spliting data to slices\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=20, |class 1|=20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [5 0 5 0]\n",
      "f1 score_KNN: 0.0\n",
      "balanced accuracy_KNN: 0.5\n",
      "confusion matrix_KNN\n",
      "[[5 0]\n",
      " [5 0]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=20, |class 1|=20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [5 0 5 0]\n",
      "f1 score_KNN: 0.0\n",
      "balanced accuracy_KNN: 0.5\n",
      "confusion matrix_KNN\n",
      "[[5 0]\n",
      " [5 0]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=20, |class 1|=20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [5 0 5 0]\n",
      "f1 score_KNN: 0.0\n",
      "balanced accuracy_KNN: 0.5\n",
      "confusion matrix_KNN\n",
      "[[5 0]\n",
      " [5 0]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=20, |class 1|=20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_KNN: 1.0\n",
      "balanced accuracy_KNN: 1.0\n",
      "confusion matrix_KNN\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=20, |class 1|=20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [1 4 0 5]\n",
      "f1 score_KNN: 0.7142857142857143\n",
      "balanced accuracy_KNN: 0.6\n",
      "confusion matrix_KNN\n",
      "[[1 4]\n",
      " [0 5]]\n",
      "-> check results\n",
      "\n",
      "====== Step {shuffleStep + 1}/{self.numOfShuffles} =======\n",
      "-> Spliting data to slices\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=20, |class 1|=20\n"
     ]
    },
    {
     "data": {
      "image/png": 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mM+tGnDgMY+wTb+wTT5w4JIpw3EZOgcaQJgogbiOnQGNIEwUQn0ZOgSbsxCFROOLTyCnQOJImIhIYQ5qISGAMaSIigTGkiYgExpAmIhIYQ5qISGAMaSIigTGkiSTCLeTkD4Y0kUS4hZz8wZAmkgi3kJM/uC2cSCLcQk7+4EiaiEhgDGkiIoExpImIBMaQJiISGEOaiEhgDGkiIoExpImIBMaQJgoT3EYenRjSRGGC28ijE0OaKExwG3l04rZwojDBbeTRiSNpIiKBMaSJiATGkCYiEhhDmohIYAxpIiKBMaSJiAQmdEg3NjYiLy8PGo0GlZWVMJkm3mk1ODiIsrIyqNVqFBQUoK2tzeP1lpYWbNq0CRqNBgUFBcEunYgoIIQN6dbWVjQ0NKCmpgYtLS2wWq2oqqry2dbhcKCiogIKhQI6nQ6bN29GdXU1enp63G2+/PJLLFu2DJs2bQrVJRBJitvII4Owm1mam5tRXl6OwsJCAEB9fT2WL1+OM2fOQKVSebTt7OyEwWBAe3s7EhISoFKp0Nvbi+bmZmRmZgIAHnnkEQBAW1sbjh49GtqLIZLAN7eRcxNM+BJyJG2329Hf34/s7Gz3sfT0dKSmpqKvr8+rvV6vh1qtRkJCgvtYTk6Oz7ZE0YLbyCODkCNpi8UCp9MJhcLzt79cLofZbPZqbzabfba91j3sG6VQJAb8nMGiVCZJXYJwoqlPViuTsDp77nXbRVOf+EO0/hAypCfL5XKF7LNMplE4naH7vBulVCbBaLRKXYZQ2Cfe2CeepOqPa/1iCMjtji+++AK9vb2BOBUAIDk5GTKZzGskbDabIZfLvdorFAqfbb89uiYiCjcBCemzZ8/ihz/8YSBOBQCIi4tDRkYGuru73ceGh4cxMjICjUbj1V6tVkOv18Nms7mPdXV1+WxLRBROhJw4BICSkhI0NTXh+PHj6O/vh1arRVZWFlQqFfR6PYqKimAwGAAA+fn5SElJgVarxcDAAHQ6HTo6OlBaWuo+n9FoxKlTp3Du3DnY7XacOnUKp06dkuryiIj84tc96cWLFwe7Di8bNmyAyWRCbW0trFYrcnNzsXPnTgCAzWbD4OAgHA4HgKsj78bGRtTU1KC4uBhKpRJ1dXXu5XfA1c0se/fudf957dq1AIDTp0+H7qKIiCYpxuXHrNvdd9+NTZs2ea1P/srIyAh++ctfRsXIlBOH4Yt94o194knEiUO/RtIqlQq333471q1b5/P1/v5+/PKXv7yx6oiIaEJ+3ZO+++67MTQ0NOHrCQkJuPfeewNWFBGFFreQi8uv2x30Nd7uCF/sE29f9ckzbf+LkQs2pM6agd3FoZ+DEoWItzsCtrrj7NmzgToVEYUYt5CLa0o7DsfHx3H06FEcOnQIvb29OHnyZKDqIqIQ4pPIxXVDIf3JJ5/g8OHDaG9vx/j4OO677z689tprga6NiCjq+R3SDocDR48eRUtLCz788EPk5ubiwoULaG9vn3BpHhERTY1fIb1nzx60t7dDoVBg7dq1ePHFF6FUKnHnnXdCJhN20yIRUdjzK6R/+9vf4oknnsCWLVsYykREIeRX4j711FN4++238b3vfQ979uzBmTNngl0XERHBz5H0o48+ikcffRRdXV04fPgw1q9fjzvuuAMulwtffPFFsGskIopak1rdkZ2djezsbJjNZrS2tuKLL77Aww8/jKVLl2LVqlV48MEHg1UnEVFUuqEbzHK5HP/wD/+AP/7xj/j1r3+Nm2++GXV1dYGujYgExW3koTPlx2fl5OQgJyfH57MHiSgy8UnkoePXSPrkyZMoLS2F1eq9p/3SpUsoKyuD0WgMeHFEJCZuIw8dv0K6qakJS5cuRVKS95eA3HzzzcjMzERTU1PAiyMiMeXMV2B38WKOokPAr5Du6+vD8uXLJ3x92bJleP/99wNWFBERXeVXSJ8/fx6zZs2a8PWZM2e6nzdIRESB41dIJyUlXfOrSM+ePevzVggREU2NXyG9ZMkS6HS6CV9/8803cc899wSsKCIiusqvJXg//vGPUVpaiptvvhlPPPEEZs+eDQAwGAx47bXX8Mc//hEHDhwIaqFERNHIr5C+5557UFdXh5/97GdoaWlBYmIiAGB0dBSxsbGora3FkiVLglooEVE08nszy/r165GXl4fW1lYYjUa4XC7MmzcPRUVF7pE1EREFll8hfeHCBWzfvh3vvPMOnE4nNBoNnn/+eaSlpQW7PiKKAO/9xYQjfZ9hteY2rq2eJL8mDl966SX09fXhySefxNNPPw2TyYSamppg10ZEEeKb28hpcvwaSb/zzjt47rnnUFBQAADIz8/H6tWr4XA4EBsbG9QCiSj8rdbc5h5J0+T4FdIGgwF33nmn+8933HEHYmNjYTQacfvttwetOCKKDHwa+Y3z63bHlStXvEbMMpkMV65cCUpRRER0ld+rO/7xH//RI6jtdju2bduG+Ph497F9+/YFtjoioijnV0ivW7fO69jq1asDXgwREXnyK6R3794d7DqIiMiHG3p8Vqg0NjYiLy8PGo0GlZWVMJkmflTP4OAgysrKoFarUVBQgLa2No/Xx8fHsXv3bmRlZWHJkiV45pln+BBdIhKesCHd2tqKhoYG1NTUoKWlBVarFVVVVT7bOhwOVFRUQKFQQKfTYfPmzaiurkZPT4+7zauvvoqOjg7827/9G/bv3w+9Xo+dO3eG6nKIiG6IsCHd3NyM8vJyFBYWYuHChaivr0dXVxfOnDnj1bazsxMGgwH19fVQqVR48MEHsWrVKjQ3NwMAnE4nDh48iK1btyInJwdqtRo7duzAkSNHcOnSpVBfGhGR34QMabvdjv7+fmRnZ7uPpaenIzU1FX19fV7t9Xo91Go1EhIS3MdycnLcbYeHh2GxWDzOl5mZCZfLhT//+c9BvBIiuhF8GvnXhAxpi8UCp9MJhcJz8btcLvf5VHKz2eyz7Vf3sL/632+2mTZtGmbOnHnN+9xEJA1uI/+a3+ukReZyuab0+mQoFIkBO1ewKZV8Ws63sU+8idgnpfnzcKjrLP5v9v8JeX2i9YeQIZ2cnAyZTAaTyYT58+e7j5vNZsjlcq/2CoUCQ0NDHse+Obq+5ZZbAFwdUaempgK4uovy4sWLXiPw6zGZRuF0Bi70g0WpTILRaJW6DKGwT7yJ2id3Km9C3QMLASCk9UnVH9f6xSDk7Y64uDhkZGSgu7vbfWx4eBgjIyPQaDRe7dVqNfR6PWw2m/tYV1eXu216ejqSk5M9ztfb24uYmBgsWrQoiFdCRDQ1QoY0AJSUlKCpqQnHjx9Hf38/tFotsrKyoFKpoNfrUVRU5H5CeX5+PlJSUqDVajEwMACdToeOjg6UlpYCuPo9I5s2bcJLL72Erq4u6PV6PPfcc1i9ejVmzpwp5WUSEV2TkLc7AGDDhg0wmUyora2F1WpFbm6ue12zzWbD4OAgHA4HgKsj78bGRtTU1KC4uBhKpRJ1dXXIzMx0n+8nP/kJxsbG8NOf/hQOhwP3338/nn32WUmujYjIXzGuQM6qRQHekw5f7BNv7BNPvCdNRESTwpAmIhIYQzqIrpw4BsejP8SVE8ekLoUoYkX67kSGdBA5Dx6Aa2gIzoMHpC6FKGJF+u5EhnQQyR4uQ8ycOZA9XCZ1KUQRa7XmNqTOmhGxD7kVdgleJJi2rBDTlhVKXQZRRIv0h9xyJE1EJDCGdIhxMpGIJoMhHWKcTCSiyWBIhxgnE4loMjhxGGKcTCSiyeBImohIYAxpIiKBMaQFwVUfRKERbtvIGdKC4KoPotAIt23kDGlBcNUHUWiE2zZyru4QBFd9EIVGuG0j50iaiEhgDGnBcUKRKLoxpAXHCUWi6MaQFhwnFImiGycOBccJRaLoxpE0EZHAGNJERAJjSIcprvogCg2pt5EzpMMUV30QhYbU28gZ0mGKqz6IQkPqbeRc3RGmuOqDKDSk3kbOkTQRkcAY0hHmqwlF2x/+IHUpRBQADOkI89WE4ujr+6QuhYgCgCEdYb6aUEx87MdSl0JEASBsSDc2NiIvLw8ajQaVlZUwma69RnFwcBBlZWVQq9UoKChAW1ubx+stLS3YtGkTNBoNCgoKglm6pKYtK0Tsb97AjJUrpS6FiAJAyJBubW1FQ0MDampq0NLSAqvViqqqqgnbOxwOVFRUQKFQQKfTYfPmzaiurkZPT4+7zZdffolly5Zh06ZNobgEIqKAEHIJXnNzM8rLy1FYeHWJWX19PZYvX44zZ85ApVJ5te/s7ITBYEB7ezsSEhKgUqnQ29uL5uZmZGZmAgAeeeQRAEBbWxuOHj0auosRxJUTx+A8eACyh8u4dI8ojAg3krbb7ejv70d2drb7WHp6OlJTU9HX1+fzPXq9Hmq1GgkJCe5jOTk5E7aPRtyhSBR8wdhCLtxI2mKxwOl0QqHwXDwul8thNpt9vsdsNvtsf7372DdCoUgM+DmDRalMcv+7bfPjGH19HxIf+zFmfON4tFFG8bVPhH3iaSr98Yf/dwqfXbqMP5z8K1Znzw1IPSEN6e3bt+Ott96a8PV169Zh69atkz6vy+WaSlmTYjKNwukM3efdKKUyCUaj9esD9+YB9+ZhFMDoN49HEa8+IfbJt0y1P1YuSsGRvs+wclHKpM5zrV8MIQ1prVaLp556asLX4+Pj8Z3vfAcymQwmkwnz5893v2Y2myGXy32+T6FQYGhoyOOYr9E1EVEwBWMLeUhDOikpCUlJ1/+rREZGBrq7u92TfsPDwxgZGYFGo/HZXq1WY//+/bDZbJgxYwYAoKura8L2REThQriJQwAoKSlBU1MTjh8/jv7+fmi1WmRlZblXduj1ehQVFcFgMAAA8vPzkZKSAq1Wi4GBAeh0OnR0dKC0tNR9TqPRiFOnTuHcuXOw2+04deoUTp06Jcn1iYTfS00kNuEmDgFgw4YNMJlMqK2thdVqRW5uLnbu3Ol+3WazYXBwEA6HAwAQFxeHxsZG1NTUoLi4GEqlEnV1de6ROHB1M8vevXvdf167di0A4PTp06G5KEF9c9UHl+YRiSfGFcpZtwgQthOHE4im9dOcJPPGPvEkVX8IM3FI4uH3UhOJTch70kREdBVDmrxwMpFIHAxp8sIt5ETiYEiTFz7klkgcnDgkL5xMJBIHR9JERAJjSBMRCYwhTX7jqg+i0GNIk9+46oMo9BjS5Deu+iAKPa7uIL9x1QdR6HEkTUQkMIY0TRknFImChyFNU8YJRaLgYUjTlHFCkSh4OHFIU8YJRaLg4UiaiEhgDGkiIoExpClouOqDaOoY0hQ0XPVBNHUMaQoarvogmjqu7qCg4aoPoqnjSJqISGAMaQo5TigS+Y8hTSHHCUUi/zGkKeQ4oUjkP04cUshxQpHIfxxJExEJjCFNRCQwhjQJg6s+iLwxpEkYXPVB5E3YkG5sbEReXh40Gg0qKythMpmu2X5wcBBlZWVQq9UoKChAW1ub+zWHw4E9e/Zg5cqV0Gg0KCgowCuvvILx8fFgXwZNAld9EHkTcnVHa2srGhoa8POf/xxpaWmor69HVVUVfvvb3/ps73A4UFFRgUWLFkGn06Gvrw/V1dVIS0tDZmYmLl++jIGBAVRVVUGlUuGTTz7BP//zP0Mmk2HLli0hvjqaCFd9EHkTMqSbm5tRXl6OwsKr/8HW19dj+fLlOHPmDFQqlVf7zs5OGAwGtLe3IyEhASqVCr29vWhubkZmZiaSkpLw+uuvu9vPmTMH5eXl6OjoYEgTkdCEu91ht9vR39+P7Oxs97H09HSkpqair6/P53v0ej3UajUSEhLcx3JyciZsDwAWiwVJSUmBK5yChhOKFM2EG0lbLBY4nU4oFAqP43K5HGaz2ed7zGazz/YT3cc+d+4cDh8+jB07dky6PoUicdLvkYpSGRm/hIxvHoRr+Cymvfl7KDcWT+lckdIngcQ+8SRaf4Q0pLdv34633nprwtfXrVuHrVu3Tvq8LpfL77YXL15ERUUFli1bhjVr1kz6s0ymUTid/n+eVJTKJBiNVqnLCIgrDz4M18EDuPLgpildUyT1SaCwTzxJ1R/X+sUQ0pDWarV46qmnJnw9Pj4e3/nOdyCTyWAymTB//nz3a2azGXK53Of7FAoFhoaGPI