{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f3c92fc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from library.exercise import Exercise\n",
    "from library.dataset import DataSet, TrainTestData\n",
    "from library.GanExamples import StupidToyListGan\n",
    "from library.SimpleGan import SimpleGan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d10f9c73",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import numpy as np\n",
    "import random\n",
    "from imblearn.datasets import fetch_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bfdaaf87",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1.,\n",
      "       0., 0., 0., 1., 0., 0., 0., 0.])), (array([[0.58, 0.61, 0.47, ..., 0.  , 0.48, 0.22],\n",
      "       [0.43, 0.67, 0.48, ..., 0.  , 0.53, 0.22],\n",
      "       [0.64, 0.62, 0.49, ..., 0.  , 0.53, 0.22],\n",
      "       ...,\n",
      "       [0.47, 0.43, 0.61, ..., 0.  , 0.48, 0.47],\n",
      "       [0.67, 0.57, 0.36, ..., 0.  , 0.56, 0.22],\n",
      "       [0.65, 0.54, 0.54, ..., 0.  , 0.53, 0.22]]), array([0., 0., 0., ..., 0., 0., 0.]), array([[0.42, 0.37, 0.59, ..., 0.  , 0.52, 0.29],\n",
      "       [0.66, 0.55, 0.45, ..., 0.  , 0.46, 0.22],\n",
      "       [0.58, 0.47, 0.54, ..., 0.  , 0.51, 0.26],\n",
      "       ...,\n",
      "       [0.71, 0.5 , 0.5 , ..., 0.  , 0.46, 0.22],\n",
      "       [0.38, 0.32, 0.64, ..., 0.  , 0.44, 0.11],\n",
      "       [0.43, 0.4 , 0.6 , ..., 0.  , 0.53, 0.39]]), array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 0.,\n",
      "       0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0.]))], 'db_size': 1484, 'db_n_attr': 10, 'imbalanced_ratio': 32.72727272727273}\n"
     ]
    }
   ],
   "source": [
    "datasetName = \"folding_yeast5\"\n",
    "pickle_in = open(f\"{datasetName}.pickle\",\"rb\")\n",
    "example_dict1 = pickle.load(pickle_in)\n",
    "\n",
    "print(example_dict1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0f6b23e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['folding', 'db_size', 'db_n_attr', 'imbalanced_ratio'])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_dict1.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ce509dbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "myData = example_dict1[\"folding\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0fabea38",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0.58, 0.61, 0.47, ..., 0.  , 0.48, 0.22],\n",
       "        [0.43, 0.67, 0.48, ..., 0.  , 0.53, 0.22],\n",
       "        [0.64, 0.62, 0.49, ..., 0.  , 0.53, 0.22],\n",
       "        ...,\n",
       "        [0.67, 0.57, 0.36, ..., 0.  , 0.56, 0.22],\n",
       "        [0.43, 0.4 , 0.6 , ..., 0.  , 0.53, 0.39],\n",
       "        [0.65, 0.54, 0.54, ..., 0.  , 0.53, 0.22]]),\n",
       " array([0., 0., 0., ..., 0., 0., 0.]),\n",
       " array([[0.48, 0.45, 0.59, ..., 0.  , 0.58, 0.34],\n",
       "        [0.66, 0.55, 0.45, ..., 0.  , 0.46, 0.22],\n",
       "        [0.58, 0.47, 0.54, ..., 0.  , 0.51, 0.26],\n",
       "        ...,\n",
       "        [0.59, 0.67, 0.54, ..., 0.  , 0.48, 0.6 ],\n",
       "        [0.48, 0.61, 0.57, ..., 0.  , 0.45, 0.22],\n",
       "        [0.61, 0.48, 0.54, ..., 0.  , 0.5 , 0.22]]),\n",
       " array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.,\n",
       "        0., 1., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0.]))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myData[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4ff91174",
   "metadata": {},
   "outputs": [],
   "source": [
    "k = myData[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9e31bb48",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_con = np.concatenate((k[0],k[2]),axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "727d84dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "target = np.concatenate((k[1],k[3]),axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "44270a01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., ..., 0., 0., 0.])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c95f51f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.58, 0.61, 0.47, ..., 0.  , 0.48, 0.22],\n",
       "       [0.43, 0.67, 0.48, ..., 0.  , 0.53, 0.22],\n",
       "       [0.64, 0.62, 0.49, ..., 0.  , 0.53, 0.22],\n",
       "       ...,\n",
       "       [0.59, 0.67, 0.54, ..., 0.  , 0.48, 0.6 ],\n",
       "       [0.48, 0.61, 0.57, ..., 0.  , 0.45, 0.22],\n",
       "       [0.61, 0.48, 0.54, ..., 0.  , 0.5 , 0.22]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_con"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "08c19220",
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadDataset():\n",
    "    labels = target\n",
    "    features = data_con\n",
    "    label_1 = list(np.where(labels == 1)[0])\n",
    "    label_0 = list(np.where(labels == 0)[0])\n",
    "    features_1 = features[label_1]\n",
    "    features_0 = features[label_0]\n",
    "    \n",
    "    data0 = np.array(range(100)).reshape((25,4))\n",
    "    return DataSet(data0=features_0, data1=features_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5890f2ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = loadDataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f27cef3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getRandGen(initValue, incValue=257, multValue=101, modulus=65537):\n",
    "    value = initValue\n",
    "    while True:\n",
    "        value = ((multValue * value) + incValue) % modulus\n",
    "        yield value\n",
    "            \n",
    "randGen = getRandGen(2021)\n",
    "random.seed(2021)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0939e1eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def shuffler(data):\n",
    "    data = list(data)\n",
    "    size = len(data)\n",
    "    shuffled = []\n",
    "    while size > 0:\n",
    "        p = next(randGen) % size\n",
    "        size -= 1\n",
    "        shuffled.append(data[p])\n",
    "        data = data[0:p] + data[(p + 1):]\n",
    "    return np.array(shuffled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "32cf8258",
   "metadata": {},
   "outputs": [],
   "source": [
    "ganName = \"SimpleGAN\"\n",
    "gan = SimpleGan(numOfFeatures=k[2].shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "40220314",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> Shuffling data\n",
      "### Start exercise for synthetic point generator\n",
      "\n",
      "====== Step 1/5 =======\n",
      "-> Shuffling data\n",
      "-> Spliting data to slices\n",
      "\n",
      "------ Step 1/5: Slice 1/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1117 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",
      "LR tn, fp: 288, 0\n",
      "LR fn, tp: 5, 4\n",
      "LR f1 score: 0.615\n",
      "LR cohens kappa score: 0.608\n",
      "LR average precision score: 0.888\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 287, 1\n",
      "GB fn, tp: 5, 4\n",
      "GB f1 score: 0.571\n",
      "GB cohens kappa score: 0.562\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 288, 0\n",
      "KNN fn, tp: 4, 5\n",
      "KNN f1 score: 0.714\n",
      "KNN cohens kappa score: 0.708\n",
      "\n",
      "\n",
      "------ Step 1/5: Slice 2/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1117 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",
      "LR tn, fp: 282, 6\n",
      "LR fn, tp: 4, 5\n",
      "LR f1 score: 0.500\n",
      "LR cohens kappa score: 0.483\n",
      "LR average precision score: 0.723\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 284, 4\n",
      "GB fn, tp: 0, 9\n",
      "GB f1 score: 0.818\n",
      "GB cohens kappa score: 0.811\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 286, 2\n",
      "KNN fn, tp: 3, 6\n",
      "KNN f1 score: 0.706\n",
      "KNN cohens kappa score: 0.697\n",
      "\n",
      "\n",
      "------ Step 1/5: Slice 3/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1117 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",
      "LR tn, fp: 284, 4\n",
      "LR fn, tp: 5, 4\n",
      "LR f1 score: 0.471\n",
      "LR cohens kappa score: 0.455\n",
      "LR average precision score: 0.627\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 285, 3\n",
      "GB fn, tp: 2, 7\n",
      "GB f1 score: 0.737\n",
      "GB cohens kappa score: 0.728\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 285, 3\n",
      "KNN fn, tp: 3, 6\n",
      "KNN f1 score: 0.667\n",
      "KNN cohens kappa score: 0.656\n",
      "\n",
      "\n",
      "------ Step 1/5: Slice 4/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1117 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",
      "LR tn, fp: 287, 1\n",
      "LR fn, tp: 7, 2\n",
      "LR f1 score: 0.333\n",
      "LR cohens kappa score: 0.323\n",
      "LR average precision score: 0.358\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 288, 0\n",
      "GB fn, tp: 4, 5\n",
      "GB f1 score: 0.714\n",
      "GB cohens kappa score: 0.708\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 288, 0\n",