75Gl2PjY3h8ccfR1paGnbt2jWFK6FQ4oQiRbOQhnRSUpJf94EzMjLQ3d2NzMxMAMDw8DBGRkag0Wh8tler1di/fz9sNhtmzJgBAOjq6vJof/nyZTzxxBOIj4/Hyy+/jOnThbvTQ0TkRbiJQwAoKSlBU1MTjh8/jv7+fmi1WmRlZblXduj1ehQVFcFgMAAA8vPzkZKSAq1Wi4GBAeh0OnR0dKC0tBTA1SV6Tz75JMxmM+rq6nDx4kUYjUYYjUbJrpGIyB9CDic3bNgAk8mE2tpaWK1W5ObmYufOne7XbTYbBgcH4XA4AABxcXFobGxETU0NiouLoVQqUVdX5x6JGwwGdHZ2AgBWrFjh8VmnT58O0VVRIF05cQzOgwcge7iMt0IoosW4JjPrRpw4FITj0R/CNTSEmDlzEPubN/x6T6T3yY1gn3gSceJQyNsdRNfDLeQULYS83UF0PVzxQdGCI2kiIoExpCmicAs5RRqGNEUUfic1RRqGNEUUTihSpOHEIUUUTihSpOFImohIYAxpigpXThyDcf0GTihS2GFIU1RwHjyA8Y8HOaFIYYchTVFB9nAZpn/3u5xQpLDDiUOKCtOWFUK5sZjfU0FhhyNpIiKBMaSJiATGkKaoxm3kJDqGNEU1biMn0TGkKapxGzmJjqs7KKpxGzmJjiNpIiKBMaSJfOCEIomCIU3kAycUSRQMaSIfOKFIouDEIZEPnFAkUXAkTUQkMIY0EZHAGNJERAJjSBMRCYwhTUQkMIY0EZHAGNJERAJjSBMRCYwhTUQkMIY0EZHAuC18kmSyGKlL8Fs41Roq7BNv7BNPovVHjMvlckldBBER+cbbHUREAmNIExEJjCFNRCQwhjQRkcAY0kREAmNIExEJjCFNRCQwhjQRkcAY0kREAmNIExEJjCEd4V577TWsWbMGd999N773ve9h165dGBsbk7qskGpsbEReXh40Gg0qKythMpmkLkky/Hm4vp/85CdYsGABuru7pS4FAEM64n344Yd47LHH0NbWhhdeeAHvvvsudu3aJXVZIdPa2oqGhgbU1NSgpaUFVqsVVVVVUpclmWj/ebie9vZ22Gw2qcvw5KKo8vbbb7uWLl0qdRkhs3btWtfLL7/s/vPZs2ddKpXKdfr0aQmrEke0/Txcy/nz513f//73XSMjIy6VSuXq6uqSuiSXy+VycSQdZSwWC5KSkqQuIyTsdjv6+/uRnZ3tPpaeno7U1FT09fVJWJk4ounn4Xq0Wi0qKipw++23S12KB4Z0FLFardi3bx/Wr18vdSkhYbFY4HQ6oVAoPI7L5XKYzWaJqhJHtP08XEtLSwvGx8exceNGqUvxwi/9D1Pbt2/HW2+9NeHr69atw7/8y7+4/2y32/Hkk08iPT0djz/+eChKJIHx5+Fr586dw969e9HS0iJ1KT4xpMOUVqvFU089NeHr8fHx7n8fHx/H1q1bMTY2hv3792P69Oj4vz05ORkymQwmkwnz5893HzebzZDL5RJWJq1o/XmYyMmTJ/H5559jxYoVHsd/9KMfYd26daivr5eosqui+/+dMJaUlOTXvUSn04lt27bh7NmzOHDgAG666aYQVCeGuLg4ZGRkoLu7G5mZmQCA4eFhjIyMQKPRSFydNKL552Ei2dnZOHLkiMexBx54ALt27UJeXp5EVX2NIR3hnn32WXR3d+PXv/41HA4HjEYjgKv3ZadNmyZxdcFXUlKC+vp6LFy4EGlpaaivr0dWVhZUKpXUpUki2n8efElMTPT585CWlobZs2dLUJEnPuMwwi1YsMDn8RMnTiAtLS3E1UijsbERBw4cgNVqRW5uLnbu3IlbbrlF6rIkwZ8H/yxYsABvvPEGsrKypC6FIU1EJDIuwSMiEhhDmohIYAxpIiKBMaSJiATGkCYiEhhDmohIYAxpIiKBMaSJvmX79u1YsGABFixYgEWLFuG+++5DdXU1LBaLu82HH36ILVu2IDc3F3fddReWL1+Of/qnf8Kf//xnr/P95je/wcKFC7F7926fnzc6OoodO3YgKysLd999Nx577DGcPXs2aNdH4YUhTeTDvffei3fffRd/+tOfoNVqcfToUWzbtg3A1ae9lJSUYPr06Xj++efx9ttv46WXXkJqaiqee+45r3MdPnwYFRUVaG9vh91u93r96aefxnvvvYeXX34ZBw8ehMvlQnl5OS5fvhz066QwIOkjB4gEtG3bNtcjjzzicezVV191ZWRkuM6fP+9avHix69lnn/X53gsXLnj8+b333nPl5OS4HA6Ha9WqVa4jR454vP7xxx+7VCqV65133vE4x5133ulqbW0NzAVRWONImsgP8fHxcDqd0Ol0sNvtqKys9Nlu5syZHn8+dOgQHnjgAUyfPh1r167F4cOHPV7/4IMPEBsbi5ycHI9zqNVqvP/++4G/EAo7DGmi6/joo4/wu9/9DhqNBkajEYmJibj11luv+z6z2Yxjx45h3bp1AIA1a9bggw8+wMcff+xuYzQaMWvWLK9voLvlllvc31BH0Y0hTeRDT08P7rnnHqjVavzgBz9Aeno6XnjhBbgm8X1kra2tmD9/PjIyMgAAKSkpyM3NxZtvvunX+2NiYm6odoos/D5pIh/UajX27NmDadOmISUlBXFxcQCAefPmYXR0FOfPn7/maNrlckGn02FoaAiLFi1yH3c6nfif//kfbN26FXFxcVAqlbhw4QKuXLniMZo2mUyYO3du0K6PwgdH0kQ+xMfHY86cOUhLS3MHNACsXLkScXFxePXVV32+7+LFiwCArq4uDA8P4/e//z3a29s9/hkfH8exY8cAAEuWLIHD4UBXV5f7HJcuXUJfXx/+9m//NohXSOGCI2miSZg9ezaqq6tRXV0Nq9WKhx56COnp6bh48SJOnDiB7u5u/O53v0NLSwuWLl2Ke+65x+scBQUFOHToEP7+7/8e8+bNw7Jly1BbW4vnnnsOSUlJePHFFzF79mysWrVKgisk0XAkTTRJDz74IA4cOIAvv/wSVVVVKCoqwk9/+lN8+umn2LFjB0wmE06cOIGVK1f6fP+qVavQ09ODTz75BADw85//HJmZmdiyZQs2btwIp9OJffv2eTxMmKIXn8xCRCQwjqSJiATGkCYiEhhDmohIYAxpIiKBMaSJiATGkCYiEhhDmohIYAxpIiKBMaSJiAT2/wGDrNQyooZcHAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [5 0 5 0]\n",
      "f1 score_KNN: 0.0\n",
      "balanced accuracy_KNN: 0.5\n",
      "confusion matrix_KNN\n",
      "[[5 0]\n",
      " [5 0]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=20, |class 1|=20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [5 0 5 0]\n",
      "f1 score_KNN: 0.0\n",
      "balanced accuracy_KNN: 0.5\n",
      "confusion matrix_KNN\n",
      "[[5 0]\n",
      " [5 0]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=20, |class 1|=20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [5 0 5 0]\n",
      "f1 score_KNN: 0.0\n",
      "balanced accuracy_KNN: 0.5\n",
      "confusion matrix_KNN\n",
      "[[5 0]\n",
      " [5 0]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=20, |class 1|=20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_KNN: 1.0\n",
      "balanced accuracy_KNN: 1.0\n",
      "confusion matrix_KNN\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=20, |class 1|=20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [1 4 0 5]\n",
      "f1 score_KNN: 0.7142857142857143\n",
      "balanced accuracy_KNN: 0.6\n",
      "confusion matrix_KNN\n",
      "[[1 4]\n",
      " [0 5]]\n",
      "-> check results\n",
      "\n",
      "====== Step {shuffleStep + 1}/{self.numOfShuffles} =======\n",
      "-> Spliting data to slices\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=20, |class 1|=20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [5 0 5 0]\n",
      "f1 score_KNN: 0.0\n",
      "balanced accuracy_KNN: 0.5\n",
      "confusion matrix_KNN\n",
      "[[5 0]\n",
      " [5 0]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=20, |class 1|=20\n"
     ]
    },
    {
     "data": {
      "image/png": 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+GcDfjv8b89N+IHVJQvHm50fIkI6Li4NMJoNer8eUKVPs2w0GA+Lj40e0l8vlOH36tMM2Z6NrT9DrL8Jq9d0vBU9QKGLQ12eSugxhsX9c+65/5k5LwL6OrzB3WgL763s88flxFfJCTndEREQgOTkZbW1t9m09PT3o7e2FWq0e0V6lUkGr1cJsNtu3tba2Om1LRNeGy/WkIWRIA0BBQQFqa2tx6NAhdHZ2QqPRIDU1FUqlElqtFnl5edDpdACArKwsJCQkQKPRoKurCw0NDWhpacHy5cvt79fX14cTJ07g7NmzGBwcxIkTJ3DixAmpTo+IyC1CTncAwJIlS6DX61FRUQGTyYSMjAxs2rQJAGA2m9Hd3Q2LxQLg8si7pqYG5eXlWLRoERQKBSorK+3L74DLX2Z5+eWX7T8vXLgQAHDy5EnfnRQR0RiF2Dxw1e3SpUv45JNPMGvWLE/UJDTOSQce9o9r7B/X/GJO+syZM/jZz37mibciIqLvEXZOmoj8E9dTe5Zbc9J33nmnt+sgogDx/dufciXI9XMrpMPCwrBs2bIR98z4Tm9vL/7whz94tDAi8k/z1bdiX8dXvP2ph7gV0kqlErfddhvy8/Od7u/s7GRIExEAPqnc09yak7777rtHfKPv+6KiojBz5kyPFUVERJd5ZAleMOESvMDD/nGN/eOaXyzBAy4vwyMiIs+6rpAeGhrC/v37sWLFCuTl5XmqJiIKElyud3XX9LXwL774Anv37kVzczOGhoYwe/ZsvPrqq56ujYgCHJfrXZ3bIW2xWHDgwAHU19fj448/RkZGBs6fP4/m5uZRl+YREbnC5XpX51ZIb9u2Dc3NzZDL5Vi4cCFefPFFKBQKTJ8+HTIZv7RIRNeGy/Wuzq2Q/vOf/4wnn3wSq1atYigTEfmQW4m7du1a7N+/H/feey+2bduGU6dOebsuIiKCmyPpxx57DI899hhaW1uxd+9eLF68GLfffjtsNhsuXbrk7RqJiILWmFZ3pKWlIS0tDQaDAY2Njbh06RIeeeQRzJo1C/PmzcNDDz3krTqJKIgd/Uxvv8AYbHPY1zTBHB8fj1/84hd477338Nprr+Gmm25CZWWlp2sjIgLguFQv2Fz347PS09ORnp4Og8HgiXqIiEYI5qV6bo2kjx8/juXLl8NkGvn99G+++QaFhYXo6+vzeHFEREBwP6ncrZCura3FrFmzEBMz8iYgN910E1JSUlBbW+vx4oiIgp1bId3R0YHc3NxR9+fk5ODDDz/0WFFERHSZWyF97tw5xMbGjrp//Pjx0Ol0nqqJiIj+P7dCOiYmxuWtSM+cOeN0KoSIyNcC7c56boX0jBkz0NDQMOr+t99+G/fcc4/HiiIiulaBtlzPrZB+9NFH8e677+K5555zmNbQ6XSoqKjAe++9h0cffdRrRRIRuWu++lYkxo4LmOV6bj8+q7GxEc899xwsFguio6MBABcvXkR4eDg2btwYNN825OOzAg/7xzX2j2vefnyW219mWbx4MTIzM9HY2Ii+vj7YbDZMnjwZeXl5mDBhwnUVSEREzrkV0ufPn8f69evx/vvvw2q1Qq1W4/nnn8fEiRO9XR8RUVBza056+/bt6OjowOrVq/HMM89Ar9ejvLzc27UREQU9t0bS77//PrZs2YLs7GwAQFZWFubPnw+LxYLw8HCvFkhE5A3+cmc9t0bSOp0O06dPt/98++23Izw8nPfrICK/5S9L9dwK6eHh4REjZplMhuHhYa8URUTkbf6yVM/t1R2//OUvHYJ6cHAQ69atQ2RkpH3bzp07PVsdEZGX+MtDcN0K6fz8/BHb5s+f7/FiiIjIkVshvXXrVm/XQURETlzT47OIiMg3GNJERFch5Z31hA7pmpoaZGZmQq1Wo6SkBHr96B3U3d2NwsJCqFQqZGdno6mpyWH/0NAQtm7ditTUVMyYMQPPPvssLl265O1TIKIAIOVyPWFDurGxEdXV1SgvL0d9fT1MJhNKS0udtrVYLCguLoZcLkdDQwNWrlyJsrIytLe329u88soraGlpwe9//3vs2rULWq0WmzZt8tXpEJEfk3K5ntt3wfO1/Px8zJ49G08//TQAoKenB7m5ufjrX/8KpVLp0Pbw4cMoLS3F0aNHERUVBQB45plnMDAwgJdeeglWqxUZGRn49a9/jcWLFwMAjh49iscffxxHjx7FTTfd5HZdvAte4GH/uMb+cc3bd8ETciQ9ODiIzs5OpKWl2bclJSUhMTERHR0dI9prtVqoVCp7QANAenq6vW1PTw+MRqPD+6WkpMBms+GTTz7x4pkQEV0ft7/M4ktGoxFWqxVyueNC8/j4eBgMhhHtDQaD07bfzWF/9+/vtwkNDcX48eNdznM7I5dHj6m9KFz9pib2z9Wwf1zzZv8IGdJjdbUZG0/O6HC6I/Cwf1xj/7gWlNMdcXFxkMlkI0a5BoMB8fHxI9rL5XKnbb8bOd98880A4NBmeHgYFy5cGDECJyK6Vt5YqidkSEdERCA5ORltbW32bT09Pejt7YVarR7RXqVSQavVwmw227e1trba2yYlJSEuLs7h/Y4dO4aQkBBMmzbNi2dCRMHEG0v1hAxpACgoKEBtbS0OHTqEzs5OaDQapKamQqlUQqvVIi8vz/5Q3KysLCQkJECj0aCrqwsNDQ1oaWnB8uXLAVy+Y9+yZcuwfft2tLa2QqvVYsuWLZg/fz7Gjx8v5WkSUQDxxlI9YeeklyxZAr1ej4qKCphMJmRkZNjXNZvNZnR3d8NisQC4PPKuqalBeXk5Fi1aBIVCgcrKSqSkpNjf76mnnkJ/fz+efvppWCwWPPDAA9i4caMk50ZEgckbd9YTdp20qHjhMPCwf1xj/7gWlBcOiYjoMoY0EZHAGNJERAJjSBMRCYwhTUQkMIY0EZHAGNJERAJjSBMRCYwhTUQkMIY0EZHAGNJERAJjSBMRCYwhTUQkMIY0EZHAGNJERAJjSBMRCYwhTUQkMIY0EZHAGNJERAJjSHvR8OGDsDz2MwwfPih1KUTkpxjSXmR9azdsp0/D+tZuqUshIj/FkPYi2SOFCJk0CbJHCqUuhYj8VJjUBQSy0Jw5CM2Z47Bt+PBBWN/aDdkjhSP2ERFdiSNpH+MUCBGNBUPaxzgFQkRjwekOH3M2BUJENBqOpAXApXpENBqGtAA4T01Eo2FIC4Dz1EQ0Gs5JC4BL9YhoNBxJC4pTIEQEMKSFxSkQIgI43SEsLtUjIoAjab/D5XpEwYUh7Wc4V00UXBjSfoZz1UTBhXPSfoZz1UTBRdiRdE1NDTIzM6FWq1FSUgK9Xu+yfXd3NwoLC6FSqZCdnY2mpiaH/fX19Vi2bBnUajWys7O9WToRkccIGdKNjY2orq5GeXk56uvrYTKZUFpaOmp7i8WC4uJiyOVyNDQ0YOXKlSgrK0N7e7u9zbfffoucnBwsW7bMF6dAROQRQk531NXVoaioCHPmXP6zvqqqCrm5uTh16hSUSuWI9keOHIFOp0NzczOioqKgVCpx7Ngx1NXVISUlBQCwYsUKAEBTUxMOHDjgu5MhIroOwo2kBwcH0dnZibS0NPu2pKQkJCYmoqOjw+lrtFotVCoVoqKi7NvS09NHbU9E5C+EG0kbjUZYrVbI5XKH7fHx8TAYDE5fYzAYnLa/2jz2tZDLoz3+nr6gUMRIXYLQ2D+usX9c82b/+DSk169fj3feeWfU/fn5+VizZs2Y39dms11PWWOi11+E1eq743mCQhGDvj6T1GUIi/3jGvvHNU/0j6uQ92lIazQarF27dtT9kZGRuOGGGyCTyaDX6zFlyhT7PoPBgPj4eKevk8vlOH36tMM2Z6NrIiJ/49OQjomJQUzM1f8sSE5ORltbm/2iX09PD3p7e6FWq522V6lU2LVrF8xmM8aNGwcAaG1tHbU9EZG/EO7CIQAUFBSgtrYWhw4dQmdnJzQaDVJTU+0rO7RaLfLy8qDT6QAAWVlZSEhIgEajQVdXFxoaGtDS0oLly5fb37Ovrw8nTpzA2bNnMTg4iBMnTuDEiROSnB8RkbuEu3AIAEuWLIFer0dFRQVMJhMyMjKwadMm+36z2Yzu7m5YLBYAQEREBGpqalBeXo5FixZBoVCgsrLSPhIHLn+Z5eWXX7b/vHDhQgDAyZMnfXNSRETXIMTmy6tuAYAXDgMP+8c19o9r3r5wKOR0BxERXcaQJiISGEOaiEhgDGkiIoExpImIBMaQJiISGEOa3MaH4BL5HkOa3MaH4BL5HkOa3MaH4BL5npBfCycx8SG4RL7HkTRdF85TE3kXQ5quC+epibyLIU3XhfPURN7FOWm6LqPNUw8fPgjrW7she6SQ89hE14EjafIKToMQeQZDmryC0yBEnsHpDvIKLtcj8gyOpMlnuFyPaOwY0uQznKcmGjuGNPkM56mJxo5z0uQzzuapuVSPyDWOpElSnAIhco0hTZLiFAiRa5zuIElxqR6RaxxJk3C4VI/oPxjSJBzOUxP9B0OahMN5aqL/4Jw0CYd31iP6D46kyW9wGoSCEUOa/AanQSgYcbqD/AaX61Ew4kia/BqX61GgY0iTX+M8NQU6hjT5Nc5TU6DjnDT5Nd5ZjwIdR9IUcDgFQoGEIU0Bh1MgFEiEDemamhpkZmZCrVajpKQEer3eZfvu7m4UFhZCpVIhOzsbTU1N9n0WiwXbtm3D3LlzoVarkZ2djZdeeglDQ0PePg2SQGjOHIT/6Q1OdVBAEDKkGxsbUV1djfLyctTX18NkMqG0tHTU9haLBcXFxZDL5WhoaMDKlStRVlaG9vZ2AMDAwAC6urpQWlqKffv2oby8HHv27EF1dbWvTokkxqV65K+EvHBYV1eHoqIizJlzeSRUVVWF3NxcnDp1CkqlckT7I0eOQKfTobm5GVFRUVAqlTh27Bjq6uqQkpKCmJgYvP766/b2kyZNQlFREVpaWrBq1SqfnRdJ5/vz1Bxhkz8RbiQ9ODiIzs5OpKWl2bclJSUhMTERHR0dTl+j1WqhUqkQFRVl35aenj5qewAwGo2IiYnxXOEkNM5Tk78SbiRtNBphtVohl8sdtsfHx8NgMDh9jcFgcNp+tHnss2fPYu/evdiwYcOY65PLo8f8GhEoFEH+C2nposv/XMH8t7+h7/U/IfrxxzBu7lwJCvMPQf/5uQpv9o9PQ3r9+vV45513Rt2fn5+PNWvWjPl9bTab220vXLiA4uJi5OTkYMGCBWM+ll5/EVar+8cTgUIRg74+k9RlCMny6h8R0nMGxlf/iIszM6UuR0j8/Ljmif5xFfI+DWmNRoO1a9eOuj8yMhI33HADZDIZ9Ho9pkyZYt9nMBgQHx/v9HVyuRynT5922OZsdN3f348nnngCEydOxObNm6/jTChQyB4pROjbf8HwQ8ukLoXIKZ+GdExMjFvzwMnJyWhra0NKSgoAoKenB729vVCr1U7bq1Qq7Nq1C2azGePGjQMAtLa2OrQfGBjAk08+icjISOzYsQNhYcLN9JAEQnPmQLF0EUeKJCzhLhwCQEFBAWpra3Ho0CF0dnZCo9EgNTXVvrJDq9UiLy8POp0OAJCVlYWEhARoNBp0dXWhoaEBLS0tWL58OYDLS/RWr14Ng8GAyspKXLhwAX19fejr65PsHElcXK5HIhFyOLlkyRLo9XpUVFTAZDIhIyMDmzZtsu83m83o7u6GxWIBAERERKCmpgbl5eVYtGgRFAoFKisr7SNxnU6HI0eOAADuv/9+h2OdPHnSR2dF/oLL9UgkIbaxXHUjXjgMQFf2D2/Q5IifH9cC6sIhkT/gnfVIJELOSROJhnfWI6kwpIncwG8sklQ43UHkBj4El6TCkTTRNeJSPfIFhjTRNeI8NfkCQ5roGnGemnyBc9JE12i0eWou1yNP4kiayMM4DUKexJAm8jBOg5AncbqDyMO4XI88iSNpIiKBMaSJiATGkCYiEhhDmohIYAxpIiKBMaSJiATGkCYiEhhDmohIYAxpIiKBMaSJiATGr4WPkUwWInUJ18Rf6/YV9o9r7B/XvNk/ITabzea1dyciouvC6Q4iIoExpImIBMaQJiISGEOaiEhgDGkiIoExpImIBMaQJiISGEOaiEhgDGkiIoExpImIBMaQDiKvvvoqFixYgLvvvhv33nsvNm/ejP7+fqnLklRNTQ0yMzOhVqtRUlICvV4vdUlC4GfFfU899RSmTp2KtrY2r7w/QzqIfPzxx3j88cfR1NSEF154AR988AE2b94sdVmSaWxsRHV1NcrLy1FfXw+TyYTS0lKpyxICPyvuaW5uhtls9u5BbBS09u/fb5s1a5bUZUhm4cKFth07dth/PnPmjE2pVNpOnjwpYVViCvbPijPnzp2z3Xfffbbe3l6bUqm0tba2euU4HEkHMaPRiJiYGKnLkMTg4CA6OzuRlpZm35aUlITExER0dHRIWJmYgvmzMhqNRoPi4mLcdtttXj0OQzpImUwm7Ny5E4sXL5a6FEkYjUZYrVbI5XKH7fHx8TAYDBJVJaZg/6w4U19fj6GhISxdutTrx+JN/wPA+vXr8c4774y6Pz8/H7/5zW/sPw8ODmL16tVISkrCE0884YsSyU/xszLS2bNn8fLLL6O+vt4nx2NIBwCNRoO1a9eOuj8yMtL+30NDQ1izZg36+/uxa9cuhIUF50cgLi4OMpkMer0eU6ZMsW83GAyIj4+XsDJx8LPi3PHjx/H111/j/vvvd9j+85//HPn5+aiqqvLo8djrASAmJsat+UKr1Yp169bhzJkz2L17N2688UYfVCemiIgIJCcno62tDSkpKQCAnp4e9Pb2Qq1WS1yd9PhZGV1aWhr27dvnsO3BBx/E5s2bkZmZ6fHjMaSDyMaNG9HW1obXXnsNFosFfX19AC7Pw4aGhkpcne8VFBSgqqoKd9xxByZOnIiqqiqkpqZCqVRKXZrk+FkZXXR0tNPPyMSJEzFhwgSPH4/POAwiU6dOdbr98OHDmDhxoo+rEUNNTQ12794Nk8mEjIwMbNq0CTfffLPUZUmOn5WxmTp1Kt544w2kpqZ6/L0Z0kREAuMSPCIigTGkiYgExpAmIhIYQ5qISGAMaSIigTGkiYgExpAmIhIYQ5roCuvXr8fUqVMxdepUTJs2DbNnz0ZZWRmMRqO9zccff4xVq1YhIyMDd911F3Jzc/GrX/0Kn3zyyYj3+9Of/oQ77rgDW7dudXq8ixcvYsOGDUhNTcXdd9+Nxx9/HGfOnPHa+ZF/YUgTOTFz5kx88MEH+Pvf/w6NRoMDBw5g3bp1AC4/0aWgoABhYWF4/vnnsX//fmzfvh2JiYnYsmXLiPfau3cviouL0dzcjMHBwRH7n3nmGRw9ehQ7duzAW2+9BZvNhqKiIgwMDHj9PMkPeOVRAkR+bN26dbYVK1Y4bHvllVdsycnJtnPnztnuvPNO28aNG52+9vz58w4/Hz161Jaenm6zWCy2efPm2fbt2+ew//PPP7cplUrb+++/7/Ae06dPtzU2NnrmhMivcSRN5IbIyEhYrVY0NDRgcHAQJSUlTtuNHz/e4ec9e/bgwQcfRFhYGBYuXIi9e/c67P/oo48QHh6O9PR0h/dQqVT48MMPPX8i5HcY0kRX8emnn+LNN9+EWq1GX18foqOjccstt1z1dQaDAQcPHkR+fj4AYMGCBfjoo4/w+eef29v09fUhNjZ2xJ3lbr75Zvud5yi4MaSJnGhvb8c999wDlUqFn/70p0hKSsILL7wA2xjuR9bY2IgpU6YgOTkZAJCQkICMjAy8/fbbbr0+JCTkmmqnwML7SRM5oVKpsG3bNoSGhiIhIQEREREAgMmTJ+PixYs4d+6cy9G0zWZDQ0MDTp8+jWnTptm3W61W/POf/8SaNWsQEREBhUKB8+fPY3h42GE0rdfr8YMf/MBr50f+gyNpIiciIyMxadIkTJw40R7QADB37lxERETglVdecfq6CxcuAABaW1vR09ODv/zlL2hubnb4Z2hoCAcPHgQAzJgxAxaLBa2trfb3+Oabb9DR0YEf/ehHXjxD8hccSRONwYQJE1BWVoaysjKYTCY8/PDDSEpKwoULF3D48GG0tbXhzTffRH19PWbNmoV77rlnxHtkZ2djz549+MlPfoLJkycjJycHFRUV2LJlC2JiYvDiiy9iwoQJmDdvngRnSKLhSJpojB566CHs3r0b3377LUpLS5GXl4enn34aX375JTZs2AC9Xo/Dhw9j7ty5Tl8/b948tLe344svvgAA/Pa3v0VKSgpWrVqFpUuXwmq1YufOnQ4PEKbgxSezEBEJjCNpIiKBMaSJiATGkCYiEhhDmohIYAxpIiKBMaSJiATGkCYiEhhDmohIYAxpIiKB/T8SXeQM7r6H7QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [5 0 5 0]\n",
      "f1 score_KNN: 0.0\n",
      "balanced accuracy_KNN: 0.5\n",
      "confusion matrix_KNN\n",
      "[[5 0]\n",
      " [5 0]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=20, |class 1|=20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [5 0 5 0]\n",
      "f1 score_KNN: 0.0\n",
      "balanced accuracy_KNN: 0.5\n",
      "confusion matrix_KNN\n",
      "[[5 0]\n",
      " [5 0]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=20, |class 1|=20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_KNN: 1.0\n",
      "balanced accuracy_KNN: 1.0\n",
      "confusion matrix_KNN\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=20, |class 1|=20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [1 4 0 5]\n",
      "f1 score_KNN: 0.7142857142857143\n",
      "balanced accuracy_KNN: 0.6\n",
      "confusion matrix_KNN\n",
      "[[1 4]\n",
      " [0 5]]\n",
      "-> check results\n",
      "\n",
      "====== Step {shuffleStep + 1}/{self.numOfShuffles} =======\n",
      "-> Spliting data to slices\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=20, |class 1|=20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [5 0 5 0]\n",
      "f1 score_KNN: 0.0\n",
      "balanced accuracy_KNN: 0.5\n",
      "confusion matrix_KNN\n",
      "[[5 0]\n",
      " [5 0]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=20, |class 1|=20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [5 0 5 0]\n",
      "f1 score_KNN: 0.0\n",
      "balanced accuracy_KNN: 0.5\n",
      "confusion matrix_KNN\n",
      "[[5 0]\n",
      " [5 0]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=20, |class 1|=20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [5 0 5 0]\n",
      "f1 score_KNN: 0.0\n",
      "balanced accuracy_KNN: 0.5\n",
      "confusion matrix_KNN\n",
      "[[5 0]\n",
      " [5 0]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=20, |class 1|=20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_KNN: 1.0\n",
      "balanced accuracy_KNN: 1.0\n",
      "confusion matrix_KNN\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=20, |class 1|=20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [1 4 0 5]\n",