      "KNN fn, tp: 8, 1\n",
      "KNN f1 score: 0.200\n",
      "KNN cohens kappa score: 0.195\n",
      "\n",
      "\n",
      "------ Step 1/5: Slice 5/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1116 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",
      "LR tn, fp: 281, 7\n",
      "LR fn, tp: 3, 5\n",
      "LR f1 score: 0.500\n",
      "LR cohens kappa score: 0.483\n",
      "LR average precision score: 0.495\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 285, 3\n",
      "GB fn, tp: 2, 6\n",
      "GB f1 score: 0.706\n",
      "GB cohens kappa score: 0.697\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 286, 2\n",
      "KNN fn, tp: 3, 5\n",
      "KNN f1 score: 0.667\n",
      "KNN cohens kappa score: 0.658\n",
      "\n",
      "\n",
      "====== Step 2/5 =======\n",
      "-> Shuffling data\n",
      "-> Spliting data to slices\n",
      "\n",
      "------ Step 2/5: Slice 1/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1117 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",
      "LR tn, fp: 285, 3\n",
      "LR fn, tp: 2, 7\n",
      "LR f1 score: 0.737\n",
      "LR cohens kappa score: 0.728\n",
      "LR average precision score: 0.707\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 285, 3\n",
      "GB fn, tp: 2, 7\n",
      "GB f1 score: 0.737\n",
      "GB cohens kappa score: 0.728\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 287, 1\n",
      "KNN fn, tp: 3, 6\n",
      "KNN f1 score: 0.750\n",
      "KNN cohens kappa score: 0.743\n",
      "\n",
      "\n",
      "------ Step 2/5: Slice 2/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1117 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",
      "LR tn, fp: 281, 7\n",
      "LR fn, tp: 7, 2\n",
      "LR f1 score: 0.222\n",
      "LR cohens kappa score: 0.198\n",
      "LR average precision score: 0.426\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 284, 4\n",
      "GB fn, tp: 5, 4\n",
      "GB f1 score: 0.471\n",
      "GB cohens kappa score: 0.455\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 284, 4\n",
      "KNN fn, tp: 7, 2\n",
      "KNN f1 score: 0.267\n",
      "KNN cohens kappa score: 0.248\n",
      "\n",
      "\n",
      "------ Step 2/5: Slice 3/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1117 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ys/KpjC+iLfO3hWdkZOiDDz7Q2rVrtWvXLgUEBOjee+/Viy++eM1BrwUljUuYt7JjzsqnMkq6zK/81a5dW0OGDNGQIUN08uRJrV69WuvWrbumgACAyyvVbxyeP39ebre7yNj//vc/BQYGauzYsVq/fr1XwgFATXfVkl6wYIF69OihPXv2FBmfPn26unfvzq+EA4AXlVjS69at08yZMzVu3DjdeuutRY7NnTtX48eP1/Tp07VhwwavhgSAmqrEPemFCxdq0qRJGjFiRLFjwcHBGjVqlAoKCrRgwQLdfffdXgsJADVViSvpo0ePqkePHiWeoFevXjp8+HCFhgIAXFRiSQcGBionJ6fEExQWFqpWrVoVGgoAcFGJJR0VFaXVq1eXeILExES1aNGiQkMBAC4qcU969OjRGjp0qEJCQjRy5EgFBQV5jmVnZ+vtt9/W4sWLtWDBAq8HBYCaqMSSbtGihV599VVNmTJFr7/+upo0aaKQkBBlZGQoJSVFoaGh+tvf/qaOHTtWVl4AqFGu+huH3bt31+bNm5WUlKR9+/YpIyND9erVU5s2bdStWzf2owHAi0os6dzcXE2bNk0bNmyQv7+/evbsqaefftorH6h09uxZTZ06Vf/5z39Uq1YtDR48WE899VSFXwcAqpISS3rWrFlKSkpSfHy8nE6nlixZovT0dL3yyisVHmTcuHFq2bKl/v3vf+vs2bOKi4tTRESE+vTpU+HXAoCqosSS3rBhg2bMmKHOnTtLkqKjozVs2DDl5+fLz8+vwkLs2bNHx48f19KlS+Xn56ebbrpJixYtYisFQI1X4lvwzpw5o6ZNm3put27dWpZlKS0trUJD7N27V7fccotmz56t22+/XTExMdq0aZN+9rOfVeh1AKCqKXElXVhYKB8fH89th8MhPz8/5efnV2iI9PR07dy5U9HR0dqyZYuOHj2q+Ph4hYeHs90BoEYz4ptk/f39FRwcrPHjx0uSmjdvrkGDBmnTpk2lLumwMPu/HcYbSvowcFwZ81Z2zFn5eHverlrSCQkJRX6JpbCwUKtXry72pbTDhg0rd4imTZsqNzdXLpdL/v7+nuuUBd/MgkuYt7JjzsrH9m9madiwoZYuXVpkrH79+kpISCgy5nA4rqmkb7vtNoWGhmr69OmaPHmyUlJStHLlSk2dOrXc5wSA6qDEkv74448rJUStWrW0ePFiPf/887r99tvl7++v+Ph4Pv4UQI1nxJ60JN10001688037Y4BAEYp1XccAgDsQUkDgMEoaQAwGCUNAAajpAHAYJQ0ABiMkgYAg1HSAGAwShoADEZJA4DBKGkAMBglDQAGo6QBwGCUNAAYjJIGAINR0gBgMEoaAAxGSQOAwShpADAYJQ0ABqOkAcBglDQAGIySBgCDUdIAYDBKGgAMRkkDgMEoaQAwGCUNAAajpAHAYJQ0ABjMuJLOyMjQnXfeqYSEBLujAIDtjCvpP/7xjzpz5ozdMQDACEaVdGJiorKysnTLLbfYHQUAjGBMSR8/flyzZ8/WtGnT7I4CAMbwtTuAJBUWFmrSpEmaPHmywsPDy3WOsLDgCk5lhvDwELsjVEnMW9kxZ+Xj7XkzoqTnzp2rJk2aqFevXuU+R1paltxuqwJT2S88PESpqZl2x6hymLeyY87Kp6LmraSiN6Kk169fr7Nnz2rTpk2SpOzsbD333HNKTk7W1KlT7Q0HADYyoqQ/+uijIrf79eunESNG6P7777cpEQCYwZgXDgEAxRmxkv6p1atX2x0BAIzAShoADEZJA4DBKGkAMBglDQAGo6QBwGCUNAAYjJIGAINR0gBgMEoaAAxGSQOAwShpADAYJQ0ABqOkAcBglDQAGIySBgCDUdIAYDBKGgAMRkkDgMEoaQAwGCUNAAajpAHAYJQ0ABiMkgYAg1HSAGAwShoADEZJA4DBKGkAMBglDQAGo6QBwGCUNAAYzJiS/vTTT3X//ferffv2io2N1bJly+yOBAC287U7gCSdOnVK48eP1/Tp09WzZ0/t3btX8fHxuuGGG3T77bfbHQ8AbGPESvrEiRPq3bu3YmNj5XQ61aZNG0VHR2vXrl12RwMAWxmxko6KilJUVJTn9vnz57Vjxw7169fPxlQAYD+HZVmW3SF+LDMzU4899phq166t119/XU6nEYt9ALCFESvpS1JSUjRu3Dg1a9ZMM2bMKFNBp6Vlye026t+baxYeHqLU1Ey7Y1Q5zFvZMWflU1HzFh4ecsVjxixTt2/frsGDBysmJkazZs1SrVq17I4EALYzYiX9zTffaPTo0ZowYYLi4uLsjgMAxjBiJb1kyRJlZ2dr5syZioyM9Px5+eWX7Y4GALYy7oXD8mJPGpcwb2XHnJVPjdqTBgAUR0kDgMEoaQAwGCUNAAajpAHAYJQ0ABiMkgYAg1HSAGAwShoADEZJA4DBKGkAMBglDQAGo6QBwGCUNAAYjJIGAINR0gBgMEoaAAxGSQOAwShpADAYJQ0ABqOkAcBglDQAGIySBgCDUdIAYDBKGgAMRkkDgMEoaQAwGCUNAAajpAHAYMaU9P79+/Xggw+qXbt26tOnj5KTk+2OBAC2M6KkXS6Xxo0bp3vuuUfbt2/XmDFj9OijjyorK8vuaABgKyNK+osvvlB+fr5GjhwpPz8/3XfffWrWrJk++OADu6MBgK2MKOnDhw8rIiKiyFjTpk118OBBmxIBgBl87Q4gSTk5OQoICCgyFhgYqNzcXK9d89FPh3j+Pv+2ZV67DgBcCyNKOigoSHl5eUXGcnNzFRQUVOpzhIUFl/v64eEh5X6st5mczWTMW9kxZ+Xj7XkzoqQjIiK0cOHCImNHjx5V//79S32OtLQsud1Wua6fmppZrsd5W3h4iLHZTMa8ld21zlnhlk0qfO4PUkb6Ve/7n0aRWtcqRr33blanY7vLfc1r5nBI1vedERYmpaVd/LvTKQWHSPXry+dXT8idvEfW++9J/rU8Yz49YyUVnbfCLZvkfm+RnEPjPMdLq6SiN6KkO3XqJMuytHDhQg0bNkwbN27UgQMHFBtbtidaFmxxoKr47Eia1uw5pb5tr1eXiLAr3q+kkvjxMXfyHlmrlus/d9yv9+q2lAIDNPybTxXVMFif7zup9xrdphwff+X5+Cnft5ZCs9J0uu71kqQ62d8p3y9QDc+d1JHwxrKcPpJlKaDAKZ/ef1S+38X7n6nz/3RdXpZiDmzTwQYRRQp5XasYnardQOtaxdhb0taPFnWXClqS3O6L/9jkZMv93iJZB/ZLFy5c/PP92OVK2P3eIlnHjl3xeHkZ8cKhv7+/3nrrLW3YsEHR0dF64403NGfOHIWGhtodDbDdmj2ndOJ8rtbsOVXi/X5cEiUds1Ytl7Kztc7RQKdq1dEpdy2tvq6ZrFXLte5nbXQqOFzfXReq7IAQuXz9PQUtSenXhSrXL0CHG0RcLGhJcjh0wS9A2YE/3N9yOJQVEKKNv7jTU8iX9N67WddnnFHvvZsrZoLKy+H44e9hP/rHz+mUateRft5IzqFxcgwcLAUEFBm7HOfQODkaXfl4uWNallW+PQLDXMt2h6n4v+3lU93mzZsr6aXfr6Tjvvm3ohpep8/3ndTS71fSF8q0ks6Tb0G+XCWtpE/uvRimXqgc99wn7U0u19aASSrqZ62k7Q5K2mDVrWwqC/NWdsxZ+VRGSRux3QEAuDxKGgAMRkkDgMEoaQAwGCUNAAajpAHAYJQ0ABiMkgYAg1HSAGAwShoADGbEp+BVBKfTcfU7VUHV9Xl5G/NWdsxZ+Xh73qrNZ3cAQHXEdgcAGIySBgCDUdIAYDBKGgAMRkkDgMEoaQAwGCUNAAajpAHAYJQ0ABiMkjbcO++8o549e6pDhw4aNGiQduzYYXckI+3fv18PPvig2rVrpz59+ig5OdnuSFXCp59+qvvvv1/t27dXbGysli1bZnekKiUjI0N33nmnEhISvHcRC8basGGD1a1bN+vQoUNWYWGhtXLlSqt9+/ZWWlqa3dGMkpeXZ/Xo0cN6++23LZfLZa1bt86KioqyMjMz7Y5mtJMnT1qRkZHWxo0brcLCQmvPnj1Wx44drU8++cTuaFXGU089ZTVv3txatWqV167BStpgqampGjNmjJo1ayan06mBAwfKx8dHBw4csDuaUb744gvl5+dr5MiR8vPz03333admzZrpgw8+sDua0U6cOKHevXsrNjZWTqdTbdq0UXR0tHbt2mV3tCohMTFRWVlZuuWWW7x6nWrzKXhVlcvlUnp6erFxh8OhYcOGFRnbvn27cnJydPPNN1dWvCrh8OHDioiIKDLWtGlTHTx40KZEVUNUVJSioqI8t8+fP68dO3aoX79+NqaqGo4fP67Zs2dr2bJlio+P9+q1KGmb7d69W8OHDy827uPjo6+++spz++DBg5owYYKefPJJ1a9fvzIjGi8nJ0cBAQFFxgIDA5Wbm2tToqonMzNTY8eOVdu2bdWzZ0+74xitsLBQkyZN0uTJkxUeHu7161HSNuvUqdNVty+SkpL09NNPa9SoURo1alQlJas6goKClJeXV2QsNzdXQUFBNiWqWlJSUjRu3Dg1a9ZMM2bMkNPJLmhJ5s6dqyZNmqhXr16Vcj3+1zDcO++8o4kTJ+qFF17QY489ZnccI0VERCglJaXI2NGjR9WsWTObElUd27dv1+DBgxUTE6NZs2apVq1adkcy3vr167VhwwbPdtHBgwf13HPPaerUqd65oNdeksQ1W79+vdW2bVvryy+/tDuK0fLy8qw77rijyLs7IiMjeRfMVRw7dsyKjIy03n33XbujVGl9+/bl3R011VtvvSWXy6WRI0cqMjLS8ycpKcnuaEbx9/fXW2+9pQ0bNig6OlpvvPGG5syZo9DQULujGW3JkiXKzs7WzJkzi/x8vfzyy3ZHw4/w9VkAYDBW0gBgMEoaAAxGSQOAwShpADAYJQ0ABqOkAcBglDSqtbvuuku33nqr50+LFi3Uo0cPTZ8+XTk5OZ77ffXVV3riiSfUtWtXRUZGauDAgVq7du1lz/nFF1/o1ltv1R/+8IfLHj9y5Iji4uLUrl07/fKXv9TGjRu98txQM1DSqPYmTpyobdu2adu2bUpKStK0adO0Zs0avfjii5KkrVu36qGHHtL111+vv//970pMTFSfPn00ZcoUzZ8/v9j51qxZo8aNG2v9+vW6cOFCkWN5eXmKj49XkyZNtGrVKg0ePFgTJ07Uvn37KuW5ohry2u8yAgbo0aOHtWjRomLjb7zxhtWhQwcrKyvL6ty5s/Xaa68Vu8+8efOsNm3aFPn18ry8PCsqKspKSEiwWrdubSUmJhZ5TGJiotW1a1crPz/fM/b4449bU6ZMqbgnhRqFlTRqJB8fH/n7+yspKUmZmZkaOXJksfsMHTpUCxYsUO3atT1jSUlJysrKUvfu3XXbbbdp1apVRR6za9cutWvXTr6+P3zAZMeOHbVz506vPRdUb5Q0ahS3263k5GQtXrxYMTEx2rdvn5o0aaLg4OBi9w0ODlaHDh2KFO6aNWvUvn17hYaGKjY2Vtu3b9fx48c9x8+ePasGDRoUOU94eLjOnDnjvSeFao2SRrX3l7/8xfPhQa1bt9bQoUPVpk0b/eY3v1FGRsZlC/py0tPTtXXrVsXGxkq6+KKkj49PkdV0bm6u/P39izzO399fLper4p4QahQ+9B/V3ujRo9W3b19Jkp+fn+rXr+8p0nr16ikjI6NU5/nwww+Vn5/v+bD3unXrKjo6WomJiXriiSfkdDoVEBBQrJBdLlexb44BSouVNKq9evXqqVGjRmrUqJEaNmxYZKXbpk0bpaSkKCsrq9jjMjMzFRcXp//+97+SLm51SFJMTIxatGihFi1a6LPPPtPp06e1bds2SVKDBg2Umppa5DypqanFtkCA0qKkUaN169ZNoaGhevvtt4sdW7ZsmXbv3q0bbrhBJ06c0K5duzR+/Hj94x//8PxJSEjQdddd59nyaN++vXbv3q2CggLPebZv367IyMhKe06oXtjuQI0WEBCgqVOn6qmnnlJ2drYGDBggX19fbdy4UXPmzNGkSZMUGhqq119/XbVq1dLw4cOLvNtDkgYMGKD3339f586dU69evTRz5kz97ne/U3x8vP71r3/pn//8p1asWGHTM0RVx0oaNV5MTIzmz5+vQ4cOacSIERo0aJA+/vhjvfTSSxoxYoQkae3atbr33nuLFbQkDRs2TAUFBVq7dq2CgoL05ptv6uuvv9aAAQO0fPlyvfLKK2revHllPy1UE3wzCwAYjJU0ABiMkgYAg1HSAGAwShoADEZJA4DBKGkAMBglDQAGo6QBwGCUNAAY7P8Avg1FKJFo+nAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "LR tn, fp: 286, 2\n",
      "LR fn, tp: 6, 3\n",
      "LR f1 score: 0.429\n",
      "LR cohens kappa score: 0.416\n",
      "LR average precision score: 0.762\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 286, 2\n",
      "GB fn, tp: 1, 8\n",
      "GB f1 score: 0.842\n",
      "GB cohens kappa score: 0.837\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 288, 0\n",
      "KNN fn, tp: 4, 5\n",
      "KNN f1 score: 0.714\n",
      "KNN cohens kappa score: 0.708\n",
      "\n",
      "\n",
      "------ Step 2/5: Slice 4/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1117 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",
      "LR tn, fp: 285, 3\n",
      "LR fn, tp: 2, 7\n",
      "LR f1 score: 0.737\n",
      "LR cohens kappa score: 0.728\n",
      "LR average precision score: 0.900\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 285, 3\n",
      "GB fn, tp: 2, 7\n",
      "GB f1 score: 0.737\n",
      "GB cohens kappa score: 0.728\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 286, 2\n",
      "KNN fn, tp: 3, 6\n",
      "KNN f1 score: 0.706\n",
      "KNN cohens kappa score: 0.697\n",
      "\n",
      "\n",
      "------ Step 2/5: Slice 5/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1116 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",
      "LR tn, fp: 286, 2\n",
      "LR fn, tp: 4, 4\n",
      "LR f1 score: 0.571\n",
      "LR cohens kappa score: 0.561\n",
      "LR average precision score: 0.640\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 286, 2\n",
      "GB fn, tp: 7, 1\n",
      "GB f1 score: 0.182\n",
      "GB cohens kappa score: 0.170\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 288, 0\n",
      "KNN fn, tp: 5, 3\n",
      "KNN f1 score: 0.545\n",
      "KNN cohens kappa score: 0.539\n",
      "\n",
      "\n",
      "====== Step 3/5 =======\n",
      "-> Shuffling data\n",
      "-> Spliting data to slices\n",
      "\n",
      "------ Step 3/5: Slice 1/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1117 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",
      "LR tn, fp: 285, 3\n",
      "LR fn, tp: 4, 5\n",
      "LR f1 score: 0.588\n",
      "LR cohens kappa score: 0.576\n",
      "LR average precision score: 0.677\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 286, 2\n",
      "GB fn, tp: 3, 6\n",
      "GB f1 score: 0.706\n",
      "GB cohens kappa score: 0.697\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 287, 1\n",
      "KNN fn, tp: 5, 4\n",
      "KNN f1 score: 0.571\n",
      "KNN cohens kappa score: 0.562\n",
      "\n",
      "\n",
      "------ Step 3/5: Slice 2/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1117 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",
      "LR tn, fp: 283, 5\n",
      "LR fn, tp: 3, 6\n",
      "LR f1 score: 0.600\n",
      "LR cohens kappa score: 0.586\n",
      "LR average precision score: 0.650\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 285, 3\n",
      "GB fn, tp: 3, 6\n",
      "GB f1 score: 0.667\n",
      "GB cohens kappa score: 0.656\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 287, 1\n",
      "KNN fn, tp: 6, 3\n",
      "KNN f1 score: 0.462\n",
      "KNN cohens kappa score: 0.451\n",
      "\n",
      "\n",
      "------ Step 3/5: Slice 3/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1117 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",