      "f1 score_KNN: 0.7142857142857143\n",
      "balanced accuracy_KNN: 0.6\n",
      "confusion matrix_KNN\n",
      "[[1 4]\n",
      " [0 5]]\n",
      "-> check results\n",
      "### Exercise is done.\n"
     ]
    }
   ],
   "source": [
    "exercise.run(gan, loadEqualDataset())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "forward-throat",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Start exercise for synthetic point generator\n",
      "\n",
      "====== Step {shuffleStep + 1}/{self.numOfShuffles} =======\n",
      "-> Spliting data to slices\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=9, |class 1|=20\n",
      "-> create 11 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [3 0 3 2]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 0.5714285714285715\n",
      "balanced accuracy_LR: 0.7\n",
      "confusion matrix_LR\n",
      "[[3 0]\n",
      " [3 2]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [3 0 5 0]\n",
      "f1 score_KNN: 0.0\n",
      "balanced accuracy_KNN: 0.5\n",
      "confusion matrix_KNN\n",
      "[[3 0]\n",
      " [5 0]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=9, |class 1|=20\n",
      "-> create 11 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [3 0 4 1]\n",
      "f1 score_KNN: 0.33333333333333337\n",
      "balanced accuracy_KNN: 0.6\n",
      "confusion matrix_KNN\n",
      "[[3 0]\n",
      " [4 1]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=9, |class 1|=20\n",
      "-> create 11 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "f1 score_KNN: 1.0\n",
      "balanced accuracy_KNN: 1.0\n",
      "confusion matrix_KNN\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=9, |class 1|=20\n",
      "-> create 11 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [1 2 0 5]\n",
      "f1 score_KNN: 0.8333333333333333\n",
      "balanced accuracy_KNN: 0.6666666666666666\n",
      "confusion matrix_KNN\n",
      "[[1 2]\n",
      " [0 5]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=12, |class 1|=20\n",
      "-> create 8 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [5]\n",
      "f1 score_KNN: 1.0\n",
      "balanced accuracy_KNN: 1.0\n",
      "confusion matrix_KNN\n",
      "[[5]]\n",
      "-> check results\n",
      "\n",
      "====== Step {shuffleStep + 1}/{self.numOfShuffles} =======\n",
      "-> Spliting data to slices\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=9, |class 1|=20\n",
      "-> create 11 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [3 0 3 2]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 0.5714285714285715\n",
      "balanced accuracy_LR: 0.7\n",
      "confusion matrix_LR\n",
      "[[3 0]\n",
      " [3 2]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [3 0 5 0]\n",
      "f1 score_KNN: 0.0\n",
      "balanced accuracy_KNN: 0.5\n",
      "confusion matrix_KNN\n",
      "[[3 0]\n",
      " [5 0]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=9, |class 1|=20\n",
      "-> create 11 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [3 0 4 1]\n",
      "f1 score_KNN: 0.33333333333333337\n",
      "balanced accuracy_KNN: 0.6\n",
      "confusion matrix_KNN\n",
      "[[3 0]\n",
      " [4 1]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=9, |class 1|=20\n",
      "-> create 11 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "f1 score_KNN: 1.0\n",
      "balanced accuracy_KNN: 1.0\n",
      "confusion matrix_KNN\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=9, |class 1|=20\n",
      "-> create 11 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [1 2 0 5]\n",
      "f1 score_KNN: 0.8333333333333333\n",
      "balanced accuracy_KNN: 0.6666666666666666\n",
      "confusion matrix_KNN\n",
      "[[1 2]\n",
      " [0 5]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=12, |class 1|=20\n",
      "-> create 8 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [5]\n",
      "f1 score_KNN: 1.0\n",
      "balanced accuracy_KNN: 1.0\n",
      "confusion matrix_KNN\n",
      "[[5]]\n",
      "-> check results\n",
      "\n",
      "====== Step {shuffleStep + 1}/{self.numOfShuffles} =======\n",
      "-> Spliting data to slices\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=9, |class 1|=20\n",
      "-> create 11 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [3 0 3 2]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 0.5714285714285715\n",
      "balanced accuracy_LR: 0.7\n",
      "confusion matrix_LR\n",
      "[[3 0]\n",
      " [3 2]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [3 0 5 0]\n",
      "f1 score_KNN: 0.0\n",
      "balanced accuracy_KNN: 0.5\n",
      "confusion matrix_KNN\n",
      "[[3 0]\n",
      " [5 0]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=9, |class 1|=20\n",
      "-> create 11 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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V+/fvV21trXJycpScnCybzaaamhrNnTtXjY2NkqSZM2dq7NixysnJ0cmTJ1VaWqrdu3crMzPTf7ympiYdO3ZMZ86cUVtbm44dO6Zjx44FtGbgy1InWvXSonuMGOrAwBWQMenTp0/r+9//fkBDb/HixXI6ncrPz5fb7VZaWpo2btwoSWptbVVdXZ08Ho+kqz3v4uJi5eXladGiRYqPj1dBQYH/9jvp6mKWN954w//zggULJEnHjx8PWM0AEGgRvgDMutXW1mrhwoVDomfKxGHwUWtwUGtwBHvisE896Xvuuee6CgAAXJs+hfTw4cO1ZMmSLiv9vtDQ0KB//Md/DGhhAIA+hrTNZtPNN9+shQsXdru/traWkAaAIOjT3R1TpkzRqVOnetwfFRWl++67L2BFAQCu6lNP+m/+5m963f/Hf/zH2rZtW0AKAoYa01YmwiwBW8xy+vTpQB0KGFJYmYjeXFdIt7e3a8+ePVq2bJnmzp0bqJqAIYWViejNNS1m+cMf/qCdO3eqoqJC7e3tmjVrlt56661A1wYMCbw3Eb3pc0h7PB7t3btXJSUl+vDDD5WWlqaWlhZVVFT0eGseAOD69CmkN23apIqKClmtVi1YsECvvvqq4uPjdffdd8tiMfIZTQAwKPQppP/1X/9VTz75pFavXk0oA0AI9Slx16xZoz179uj+++/Xpk2bdOLEiWDXBQBQH3vSTzzxhJ544glVVlZq586deuSRR3THHXfI5/Pp8uXLwa4RAIasft3dkZKSopSUFLlcLpWVleny5ct6/PHHNX36dM2bN0+PPvposOoEgCHpmgaY4+Li9Bd/8Rf69a9/rZ/+9Ke68cYbVVBQEOjaAHzJoY+dWl/+Ox36OPAvWIa5rvuh/6mpqUpNTe32Ld4AAufLKxO5r3ro6FNP+ujRo8rMzJTb3fXB1hcuXFBWVpaampoCXhyA/8PKxKGpTyG9detWTZ8+XTExXd8ecOONNyopKUlbt24NeHEA/g/vTBya+hTS1dXVmj17do/7MzIy9P777wesKADAVX0K6XPnzmnMmDE97h89erT/zd0AgMDpU0jHxMT0+ijS06dPdzsUAgC4Pn0K6WnTpqm0tLTH/e+8846mTp0asKIAAFf16Ra8P//zP1dmZqZuvPFGPfnkkxo3bpwkqbGxUW+99ZZ+/etf82YWAAiCPoX01KlTVVBQoOeff14lJSWKjo6WJF28eFEjRoxQfn6+pk2bFtRCAWAo6vNilkceeUQzZsxQWVmZmpqa5PP5dNttt2nu3Ln+njUAM/DexMGjTyHd0tKidevW6d1335XX65XD4dDLL7+sCRMmBLs+ANeA1YmDR58mDl977TVVV1frqaee0rPPPiun06m8vLxg1wbgGrE6cfDoU0/63Xff1Ysvvqj09HRJ0syZMzV//nx5PB6NGDEiqAUC6D/emzh49Kkn3djYqLvvvtv/8x133KERI0bwvA4ACLI+hXRHR0eXHrPFYlFHR0dQigIAXNXnuzv+8i//slNQt7W1ae3atRo5cqR/25YtWwJbHQAMcX0K6YULF3bZNn/+/IAXAwDorE8h/dJLLwW7DgBAN67p9VkAgNAgpIEhjPcmmo+QBoawL69MhJkIaWAIY2Wi+a77beEABi5WJpqPnjQAGIyQBgCDEdIAYDBCGgAMRkgDgMEIaQAwGCENoE9YnRgehDSAPmF1YngQ0gD6hNWJ4cGKQwB9wurE8KAnDQAGI6QBwGCENAAYjJAGAIMR0gBgMEIaAAxmdEgXFxdrxowZcjgcWrVqlZzOnlc61dXVKSsrS3a7Xenp6SovL++0v729XS+99JKSk5M1bdo0rV+/XpcvXw72JQBDDisTA8vYkC4rK1NRUZHy8vJUUlIit9ut7Ozsbtt6PB6tWLFCVqtVpaWlWrlypXJzc3X48GF/mzfffFO7d+/WP/zDP+hnP/uZampqtHHjxlBdDjBksDIxsIwN6e3bt2v58uWaM2eO7rzzThUWFqqyslInTpzo0vbgwYNqbGxUYWGhbDabHn30Uc2bN0/bt2+XJHm9Xr399tt65plnlJqaKrvdrg0bNmjXrl26cOFCqC8NGNRYmRhYRoZ0W1ubamtrlZKS4t+WmJiohIQEVVdXd2lfU1Mju92uqKgo/7bU1FR/2/r6ejU3N3c6XlJSknw+n37/+98H8UqAoSd1olUvLbqH1YkBYuSy8ObmZnm9Xlmtnf8jx8XFyeVydWnvcrm6bfvFGPYX//5ym2HDhmn06NG9jnN3x2qN7vRzfHxMvz4fTtQaHNQaHNR6lZEh3V8+n++69veH03lRXu/V48XHx6ipyR2wYwcTtQYHtQbHUKu1t5A3crgjNjZWFoulSy/X5XIpLi6uS3ur1dpt2y96zjfddJMkdWrT0dGh8+fPd+mBA4BJjAzpyMhITZ48WVVVVf5t9fX1amhokMPh6NLebrerpqZGra2t/m2VlZX+tomJiYqNje10vCNHjigiIkJ33XVXEK8EAK6PkSEtSUuXLtXWrVu1f/9+1dbWKicnR8nJybLZbKqpqdHcuXPV2NgoSZo5c6bGjh2rnJwcnTx5UqWlpdq9e7cyMzMlSRaLRUuWLNFrr72myspK1dTU6MUXX9T8+fM1evTocF4mAPTK2DHpxYsXy+l0Kj8/X263W2lpaf77mltbW1VXVyePxyPpas+7uLhYeXl5WrRokeLj41VQUKCkpCT/8X784x/r0qVLevrpp+XxePTAAw/oueeeC8u1AUBfRfgCOas2BDBxGHzUGhwm1nroY6d2VZ/VfMf4TrfsmVhrT4bkxCGAoYHViV+PkAYQNqxO/HrGjkkDGPx4b+LXoycNAAYjpAHAYIQ0ABiMkAYAgxHSAGAwQhoADEZIAzDeUH5vIiENwHhDeWUiIQ3AeEN5ZSIrDgEYbyivTKQnDQAGI6QBwGCENAAYjJAGAIMR0gBgMEIaAAxGSAMYVAbb6kRCGsCgMthWJxLSAAaVwbY6kRWHAAaVwbY6kZ40ABiMkAYAgxHSAGAwQhoADEZIA4DBuLsjiNqm3OX/c+Rvj4axEgADFT1pAEPSQFmZSEgDGJIGyspEhjuCiCEOwFzzHeO1q/qs8SsTCWkAQ9JAWZnIcEeIdRzYJ88T31fHgX3hLgXAAEBIh5j37W3ynTol79vbwl0KgAGAkA4xy+NZirjlFlkezwp3KQAGAMakQ2xYxhwNy5gT7jIADBD0pAHAYIS0AZhMBNATQtoATCYCZgvn6kRC2gBMJgJmC+fqRCYODcBkImC2cK5OJKQB4GuEc3Uiwx0AYDBCGgAMRkgDgMEIaQAwGCENAAYjpAHAYIQ0AARIMFYmEtIAECDBWJlISANAgMx3jFfCmFEBXZnIikMACJBgrEw0tiddXFysGTNmyOFwaNWqVXI6ex/jqaurU1ZWlux2u9LT01VeXt5pf0lJiZYsWSKHw6H09PRglg4AAWNkSJeVlamoqEh5eXkqKSmR2+1WdnZ2j+09Ho9WrFghq9Wq0tJSrVy5Urm5uTp8+LC/zeeff66MjAwtWbIkFJcAAAFh5HDH9u3btXz5cs2Zc/XJcIWFhZo9e7ZOnDghm83Wpf3BgwfV2NioiooKRUVFyWaz6ciRI9q+fbuSkpIkScuWLZMklZeXa+/evaG7GAC4Dsb1pNva2lRbW6uUlBT/tsTERCUkJKi6urrbz9TU1MhutysqKsq/LTU1tcf2ADBQGBfSzc3N8nq9slo7D77HxcXJ5XJ1+xmXy9Vt+68bxwYA04V0uGPdunX65S9/2eP+hQsX6plnnun3cX0+3/WU1S9Wa3Snn+PjY0J27utFrcFBrcFBrVeFNKRzcnK0Zs2aHvePHDlSf/RHfySLxSKn06mJEyf697lcLsXFxXX7OavVqlOnTnXa1l3vOhCczovyeq/+pRAfH6OmJnfAzxEIHQf2yfv2Nlkez9KwjDlG1/pV1Boc1Bocgai1t5AP6XBHTEyM4uPje/wnJiZGkZGRmjx5sqqqqvyfq6+vV0NDgxwOR7fHtdvtqqmpUWtrq39bZWVlj+2HAl5uCwwOxo1JS9LSpUu1detW7d+/X7W1tcrJyVFycrL/zo6amhrNnTtXjY2NkqSZM2dq7NixysnJ0cmTJ1VaWqrdu3crMzPTf8ympiYdO3ZMZ86cUVtbm44dO6Zjx46F5fpCgZfbAoODkbfgLV68WE6nU/n5+XK73UpLS9PGjRv9+1tbW1VXVyePxyNJioyMVHFxsfLy8rRo0SLFx8eroKDAf/uddHUxyxtvvOH/ecGCBZKk48ePh+aiQoyX2wKDQ4QvlLNug8BAGZP+KmoNDmoNjqFWqzFj0givjgP75Hni++o4sC/cpQDoI0J6CGEyERh4COkhhMlEYOAxcuIQwcFkIjDw0JMGAIMR0mBCETAYIQ0mFAGDEdJgQhEwGBOHYEIRMBg9aQAwGCGNbjGZCJiBkEa3mEwEzEBIo1tMJgJmYOIQ3WIyETADPWkAMBghjT5jMhEIPUIafcZkIhB6hDT6jMlEIPSYOESfMZkIhB49aQAwGCGN68JkIhBchDSuC5OJQHAR0rguTCYCwcXEIa4Lk4lAcNGTBgCDEdIAYDBCGgAMRkgDgMEIaQAwGCENAAYjpAHAYIQ0ABiMkAYAg7HisJ8slohefzYZtQYHtQYHtV4V4fP5fEE7OgDgujDcAQAGI6QBwGCENAAYjJAGAIMR0gBgMEIaAAxGSAOAwQhpADAYIQ0ABiOkAcBghHQAvPXWW3r44Yc1ZcoU3X///XrhhRd06dKlcJfVrb1792rZsmX65je/qUmTJoW7nC6Ki4s1Y8YMORwOrVq1Sk6nM9wldWH67/DLBtJ3U5I2b96sBx54QHa7XWlpaVqzZo2amprCXVavfvzjH2vSpEmqqqoKyvEJ6QD48MMP9cMf/lDl5eV65ZVX9N577+mFF14Id1ndam1tVUpKin70ox+Fu5QuysrKVFRUpLy8PJWUlMjtdis7OzvcZXVh8u/wqwbSd1OSbr/9dj3//PPas2ePioqKdPbsWa1bty7cZfWooqJCra2twT2JDwG3Z88e3/Tp08NdRq8qKyt9Npst3GV0smDBAt/rr7/u//n06dM+m83mO378eBir6pmJv8OvMxC+m1924MAB35QpU8JdRrfOnTvn+/a3v+1raGjw2Ww2X2VlZVDOQ086CJqbmxUTExPuMgaUtrY21dbWKiUlxb8tMTFRCQkJqq6uDmNlg8tA+m663W796le/0rRp08JdSrdycnK0YsUK3XzzzUE9DyEdYG63W1u2bNEjjzwS7lIGlObmZnm9Xlmt1k7b4+Li5HK5wlTV4DJQvpu7du3S1KlTdd999+nTTz/VK6+8Eu6SuigpKVF7e7see+yxoJ+Lh/73Yt26dfrlL3/Z4/6FCxfqb//2b/0/t7W16amnnlJiYmLIxyv7WyuGlnB+N/srPT1ddrtdZ8+e1U9+8hPl5uZq8+bN4S7L78yZM3rjjTdUUlISkvMR0r3IycnRmjVretw/cuRI/5/b29v1zDPP6NKlS/rZz36m4cND+6vtT60mio2NlcVikdPp1MSJE/3bXS6X4uLiwljZwBfu72Z/RUdHKzo6Wrfeeqtuv/123X///froo490xx13hLs0SdLRo0f12Wef6Tvf+U6n7T/4wQ+0cOFCFRYWBvR8Zv/XCrOYmJg+jd95vV6tXbtWp0+f1rZt23TDDTeEoLrO+lqrqSIjIzV58mRVVVUpKSlJklRfX6+GhgY5HI4wVzdwmfDdvB6+///iKIvFnJHZlJQU7dq1q9O2hx56SC+88IJmzJgR8PMR0gHw3HPPqaqqSj/96U/l8Xj893XGxcVp2LBhYa6us5aWFp09e1anT5+WJB07dkySNHHiREVGRoazNC1dulSFhYW68847NWHCBBUWFio5OVk2my2sdX2Vyb/DrxpI302Px6M33nhDGRkZslqtOnv2rF5//XXdfffduvXWW8Ndnl90dHS338kJEyZo3LhxAT8f7zgMgJ4WNBw4cEATJkwIcTW9Ky8v1/r167tsN6XW4uJibdu2TW63W2lpadq4caNuuummcJfViem/wy8bSN/N9vZ2/dVf/ZV++9vfqqWlRfHx8frWt76lp59+WmPHjg13eb2aNGmSfv7znys5OTngxyakAcBg5gz0AAC6IKQBwGCENAAYjJAGAIMR0gBgMEIaAAxGSAOAwQhp4CvWrVunSZMmadKkSbrrrrs0a9Ys5ebmqrm52d/mww8/1OrVq5WWlqZ7771Xs2fP1l//9V/r97//fZfj/cu//IvuvPNOvfTSS92e7+LFi9qwYYOSk5M1ZcoU/fCHP/SvZgQIaaAb9913n9577z395je/UU5Ojvbu3au1a9dKuvoGmaVLl2r48OF6+eWXtWfPHr322mtKSEjQiy++2OVYO3fu1IoVK1RRUaG2trYu+5999lkdOnRIr7/+ut5++235fD4tX75cV65cCfp1YgAIyqsEgAFs7dq1vmXLlnXa9uabb/omT57sO3funO+ee+7xPffcc91+tqWlpdPPhw4d8qWmpvo8Ho9v3rx5vl27dnXa/8knn/hsNpvv3Xff7XSMu+++21dWVhaYC8KARk8a6IORI0fK6/WqtLRUbW1tWrVqVbftRo8e3ennHTt26KGHHtLw4cO1YMEC7dy5s9P+Dz74QCNGjFBqamqnY9jtdr3//vuBvxAMOIQ08DU++ugj/eIXv5DD4VBTU5Oio6P1jW9842s/53K5tG/fPi1cuFCS9PDDD+uDDz7QJ5984m/T1NSkMWPGdHki3U033WT8W7IRGoQ00I3Dhw9r6tSpstvt+u53v6vExES98sor/ucb90VZWZkmTpyoyZMnS5LGjh2rtLQ0vfPOO336fERExDXVjsGF50kD3bDb7dq0aZOGDRumsWPH+p8Tfdttt+nixYs6d+5cr71pn8+n0tJSnTp1SnfddZd/u9fr1f/8z//omWeeUWRkpOLj49XS0qKOjo5OvWmn02nUM5QRPvSkgW6MHDlSt9xyiyZMmNDpQf4PPvigIiMj9eabb3b7ufPnz0uSKisrVV9fr3/7t39TRUVFp3/a29u1b98+SdK0adPk8XhUWVnpP8aFCxdUXV2tb37zm0G8QgwU9KSBfhg3bpxyc3OVm5srt9ut733ve0pMTNT58+d14MABVVVV6Re/+IVKSko0ffp0TZ06tcsx0tPTtWPHDv3Zn/2ZbrvtNmVkZCg/P18vvviiYmJi9Oqrr2rcuHGaN29eGK4QpqEnDfTTo48+qm3btunzzz9Xdna25s6dq6efflqffvqpNmzYIKfTqQMHDujBBx/s9vPz5s3T4cOH9Yc//EGS9Hd/93dKSkrS6tWr9dhjj8nr9WrLli3GvzwYocGbWQDAYPSkAcBghDQAGIyQBgCDEdIAYDBCGgAMRkgDgMEIaQAwGCENAAYjpAHAYP8PynuC0g3GFbkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [3 0 4 1]\n",
      "f1 score_KNN: 0.33333333333333337\n",
      "balanced accuracy_KNN: 0.6\n",
      "confusion matrix_KNN\n",
      "[[3 0]\n",
      " [4 1]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=9, |class 1|=20\n",
      "-> create 11 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "f1 score_KNN: 1.0\n",
      "balanced accuracy_KNN: 1.0\n",
      "confusion matrix_KNN\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=9, |class 1|=20\n",
      "-> create 11 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [1 2 0 5]\n",
      "f1 score_KNN: 0.8333333333333333\n",
      "balanced accuracy_KNN: 0.6666666666666666\n",
      "confusion matrix_KNN\n",
      "[[1 2]\n",
      " [0 5]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=12, |class 1|=20\n",
      "-> create 8 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [5]\n",
      "f1 score_KNN: 1.0\n",
      "balanced accuracy_KNN: 1.0\n",
      "confusion matrix_KNN\n",
      "[[5]]\n",
      "-> check results\n",
      "\n",
      "====== Step {shuffleStep + 1}/{self.numOfShuffles} =======\n",
      "-> Spliting data to slices\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=9, |class 1|=20\n",
      "-> create 11 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [3 0 3 2]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 0.5714285714285715\n",
      "balanced accuracy_LR: 0.7\n",
      "confusion matrix_LR\n",
      "[[3 0]\n",
      " [3 2]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [3 0 5 0]\n",
      "f1 score_KNN: 0.0\n",
      "balanced accuracy_KNN: 0.5\n",
      "confusion matrix_KNN\n",
      "[[3 0]\n",
      " [5 0]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=9, |class 1|=20\n",
      "-> create 11 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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V+/fvV21trXJycpScnCybzaaamhrNnTtXjY2NkqSZM2dq7NixysnJ0cmTJ1VaWqrdu3crMzPTf7ympiYdO3ZMZ86cUVtbm44dO6Zjx44FtGbgy1InWvXSonuMGOrAwBWQMenTp0/r+9//fkBDb/HixXI6ncrPz5fb7VZaWpo2btwoSWptbVVdXZ08Ho+kqz3v4uJi5eXladGiRYqPj1dBQYH/9jvp6mKWN954w//zggULJEnHjx8PWM0AEGgRvgDMutXW1mrhwoVDomfKxGHwUWtwUGtwBHvisE896Xvuuee6CgAAXJs+hfTw4cO1ZMmSLiv9vtDQ0KB//Md/DGhhAIA+hrTNZtPNN9+shQsXdru/traWkAaAIOjT3R1TpkzRqVOnetwfFRWl++67L2BFAQCu6lNP+m/+5m963f/Hf/zH2rZtW0AKAoYa01YmwiwBW8xy+vTpQB0KGFJYmYjeXFdIt7e3a8+ePVq2bJnmzp0bqJqAIYWViejNNS1m+cMf/qCdO3eqoqJC7e3tmjVrlt56661A1wYMCbw3Eb3pc0h7PB7t3btXJSUl+vDDD5WWlqaWlhZVVFT0eGseAOD69CmkN23apIqKClmtVi1YsECvvvqq4uPjdffdd8tiMfIZTQAwKPQppP/1X/9VTz75pFavXk0oA0AI9Slx16xZoz179uj+++/Xpk2bdOLEiWDXBQBQH3vSTzzxhJ544glVVlZq586deuSRR3THHXfI5/Pp8uXLwa4RAIasft3dkZKSopSUFLlcLpWVleny5ct6/PHHNX36dM2bN0+PPvposOoEgCHpmgaY4+Li9Bd/8Rf69a9/rZ/+9Ke68cYbVVBQEOjaAHzJoY+dWl/+Ox36OPAvWIa5rvuh/6mpqUpNTe32Ld4AAufLKxO5r3ro6FNP+ujRo8rMzJTb3fXB1hcuXFBWVpaampoCXhyA/8PKxKGpTyG9detWTZ8+XTExXd8ecOONNyopKUlbt24NeHEA/g/vTBya+hTS1dXVmj17do/7MzIy9P777wesKADAVX0K6XPnzmnMmDE97h89erT/zd0AgMDpU0jHxMT0+ijS06dPdzsUAgC4Pn0K6WnTpqm0tLTH/e+8846mTp0asKIAAFf16Ra8P//zP1dmZqZuvPFGPfnkkxo3bpwkqbGxUW+99ZZ+/etf82YWAAiCPoX01KlTVVBQoOeff14lJSWKjo6WJF28eFEjRoxQfn6+pk2bFtRCAWAo6vNilkceeUQzZsxQWVmZmpqa5PP5dNttt2nu3Ln+njUAM/DexMGjTyHd0tKidevW6d1335XX65XD4dDLL7+sCRMmBLs+ANeA1YmDR58mDl977TVVV1frqaee0rPPPiun06m8vLxg1wbgGrE6cfDoU0/63Xff1Ysvvqj09HRJ0syZMzV//nx5PB6NGDEiqAUC6D/emzh49Kkn3djYqLvvvtv/8x133KERI0bwvA4ACLI+hXRHR0eXHrPFYlFHR0dQigIAXNXnuzv+8i//slNQt7W1ae3atRo5cqR/25YtWwJbHQAMcX0K6YULF3bZNn/+/IAXAwDorE8h/dJLLwW7DgBAN67p9VkAgNAgpIEhjPcmmo+QBoawL69MhJkIaWAIY2Wi+a77beEABi5WJpqPnjQAGIyQBgCDEdIAYDBCGgAMRkgDgMEIaQAwGCENoE9YnRgehDSAPmF1YngQ0gD6hNWJ4cGKQwB9wurE8KAnDQAGI6QBwGCENAAYjJAGAIMR0gBgMEIaAAxmdEgXFxdrxowZcjgcWrVqlZzOnlc61dXVKSsrS3a7Xenp6SovL++0v729XS+99JKSk5M1bdo0rV+/XpcvXw72JQBDDisTA8vYkC4rK1NRUZHy8vJUUlIit9ut7Ozsbtt6PB6tWLFCVqtVpaWlWrlypXJzc3X48GF/mzfffFO7d+/WP/zDP+hnP/uZampqtHHjxlBdDjBksDIxsIwN6e3bt2v58uWaM2eO7rzzThUWFqqyslInTpzo0vbgwYNqbGxUYWGhbDabHn30Uc2bN0/bt2+XJHm9Xr399tt65plnlJqaKrvdrg0bNmjXrl26cOFCqC8NGNRYmRhYRoZ0W1ubamtrlZKS4t+WmJiohIQEVVdXd2lfU1Mju92uqKgo/7bU1FR/2/r6ejU3N3c6XlJSknw+n37/+98H8UqAoSd1olUvLbqH1YkBYuSy8ObmZnm9Xlmtnf8jx8XFyeVydWnvcrm6bfvFGPYX//5ym2HDhmn06NG9jnN3x2qN7vRzfHxMvz4fTtQaHNQaHNR6lZEh3V8+n++69veH03lRXu/V48XHx6ipyR2wYwcTtQYHtQbHUKu1t5A3crgjNjZWFoulSy/X5XIpLi6uS3ur1dpt2y96zjfddJMkdWrT0dGh8+fPd+mBA4BJjAzpyMhITZ48WVVVVf5t9fX1amhokMPh6NLebrerpqZGra2t/m2VlZX+tomJiYqNje10vCNHjigiIkJ33XVXEK8EAK6PkSEtSUuXLtXWrVu1f/9+1dbWKicnR8nJybLZbKqpqdHcuXPV2NgoSZo5c6bGjh2rnJwcnTx5UqWlpdq9e7cyMzMlSRaLRUuWLNFrr72myspK1dTU6MUXX9T8+fM1evTocF4mAPTK2DHpxYsXy+l0Kj8/X263W2lpaf77mltbW1VXVyePxyPpas+7uLhYeXl5WrRokeLj41VQUKCkpCT/8X784x/r0qVLevrpp+XxePTAAw/oueeeC8u1AUBfRfgCOas2BDBxGHzUGhwm1nroY6d2VZ/VfMf4TrfsmVhrT4bkxCGAoYHViV+PkAYQNqxO/HrGjkkDGPx4b+LXoycNAAYjpAHAYIQ0ABiMkAYAgxHSAGAwQhoADEZIAzDeUH5vIiENwHhDeWUiIQ3AeEN5ZSIrDgEYbyivTKQnDQAGI6QBwGCENAAYjJAGAIMR0gBgMEIaAAxGSAMYVAbb6kRCGsCgMthWJxLSAAaVwbY6kRWHAAaVwbY6kZ40ABiMkAYAgxHSAGAwQhoADEZIA4DBuLsjiNqm3OX/c+Rvj4axEgADFT1pAEPSQFmZSEgDGJIGyspEhjuCiCEOwFzzHeO1q/qs8SsTCWkAQ9JAWZnIcEeIdRzYJ88T31fHgX3hLgXAAEBIh5j37W3ynTol79vbwl0KgAGAkA4xy+NZirjlFlkezwp3KQAGAMakQ2xYxhwNy5gT7jIADBD0pAHAYIS0AZhMBNATQtoATCYCZgvn6kRC2gBMJgJmC+fqRCYODcBkImC2cK5OJKQB4GuEc3Uiwx0AYDBCGgAMRkgDgMEIaQAwGCENAAYjpAHAYIQ0AARIMFYmEtIAECDBWJlISANAgMx3jFfCmFEBXZnIikMACJBgrEw0tiddXFysGTNmyOFwaNWqVXI6ex/jqaurU1ZWlux2u9LT01VeXt5pf0lJiZYsWSKHw6H09PRglg4AAWNkSJeVlamoqEh5eXkqKSmR2+1WdnZ2j+09Ho9WrFghq9Wq0tJSrVy5Urm5uTp8+LC/zeeff66MjAwtWbIkFJcAAAFh5HDH9u3btXz5cs2Zc/XJcIWFhZo9e7ZOnDghm83Wpf3BgwfV2NioiooKRUVFyWaz6ciRI9q+fbuSkpIkScuWLZMklZeXa+/evaG7GAC4Dsb1pNva2lRbW6uUlBT/tsTERCUkJKi6urrbz9TU1MhutysqKsq/LTU1tcf2ADBQGBfSzc3N8nq9slo7D77HxcXJ5XJ1+xmXy9Vt+68bxwYA04V0uGPdunX65S9/2eP+hQsX6plnnun3cX0+3/WU1S9Wa3Snn+PjY0J27utFrcFBrcFBrVeFNKRzcnK0Zs2aHvePHDlSf/RHfySLxSKn06mJEyf697lcLsXFxXX7OavVqlOnTnXa1l3vOhCczovyeq/+pRAfH6OmJnfAzxEIHQf2yfv2Nlkez9KwjDlG1/pV1Boc1Bocgai1t5AP6XBHTEyM4uPje/wnJiZGkZGRmjx5sqqqqvyfq6+vV0NDgxwOR7fHtdvtqqmpUWtrq39bZWVlj+2HAl5uCwwOxo1JS9LSpUu1detW7d+/X7W1tcrJyVFycrL/zo6amhrNnTtXjY2NkqSZM2dq7NixysnJ0cmTJ1VaWqrdu3crMzPTf8ympiYdO3ZMZ86cUVtbm44dO6Zjx46F5fpCgZfbAoODkbfgLV68WE6nU/n5+XK73UpLS9PGjRv9+1tbW1VXVyePxyNJioyMVHFxsfLy8rRo0SLFx8eroKDAf/uddHUxyxtvvOH/ecGCBZKk48ePh+aiQoyX2wKDQ4QvlLNug8BAGZP+KmoNDmoNjqFWqzFj0givjgP75Hni++o4sC/cpQDoI0J6CGEyERh4COkhhMlEYOAxcuIQwcFkIjDw0JMGAIMR0mBCETAYIQ0mFAGDEdJgQhEwGBOHYEIRMBg9aQAwGCGNbjGZCJiBkEa3mEwEzEBIo1tMJgJmYOIQ3WIyETADPWkAMBghjT5jMhEIPUIafcZkIhB6hDT6jMlEIPSYOESfMZkIhB49aQAwGCGN68JkIhBchDSuC5OJQHAR0rguTCYCwcXEIa4Lk4lAcNGTBgCDEdIAYDBCGgAMRkgDgMEIaQAwGCENAAYjpAHAYIQ0ABiMkAYAg7HisJ8slohefzYZtQYHtQYHtV4V4fP5fEE7OgDgujDcAQAGI6QBwGCENAAYjJAGAIMR0gBgMEIaAAxGSAOAwQhpADAYIQ0ABiOkAcBghHQAvPXWW3r44Yc1ZcoU3X///XrhhRd06dKlcJfVrb1792rZsmX65je/qUmTJoW7nC6Ki4s1Y8YMORwOrVq1Sk6nM9wldWH67/DLBtJ3U5I2b96sBx54QHa7XWlpaVqzZo2amprCXVavfvzjH2vSpEmqqqoKyvEJ6QD48MMP9cMf/lDl5eV65ZVX9N577+mFF14Id1ndam1tVUpKin70ox+Fu5QuysrKVFRUpLy8PJWUlMjtdis7OzvcZXVh8u/wqwbSd1OSbr/9dj3//PPas2ePioqKdPbsWa1bty7cZfWooqJCra2twT2JDwG3Z88e3/Tp08NdRq8qKyt9Npst3GV0smDBAt/rr7/u//n06dM+m83mO378eBir6pmJv8OvMxC+m1924MAB35QpU8JdRrfOnTvn+/a3v+1raGjw2Ww2X2VlZVDOQ086CJqbmxUTExPuMgaUtrY21dbWKiUlxb8tMTFRCQkJqq6uDmNlg8tA+m663W796le/0rRp08JdSrdycnK0YsUK3XzzzUE9DyEdYG63W1u2bNEjjzwS7lIGlObmZnm9Xlmt1k7b4+Li5HK5wlTV4DJQvpu7du3S1KlTdd999+nTTz/VK6+8Eu6SuigpKVF7e7see+yxoJ+Lh/73Yt26dfrlL3/Z4/6FCxfqb//2b/0/t7W16amnnlJiYmLIxyv7WyuGlnB+N/srPT1ddrtdZ8+e1U9+8hPl5uZq8+bN4S7L78yZM3rjjTdUUlISkvMR0r3IycnRmjVretw/cuRI/5/b29v1zDPP6NKlS/rZz36m4cND+6vtT60mio2NlcVikdPp1MSJE/3bXS6X4uLiwljZwBfu72Z/RUdHKzo6Wrfeeqtuv/123X///froo490xx13hLs0SdLRo0f12Wef6Tvf+U6n7T/4wQ+0cOFCFRYWBvR8Zv/XCrOYmJg+jd95vV6tXbtWp0+f1rZt23TDDTeEoLrO+lqrqSIjIzV58mRVVVUpKSlJklRfX6+GhgY5HI4wVzdwmfDdvB6+///iKIvFnJHZlJQU7dq1q9O2hx56SC+88IJmzJgR8PMR0gHw3HPPqaqqSj/96U/l8Xj893XGxcVp2LBhYa6us5aWFp09e1anT5+WJB07dkySNHHiREVGRoazNC1dulSFhYW68847NWHCBBUWFio5OVk2my2sdX2Vyb/DrxpI302Px6M33nhDGRkZslqtOnv2rF5//XXdfffduvXWW8Ndnl90dHS338kJEyZo3LhxAT8f7zgMgJ4WNBw4cEATJkwIcTW9Ky8v1/r167tsN6XW4uJibdu2TW63W2lpadq4caNuuummcJfViem/wy8bSN/N9vZ2/dVf/ZV++9vfqqWlRfHx8frWt76lp59+WmPHjg13eb2aNGmSfv7znys5OTngxyakAcBg5gz0AAC6IKQBwGCENAAYjJAGAIMR0gBgMEIaAAxGSAOAwQhp4CvWrVunSZMmadKkSbrrrrs0a9Ys5ebmqrm52d/mww8/1OrVq5WWlqZ7771Xs2fP1l//9V/r97//fZfj/cu//IvuvPNOvfTSS92e7+LFi9qwYYOSk5M1ZcoU/fCHP/SvZgQIaaAb9913n9577z395je/UU5Ojvbu3au1a9dKuvoGmaVLl2r48OF6+eWXtWfPHr322mtKSEjQiy++2OVYO3fu1IoVK1RRUaG2trYu+5999lkdOnRIr7/+ut5++235fD4tX75cV65cCfp1YgAIyqsEgAFs7dq1vmXLlnXa9uabb/omT57sO3funO+ee+7xPffcc91+tqWlpdPPhw4d8qWmpvo8Ho9v3rx5vl27dnXa/8knn/hsNpvv3Xff7XSMu+++21dWVhaYC8KARk8a6IORI0fK6/WqtLRUbW1tWrVqVbftRo8e3ennHTt26KGHHtLw4cO1YMEC7dy5s9P+Dz74QCNGjFBqamqnY9jtdr3//vuBvxAMOIQ08DU++ugj/eIXv5DD4VBTU5Oio6P1jW9842s/53K5tG/fPi1cuFCS9PDDD+uDDz7QJ5984m/T1NSkMWPGdHki3U033WT8W7IRGoQ00I3Dhw9r6tSpstvt+u53v6vExES98sor/ucb90VZWZkmTpyoyZMnS5LGjh2rtLQ0vfPOO336fERExDXVjsGF50kD3bDb7dq0aZOGDRumsWPH+p8Tfdttt+nixYs6d+5cr71pn8+n0tJSnTp1SnfddZd/u9fr1f/8z//omWeeUWRkpOLj49XS0qKOjo5OvWmn02nUM5QRPvSkgW6MHDlSt9xyiyZMmNDpQf4PPvigIiMj9eabb3b7ufPnz0uSKisrVV9fr3/7t39TRUVFp3/a29u1b98+SdK0adPk8XhUWVnpP8aFCxdUXV2tb37zm0G8QgwU9KSBfhg3bpxyc3OVm5srt9ut733ve0pMTNT58+d14MABVVVV6Re/+IVKSko0ffp0TZ06tcsx0tPTtWPHDv3Zn/2ZbrvtNmVkZCg/P18vvviiYmJi9Oqrr2rcuHGaN29eGK4QpqEnDfTTo48+qm3btunzzz9Xdna25s6dq6efflqffvqpNmzYIKfTqQMHDujBBx/s9vPz5s3T4cOH9Yc//EGS9Hd/93dKSkrS6tWr9dhjj8nr9WrLli3GvzwYocGbWQDAYPSkAcBghDQAGIyQBgCDEdIAYDBCGgAMRkgDgMEIaQAwGCENAAYjpAHAYP8PynuC0g3GFbkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [3 0 4 1]\n",
      "f1 score_KNN: 0.33333333333333337\n",
      "balanced accuracy_KNN: 0.6\n",
      "confusion matrix_KNN\n",
      "[[3 0]\n",
      " [4 1]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=9, |class 1|=20\n",
      "-> create 11 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "f1 score_KNN: 1.0\n",
      "balanced accuracy_KNN: 1.0\n",
      "confusion matrix_KNN\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=9, |class 1|=20\n",
      "-> create 11 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [1 2 0 5]\n",
      "f1 score_KNN: 0.8333333333333333\n",
      "balanced accuracy_KNN: 0.6666666666666666\n",
      "confusion matrix_KNN\n",
      "[[1 2]\n",
      " [0 5]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=12, |class 1|=20\n",
      "-> create 8 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [5]\n",
      "f1 score_KNN: 1.0\n",
      "balanced accuracy_KNN: 1.0\n",
      "confusion matrix_KNN\n",
      "[[5]]\n",
      "-> check results\n",
      "\n",
      "====== Step {shuffleStep + 1}/{self.numOfShuffles} =======\n",
      "-> Spliting data to slices\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=9, |class 1|=20\n",
      "-> create 11 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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oeZksyOm6yDFXxzBDFoSM5B+7pzEjFH8OXePJ8RNlSA+VzWZzuY74+HjEx8cDAGJiYnD//fdDpVLhzJkzUCgUTtej11+F1eq4PTJZEDo6TC63dSzjGLqOY+ia4Ri/wUJflLc7QkND4evr228mbDAYIJFI+pWXSqUOy/5wdj0U06ZNQ0hICNra2u64DiIiV4kypAMCAhAbG4u6ujrhWGtrK9ra2qBUKvuVVygU0Gq16OnpEY7V1tY6LOus9vZ2dHZ2Ijw8/I7rICJylShDGgCys7OxZ88eHDlyBI2NjVCr1UhKSoJcLodWq8X8+fOh0+kAAGlpaQgLC4NarUZTUxM0Gg1qamqwfPlyob6Ojg40NDTg4sWLMJvNaGhoQENDg3B+9+7d+OKLL9Da2opTp05hzZo1iIuLQ1xcnMf7TkR0k2jvSS9ZsgR6vR4lJSUwmUxITU3Fxo0bAQA9PT1oaWmBxWIB0DfzLi8vR3FxMbKysiCTyVBaWiosvwP6/pjlrbfeEr7PzMwEAJw9exYAYDKZoFarcfnyZUgkEqSmpmLdunXw8/PzUI+JiPrzsbnjqRsJ+OBweHEMXccxdA0fHBIRkYAhTUQkYgxpIiIRY0gTEYkYQ5poEMeb9Vhf/RWONzu/xQCROzGkiQZx6455RN7AkCYaBHfMI28T7R+zEIlBSpSUe06TV3EmTUQkYgxpIiIRY0gTEYkYQ5qISMQY0kREIsaQJiISMYY0EZGIMaSJiESMIU1EJGIMaSIiEWNIExGJGEOayE24rSkNB4Y0kZtwW1MaDgxpIjfhtqY0HLhVKZGbcFtTGg6cSRMRiZioQ7q8vBxz5syBUqlEfn4+9PqBH8i0tLQgJycHCoUCKpUK1dXVducrKyuxbNkyKJVKqFQqh3V8+umnWLBgAWbNmoWsrCxotVq39oeIaKhEG9JVVVXYuXMniouLUVlZCZPJhIKCAodlLRYL8vLyIJVKodFosGrVKhQVFeHEiRNCmevXr2Pu3LlYtmyZwzqam5uxevVqZGZm4uDBg0hISMDTTz+Nrq6uYekfEZEzRBvSFRUVyM3Nxbx58zBjxgyUlZWhtrYW586d61f22LFj0Ol0KCsrg1wux9KlS7Fw4UJUVFQIZVasWIGVK1dCLpc7vN6BAwcQHx+PvLw8REdHQ61WIzAwEB9++OGw9ZGI6HZEGdJmsxmNjY1ITk4WjkVGRiI8PBz19fX9ymu1WigUCgQGBgrHUlJSHJYdiFartbuej48PkpOTh1QHEZG7iXJ1h9FohNVqhVRq/6RcIpHAYDD0K28wGByWHewetqM6JBKJ3bHQ0FCcPXt2CC0HpNJJg56XyYKGVB/1xzF0HcfQNZ4cP1GG9FDZbDZR1AEAev1VWK2O65LJgtDRYXLLdcYqjqHrOIauGY7xGyz0RXm7IzQ0FL6+vv1mwo5muwAglUodlv3h7HowUqm03yzdaDQ6vB4RkaeIMqQDAgIQGxuLuro64Vhrayva2tqgVCr7lVcoFNBqtejp6RGO1dbWOiw7EIVCYXc9AKirqxtSHURE7ibKkAaA7Oxs7NmzB0eOHEFjYyPUajWSkpIgl8uh1Woxf/586HQ6AEBaWhrCwsKgVqvR1NQEjUaDmpoaLF++XKivo6MDDQ0NuHjxIsxmMxoaGtDQ0CCcf+KJJ3D69Gm88847aG5uxqZNm9Dd3Y1FixZ5vO9ERDeJ9p70kiVLoNfrUVJSApPJhNTUVGzcuBEA0NPTg5aWFlgsFgB9M+/y8nIUFxcjKysLMpkMpaWlSExMFOqrrKzEW2+9JXyfmZkJAMKDwaioKLz55pvYunUrtm/fDrlcjl27diE4ONhDPSYi6s/H5q4nZgSADw6H22gYw+PNehyu/w4Zynu8stfHaBhDb+KDQ6JRjlua0lAwpIk8jFua0lCI9p400WjFLU1pKDiTJiISMYY0EZGIMaSJiESMIU1EJGJuCenvv/8eJ0+edEdVRER0C7eE9IULF/DLX/7SHVUREdEteLuDiEjEnFon/cADDwx3O4iIyAGnQtrf3x/Lli0b8P2AbW1t+I//+A+3NoyIiJwMablcjmnTpmHx4sUOzzc2NjKkiYiGgVP3pOPj43H+/PkBzwcGBuKhhx5yW6OIqM/xZj3WV3+F483Ov6+TRhduVepm3Kp0eI21MVxf/RXaOnsQHjIRm7Pc82xorI2hu43YrUovXLjgrqqI6H9wxzxyaRe83t5efPzxx3j//fdx8uRJfPPNN+5qFxGBO+bRHYb0t99+iwMHDuDQoUPo7e3Fww8/jLffftvdbSMiGvOcDmmLxYKPP/4YlZWVOH36NFJTU9HZ2YlDhw4NuDSPiIhc41RIb9myBYcOHYJUKkVmZia2bdsGmUyGuLg4+PryjxaJiIaLUyG9b98+PPvss1i9ejVDmYjIg5xK3HXr1uGjjz7CT3/6U2zZsgXnzp0b7nYRERGcnEk/9dRTeOqpp1BbW4sDBw7g8ccfR3R0NGw2G77//vvhbiMR0Zg1pNUdycnJSE5OhsFgQFVVFb7//ns8+eSTmD17NhYuXIilS5cOVzuJiMakO7rBLJFI8PTTT+PPf/4zdu3ahbvvvhulpaXubhvKy8sxZ84cKJVK5OfnQ68f+E9jW1pakJOTA4VCAZVKherqarvzvb292Lx5M5KSkpCQkID169fb/RZQXV2NmJgYu6/8/Hy394mIaChc+mMWAEhJSUFKSgoMBoM72iOoqqrCzp07sXXrVkRERKCsrAwFBQXYt29fv7IWiwV5eXmYOXMmNBoN6uvrUVRUhIiICCQmJgIAduzYgZqaGrz++uu46667sH79emzcuBGbN28W6pk6dSo0Go3w/fjx493aJyKioXJqJv3NN99g+fLlMJn6/736lStXkJOTg46ODrc2rKKiArm5uZg3bx5mzJiBsrIy1NbWOnxoeezYMeh0OpSVlUEul2Pp0qVYuHAhKioqAABWqxX79+/H2rVrkZKSAoVCgQ0bNuDw4cO4cuWKUI+fnx9kMpnwdffdd7u1T0REQ+VUSO/ZswezZ89GUFD/TUDuvvtuJCYmYs+ePW5rlNlsRmNjI5KTk4VjkZGRCA8PR319fb/yWq0WCoUCgYGBwrGUlBShbGtrK4xGo119iYmJsNls+Prrr4Vj7e3tSEtLQ3p6OgoLC2E0Gt3WJyKiO+HU7Y76+nr86le/GvD83Llz8Zvf/MZdbYLRaITVaoVUar9ngUQicXhbxWAwOCx78x72zX/eWsbPzw/BwcHCuenTp2PLli2Ijo7GpUuXsG3bNuTn52P//v3w8fFxuu1S6aRBzw+22xU5h2Po2KffXML7tRfwf5J/hJ/NnDpoWY6hazw5fk6F9KVLlxASEjLg+eDgYOh0One1achut9uqM7uxxsfHIz4+HgAQExOD+++/HyqVCmfOnIFCoXC6LdyqdHhxDAdW8VkL2jp7UPFZC+Jkdw1YjmPoGlFuVRoUFDToVqQXLlxweCvkToWGhsLX17ffag6DwQCJRNKvvFQqdVj25sx58uTJAGBX5saNG+jq6uo3A79p2rRpCAkJQVtbm0t9IfIUbms6OjkV0gkJCXarHn7ogw8+wIMPPui2RgUEBCA2NhZ1dXXCsdbWVrS1tUGpVPYrr1AooNVq0dPTIxyrra0VykZGRiI0NNSuvpMnT8LHxwczZ8502Ib29nZ0dnYiPDzcXd0iGlYpUVJsznqAW5uOMk6F9K9//Wv813/9F15++WW72xo6nQ4lJSX485//jF//+tdubVh2djb27NmDI0eOoLGxEWq1GklJSZDL5dBqtZg/f77QlrS0NISFhUGtVqOpqQkajQY1NTVYvnx5Xyd9fbFs2TK89tprqK2thVarxaZNm5CRkYHg4GAAwO7du/HFF1+gtbUVp06dwpo1axAXF4e4uDi39ouIaCicuif94IMPorS0FC+//DIqKysxaVLfw7GrV69i3LhxKCkpQUJCglsbtmTJEuj1epSUlMBkMiE1NRUbN24EAPT09KClpQUWiwVA38y7vLwcxcXFyMrKgkwmQ2lpqbBGGgCee+45dHd3Y82aNbBYLHj00UdRWFgonDeZTFCr1bh8+TIkEglSU1Oxbt06+Pn5ubVfRERDMaR3HOp0OlRVVaGjowM2mw333Xcf5s+fjylTpgxnG0cUPjgcXhxD13EMXePpB4dOzaQ7Ozvx0ksv4bPPPoPVaoVSqcSrr76KiIgItzWSiIj6c+qe9GuvvYb6+no8//zzeOGFF6DX61FcXDzcbSMiGvOcmkl/9tln2LRpE1QqFYC+B3UZGRmwWCwYN27csDaQiGgsc2omrdPp7FY5REdHY9y4cW7fr4OIiOw5FdI3btzoN2P29fXFjRs3hqVRRETUx+mtSn/zm9/YBbXZbMaLL76ICRMmCMfeffdd97aOiGiMcyqkFy9e3O9YRkaG2xtDRET2nArpWzfGJyIiz7mj12cR0ch2vFmP9dVf4XjzwK+kI3FgSBONQYfrv0NbZw8O13/n7abQbTCkicYgbms6crj8IloiGnlSoqTc0nSE4EyaiEjEGNJERCLGkCYiEjGGNBGRiDGkiYhEjCFNRCRiDGkiIhFjSBMRiRhDmohIxBjSREQixpAmokFxxzzvYkgT0aC4Y553iTqky8vLMWfOHCiVSuTn50OvH/i/5C0tLcjJyYFCoYBKpUJ1dbXd+d7eXmzevBlJSUlISEjA+vXr8f3339uV+fTTT7FgwQLMmjULWVlZ0Gq1w9IvopGEO+Z5l2hDuqqqCjt37kRxcTEqKythMplQUFDgsKzFYkFeXh6kUik0Gg1WrVqFoqIinDhxQiizY8cO1NTU4PXXX8fevXuh1WqxceNG4XxzczNWr16NzMxMHDx4EAkJCXj66afR1dU17H0lErOUKCk2Zz3AXfO8RLQhXVFRgdzcXMybNw8zZsxAWVkZamtrce7cuX5ljx07Bp1Oh7KyMsjlcixduhQLFy5ERUUFAMBqtWL//v1Yu3YtUlJSoFAosGHDBhw+fBhXrlwBABw4cADx8fHIy8tDdHQ01Go1AgMD8eGHH3q030REtxJlSJvNZjQ2NiI5OVk4FhkZifDwcNTX1/crr9VqoVAoEBgYKBxLSUkRyra2tsJoNNrVl5iYCJvNhq+//lqo49bzPj4+SE5Odng9IiJPEeWm/0ajEVarFVKp/a9XEokEBoOhX3mDweCw7M172Df/eWsZPz8/BAcHC+cMBgMkEoldHaGhoTh79uyQ2i6VThr0vEwWNKT6qD+Ooes4hq7x5PiJMqSHymazuXTe2TLO0Ouvwmp1XJdMFoSODpNbrjNWcQxdxzF0zXCM32ChL8rbHaGhofD19e23msPRbBfomyE7Kntz5jx58mQAsCtz48YNdHV1CWWkUmm/WbrRaHR4PSIiTxFlSAcEBCA2NhZ1dXXCsdbWVrS1tUGpVPYrr1AooNVq0dPTIxyrra0VykZGRiI0NNSuvpMnT8LHxwczZ84U6rj1PADU1dU5vB4RkaeIMqQBIDs7G3v27MGRI0fQ2NgItVqNpKQkyOVyaLVazJ8/HzqdDgCQlpaGsLAwqNVqNDU1QaPRoKamBsuXLwcA+Pr6YtmyZXjttddQW1sLrVaLTZs2ISMjA8HBwQCAJ554AqdPn8Y777yD5uZmbNq0Cd3d3Vi0aJHXxoCISLT3pJcsWQK9Xo+SkhKYTCakpqYK65p7enrQ0tICi8UCoG/mXV5ejuLiYmRlZUEmk6G0tBSJiYlCfc899xy6u7uxZs0aWCwWPProoygsLBTOR0VF4c0338TWrVuxfft2yOVy7Nq1SwhxIiJv8LG564kZAeCDw+HGMXQdx9A1fHBIREQChjQRkYgxpInIbbitqfsxpInIbbitqfsxpInIbbitqfuJdgkeEY08KVFSbmnqZpxJExGJGEOaiEjEGNJERCLGkCYiEjGGNBGRiDGkiYhEjCFNRCRiDGkiIhFjSBMRiRhDmohIxBjSROQV3DHPOQxpIvIK7pjnHIY0EXkFd8xzDnfBIyKv4I55zuFMmohIxDiT9iJz/Ezhfwf89RsvtoSIxIozaSIiEWNIExGJmGhDury8HHPmzIFSqUR+fj70+sHXUra0tCAnJwcKhQIqlQrV1dV253t7e7F582YkJSUhISEB69evx/fffy+cr66uRkxMjN1Xfn7+sPTtpoC/fiN8ERE5IsqQrqqqws6dO1FcXIzKykqYTCYUFBQMWN5isSAvLw9SqRQajQarVq1CUVERTpw4IZTZsWMHampq8Prrr2Pv3r3QarXYuHGjXT1Tp07F559/Lnz99re/HbY+EhE5Q5QPDisqKpCbm4t58+YBAMrKypCeno5z585BLpf3K3/s2DHodDocOnQIgYGBkMvlOHnyJCoqKpCYmAir1Yr9+/fjX//1X5GSkgIA2LBhA1auXIn169fj7rvvBgD4+flBJpN5rqNERLchupm02WxGY2MjkpOThWORkZEIDw9HfX29w89otVooFAoEBgYKx1JSUoTyra2tMBqNdnUmJibCZrPh66+/Fo61t7cjLS0N6enpKCwshNFodHf3iIiGRHQzaaPRCKvVCqnUfpG7RCKBwWBw+BmDweCw/M372Df/eWsZPz8/BAcHC+emT5+OLVu2IDo6GpcuXcK2bduQn5+P/fv3w8fHx+n2S6WTBj0vkwUNer7nP/8TV3f/HpNWPoWJCxY4fd2x5HZjSLfHMXSNJ8fPoyH90ksv4eDBgwOeX7x4MdauXTvkem02m0vnASA+Ph7x8fEAgJiYGNx///1QqVQ4c+YMFAqF023R66/CanV8PZksCB0dpkE/b3n7HdjOn4fx7Xdw9aE5Tl93rHBmDGlwHEPXDMf4DRb6Hg1ptVqNdevWDXh+woQJGD9+PHx9faHX6xEVFSWcMxgMkEgkDj8nlUpx/vx5u2O3zq4nT54MoG9GHR4eDgC4ceMGurq6+s3Ab5o2bRpCQkLQ1tY2pJB2le+TObDufw++T+Z47JpEJF4eDemgoCAEBd3+14TY2FjU1dUhMTERQN895ba2NiiVSoflFQoF9u7di56eHkycOBEAUFtbK5SPjIxEaGgo6urqkJWVBQA4efIkfHx8MHPmTId1tre3o7OzUwh1T/GbOw9+c+d59JpEYna8WY/D9d8hQ3nPmNzrQ3QPDgEgOzsbe/bswZEjR9DY2Ai1Wo2kpCRhZYdWq8X8+fOh0+kAAGlpaQgLC4NarUZTUxM0Gg1qamqwfPlyAICvry+WLVuG1157DbW1tdBqtdi0aRMyMjIQHBwMANi9eze++OILtLa24tSpU1izZg3i4uIQFxfnnUEgIgDc0lR0Dw4BYMmSJdDr9SgpKYHJZEJqaqrdmuaenh60tLTAYrEAAAICAlBeXo7i4mJkZWVBJpOhtLRUmIkDwHPPPYfu7m6sWbMGFosFjz76KAoLC4XzJpMJarUaly9fhkQiQWpqKtatWwc/Pz/PdZyI+slQ3iPMpMciH5szT9XIaa4+OKTBcQxdxzF0jacfHIrydgfZu3H0E1ie+iVuHP3E200hIg9jSI8A1v3vwXb+PKz73/N2U4jIwxjSI4DvkznwufdeLssjGoNE+eCQ7HFZHtHYxZk0EZGIMaSJiESMIU1EJGIMaSIiEWNIj2BcP000+jGkRzCunyYa/RjSIxjXTxONflwnPYJx/TSRvdG4rSln0kQ0aozGbU0Z0kQ0amQo70F4yMRRta0pb3cQ0aiREiUdNbc5buJMepThsjyi0YUhPcpwWR7R6MKQHmW4LI9odOE96VGGy/KIRhfOpImIRIwhTUQkYgxpIiIRY0iPEVyaRzQyMaTHCC7NIxqZRBvS5eXlmDNnDpRKJfLz86HX6wct39LSgpycHCgUCqhUKlRXV9udr6ysxLJly6BUKqFSqRzW8emnn2LBggWYNWsWsrKyoNVq3dYfb+PSPKKRSZQhXVVVhZ07d6K4uBiVlZUwmUwoKCgYsLzFYkFeXh6kUik0Gg1WrVqFoqIinDhxQihz/fp1zJ07F8uWLXNYR3NzM1avXo3MzEwcPHgQCQkJePrpp9HV1eX2/nmD39x5GPf7P3B5HtH/ON6sx/rqr3C8efAJoLeJMqQrKiqQm5uLefPmYcaMGSgrK0NtbS3OnTvnsPyxY8eg0+lQVlYGuVyOpUuXYuHChaioqBDKrFixAitXroRcLndYx4EDBxAfH4+8vDxER0dDrVYjMDAQH3744bD0kYi8a6TsmCe6kDabzWhsbERycrJwLDIyEuHh4aivr3f4Ga1WC4VCgcDAQOFYSkrKgOUHquPWa/r4+CA5OXlIdRDRyDFSdswT3V8cGo1GWK1WSKX2O1lJJBIYDAaHnzEYDA7L3+4+9g/rkEgkdsdCQ0Nx9uxZp+sAAKl00qDnZbKgIdVH/XEMXccxBDJkQchI/vEdfdaT4+fRkH7ppZdw8ODBAc8vXrwYa9euHXK9NpvNlWa5rQ4A0Ouvwmp1XJdMFoSODpNbrjNWcQxdxzF0zXCM32Ch79GQVqvVWLdu3YDnJ0yYgPHjx8PX1xd6vR5RUVHCOUcz3ZukUinOnz9vd8zR7HowUqm030zdaDQOeM3R4MbRT2Dd/x58n8zhA0UikfLoPemgoCDIZLIBv4KCghAQEIDY2FjU1dUJn2ttbUVbWxuUSqXDehUKBbRaLXp6eoRjtbW1A5YfqI5brwkAdXV1Q6pjpOHaaSLxE92DQwDIzs7Gnj17cOTIETQ2NkKtViMpKUlYmaHVajF//nzodDoAQFpaGsLCwqBWq9HU1ASNRoOamhosX75cqLOjowMNDQ24ePEizGYzGhoa0NDQIJx/4okncPr0abzzzjtobm7Gpk2b0N3djUWLFnm28x7EtdNE4ie6B4cAsGTJEuj1epSUlMBkMiE1NRUbN24Uzvf09KClpQUWiwUAEBAQgPLychQXFyMrKwsymQylpaVITEwUPlNZWYm33npL+D4zMxMAhAeDUVFRePPNN7F161Zs374dcrkcu3btQnBwsAd67B3c1pRI/Hxs7npiRgD44HC4cQxdxzF0jacfHIrydgcREfVhSBMRiRhDmvrhtqZE4sGQpn64NI9IPBjS1A+X5hGJhyiX4JF3cWkekb3jzXocrv8OGcp7kOHhfU84kyYiug1vbmvKkCYiug1vbmvK2x1ERLeREiVFSpTzG7a5E2fS5BQuyyPyDoY0OYXL8oi8gyFNTuGyPCLv4D1pcgqX5RF5B2fSREQixpAmIhIxhjQRkYgxpImIRIwhTUQkYgxpIiIRY0gTEbnR8WY91ld/hePNerfUx5AmInIjd++Yx5AmInIjd++Yx784JCJyI3fvmMeZNBGRiIk2pMvLyzFnzhwolUrk5+dDrx/8JnxLSwtycnKgUCigUqlQXV1td76yshLLli2DUqmESqXq9/nq6mrExMTYfeXn57u1T0REQyXK2x1VVVXYuXMntm7dioiICJSVlaGgoAD79u1zWN5isSAvLw8zZ86ERqNBfX09ioqKEBERgcTERADA9evXMXfuXCiVSnz88ccO65k6dSo0Go3w/fjx493fOSKiIRBlSFdUVCA3Nxfz5vXtulZWVob09HScO3cOcrm8X/ljx45Bp9Ph0KFDCAwMhFwux8mTJ1FRUSGE9IoVKwD0zZgHCmk/Pz/IZLJh6hUR0dCJ7naH2WxGY2MjkpOThWORkZEIDw9HfX29w89otVooFAoEBgYKx1JSUgYsP5D29nakpaUhPT0dhYWFMBqNd9YJIiI3Ed1M2mg0wmq1Qiq1fzoqkUhgMBgcfsZgMDgsf7v72LeaPn06tmzZgujoaFy6dAnbtm1Dfn4+9u/fDx8fH6frkUonDXpe5uHXwY9GHEPXcQxd48nx82hIv/TSSzh48OCA5xcvXoy1a9cOuV6bzeZKswAA8fHxiI+PBwDExMTg/vvvh0qlwpkzZ6BQKJyuR6+/CqvVcXtksiB0dJhcbutYxjF0HcfQNcMxfoOFvkdDWq1WY926dQOenzBhAsaPHw9fX1/o9XpERUUJ5wwGAyQSicPPSaVSnD9/3u6Yo9n1UEybNg0hISFoa2sbUkgTEbmTR0M6KCgIQUG3/zUhNjYWdXV1wkO/1tZWtLW1QalUOiyvUCiwd+9e9PT0YOLEiQCA2traAcs7o729HZ2dnQgPD7/jOoiIXCW6B4cAkJ2djT179uDIkSNobGyEWq1GUlKSsLJDq9Vi/vz50Ol0AIC0tDSEhYVBrVajqakJGo0GNTU1WL58uVBnR0cHGhoacPHiRZjNZjQ0NKChoUE4v3v3bnzxxRdobW3FqVOnsGbNGsTFxSEuLs6znSciuoXoHhwCwJIlS6DX61FSUgKTyYTU1FRs3LhRON/T04OWlhZYLBYAQEBAAMrLy1FcXIysrCzIZDKUlpYKM3Gg749Z3nrrLeH7zMxMAMDZs2cBACaTCWq1GpcvX4ZEIkFqairWrVsHPz+/IbXd13fwh4y3O0+3xzF0HcfQNZ4cPx+bO566ERHRsBDl7Q4iIurDkCYiEjGGNBGRiDGkiYhEjCFNRCRiDGkiIhFjSBMRiRhDmohIxBjSREQixpAmIhIxhrQXvP3223jssccQHx+Pn/70p3jllVfQ3d3t7WaJ3lBfTkz/iz9z7vXcc88hJiYGdXV1w34thrQXnD59GitXrkR1dTV+97vf4fPPP8crr7zi7WaJ2s2XExcXF6OyshImkwkFBQXebtaIwZ859zl06BB6eno8d0Ebed1HH31kmz17trebIWqZmZm2N954Q/j+woULNrlcbjt79qwXWzVy8Wfuzly6dMn2s5/9zNbW1maTy+W22traYb8mZ9IiYDQanXoZwlh1Jy8npsHxZ+7OqNVq5OXlYdq0aR67JkPay0wmE9599108/vjj3m6KaN3Jy4lpYPyZuzOVlZXo7e3FL37xC49eV5Sb/o9Uzrxo97e//a3wvdlsxvPPP4/IyEg888wznmgijXH8mbszFy9exFtvvYXKykqPX5sh7UbOvGj3pt7eXqxduxbd3d3Yu3cv/P35r2IgoaGhQ345MfXHn7k798033+Dy5ct45JFH7I7/6le/wuLFi1FWVjZs1+a/JTdy9kW7VqsVL774Ii5cuID33nsPd911lwdaN3IFBAQM+eXEZI8/c65JTk7G4cOH7Y4tWrQIr7zyCubMmTOs12ZIe0FhYSHq6uqwa9cuWCwWdHR0AOi7xzrUdyqOFdnZ2SgrK8OMGTMQERGBsrIyu5cT0+D4M+eaSZMmOfxZi4iIwJQpU4b12nzHoRfExMQ4PH706FFERER4uDUjR3l5Od577z27lxNPnjzZ280aEfgz534xMTH4wx/+gKSkpGG9DkOaiEjEuASPiEjEGNJERCLGkCYiEjGGNBGRiDGkiYhEjCFNRCRiDGkiIhFjSBPdxksvvYSYmBjExMRg5syZePjhh1FUVASj0SiUOX36NFavXo3U1FTMmjUL6enp+Jd/+Rd8/fXX/er7/e9/jxkzZmDz5s0Or3f16lVs2LABSUlJiI+Px8qVK3HhwoVh6x+JG0OayAkPPfQQPv/8c/zlL3+BWq3Gxx9/jBdffBFA31tjsrOz4e/vj1dffRUfffQRXnvtNYSHh2PTpk396jpw4ADy8vJw6NAhmM3mfudfeOEFHD9+HG+88Qb2798Pm82G3NxcXLt2bdj7SSI07K8VIBrhXnzxRduKFSvsju3YscMWGxtru3Tpku2BBx6wFRYWOvxsZ2en3ffHjx+3paSk2CwWi23hwoW2w4cP253/+9//bpPL5bbPPvvMro64uDhbVVWVezpEIwpn0kR3YMKECbBardBoNDCbzcjPz3dYLjg42O77999/H4sWLYK/vz8yMzNx4MABu/Nffvklxo0bh5SUFLs6FAoFTp065f6OkOgxpImG6G9/+xv++Mc/QqlUoqOjA5MmTcLUqVNv+zmDwYBPPvkEixcvBgA89thj+PLLL/H3v/9dKNPR0YGQkJB+O9NNnjxZ2LmOxhaGNJETTpw4gQcffBAKhQI///nPERkZid/97newDWF/sqqqKkRFRSE2NhYAEBYWhtTUVHzwwQdOfd7Hx+eO2k4jG/eTJnKCQqHAli1b4Ofnh7CwMAQEBAAA7rvvPly9ehWXLl0adDZts9mg0Whw/vx5zJw5UzhutVpx5swZrF27FgEBAZDJZOjs7MSNGzfsZtN6vR4//vGPh61/JF6cSRM5YcKECbj33nsREREhBDQALFiwAAEBAdixY4fDz3V1dQEAamtr0draij/96U84dOiQ3Vdvby8++eQTAEBCQgIsFgtqa2uFOq5cuYL6+nr80z/90zD2kMSKM2kiF0yZMgVFRUUoKiqCyWTCE088gcjISHR1deHo0aOoq6vDH//4R1RWVmL27Nl48MEH+9WhUqnw/vvv45//+Z9x3333Ye7cuSgpKcGmTZsQFBSEbdu2YcqUKVi4cKEXekjexpk0kYuWLl2K9957D9evX0dBQQHmz5+PNWvW4B//+Ac2bNgAvV6Po0ePYsGCBQ4/v3DhQpw4cQLffvstAGDr1q1ITEzE6tWr8Ytf/AJWqxXvvvuu3YuMaezgm1mIiESMM2kiIhFjSBMRiRhDmohIxBjSREQixpAmIhIxhjQRkYgxpImIRIwhTUQkYgxpIiIR+/9tP4xwXElXuQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [3 0 3 2]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 0.5714285714285715\n",
      "balanced accuracy_LR: 0.7\n",
      "confusion matrix_LR\n",
      "[[3 0]\n",
      " [3 2]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [3 0 5 0]\n",
      "f1 score_KNN: 0.0\n",
      "balanced accuracy_KNN: 0.5\n",
      "confusion matrix_KNN\n",
      "[[3 0]\n",
      " [5 0]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=9, |class 1|=20\n",
      "-> create 11 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [3 0 4 1]\n",
      "f1 score_KNN: 0.33333333333333337\n",
      "balanced accuracy_KNN: 0.6\n",
      "confusion matrix_KNN\n",
      "[[3 0]\n",
      " [4 1]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=9, |class 1|=20\n",
      "-> create 11 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "f1 score_KNN: 1.0\n",
      "balanced accuracy_KNN: 1.0\n",
      "confusion matrix_KNN\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=9, |class 1|=20\n",
      "-> create 11 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [3 0 0 5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[3 0]\n",
      " [0 5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [1 2 0 5]\n",
      "f1 score_KNN: 0.8333333333333333\n",
      "balanced accuracy_KNN: 0.6666666666666666\n",
      "confusion matrix_KNN\n",
      "[[1 2]\n",
      " [0 5]]\n",
      "-> check results\n",
      "\n",
      "------ Step {shuffleStep + 1}/{self.numOfShuffles}: Slice {sliceNr + 1}/{self.numOfSlices} -------\n",
      "-> Train generator for synthetic samples\n",
      "Train GAN with |class 0|=12, |class 1|=20\n",
      "-> create 8 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "tn, fp, fn, tp: [5]\n",
      "average_pr_score: 1.0\n",
      "f1 score_LR: 1.0\n",
      "balanced accuracy_LR: 1.0\n",
      "confusion matrix_LR\n",
      "[[5]]\n",
      "-> test with 'SVM'\n",
      "tn, fp, fn, tp: [5]\n",
      "f1 score_SVM: 1.0\n",
      "balanced accuracy_SVM: 1.0\n",
      "confusion matrix_SVM\n",
      "[[5]]\n",
      "-> test with 'KNN'\n",
      "tn, fp, fn, tp: [5]\n",
      "f1 score_KNN: 1.0\n",
      "balanced accuracy_KNN: 1.0\n",
      "confusion matrix_KNN\n",
      "[[5]]\n",
      "-> check results\n",
      "### Exercise is done.\n"
     ]
    }
   ],
   "source": [
    "exercise.run(gan, loadDataset())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "extraordinary-hughes",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expected class 0 to be the minority class but class 0 is bigger than class 1.\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    exercise.run(gan, loadFailingDataset())\n",
    "except AttributeError as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "focused-respect",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": 5
}