      "LR tn, fp: 285, 3\n",
      "LR fn, tp: 4, 5\n",
      "LR f1 score: 0.588\n",
      "LR cohens kappa score: 0.576\n",
      "LR average precision score: 0.771\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 288, 0\n",
      "GB fn, tp: 3, 6\n",
      "GB f1 score: 0.800\n",
      "GB cohens kappa score: 0.795\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 288, 0\n",
      "KNN fn, tp: 7, 2\n",
      "KNN f1 score: 0.364\n",
      "KNN cohens kappa score: 0.357\n",
      "\n",
      "\n",
      "------ Step 3/5: Slice 4/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1117 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",
      "LR tn, fp: 286, 2\n",
      "LR fn, tp: 6, 3\n",
      "LR f1 score: 0.429\n",
      "LR cohens kappa score: 0.416\n",
      "LR average precision score: 0.738\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 288, 0\n",
      "GB fn, tp: 4, 5\n",
      "GB f1 score: 0.714\n",
      "GB cohens kappa score: 0.708\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 288, 0\n",
      "KNN fn, tp: 4, 5\n",
      "KNN f1 score: 0.714\n",
      "KNN cohens kappa score: 0.708\n",
      "\n",
      "\n",
      "------ Step 3/5: Slice 5/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1116 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",
      "LR tn, fp: 283, 5\n",
      "LR fn, tp: 5, 3\n",
      "LR f1 score: 0.375\n",
      "LR cohens kappa score: 0.358\n",
      "LR average precision score: 0.483\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 284, 4\n",
      "GB fn, tp: 1, 7\n",
      "GB f1 score: 0.737\n",
      "GB cohens kappa score: 0.728\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 285, 3\n",
      "KNN fn, tp: 3, 5\n",
      "KNN f1 score: 0.625\n",
      "KNN cohens kappa score: 0.615\n",
      "\n",
      "\n",
      "====== Step 4/5 =======\n",
      "-> Shuffling data\n",
      "-> Spliting data to slices\n",
      "\n",
      "------ Step 4/5: Slice 1/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1117 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",
      "LR tn, fp: 284, 4\n",
      "LR fn, tp: 3, 6\n",
      "LR f1 score: 0.632\n",
      "LR cohens kappa score: 0.619\n",
      "LR average precision score: 0.738\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 287, 1\n",
      "GB fn, tp: 4, 5\n",
      "GB f1 score: 0.667\n",
      "GB cohens kappa score: 0.658\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 285, 3\n",
      "KNN fn, tp: 5, 4\n",
      "KNN f1 score: 0.500\n",
      "KNN cohens kappa score: 0.486\n",
      "\n",
      "\n",
      "------ Step 4/5: Slice 2/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1117 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",
      "LR tn, fp: 284, 4\n",
      "LR fn, tp: 5, 4\n",
      "LR f1 score: 0.471\n",
      "LR cohens kappa score: 0.455\n",
      "LR average precision score: 0.615\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 287, 1\n",
      "GB fn, tp: 3, 6\n",
      "GB f1 score: 0.750\n",
      "GB cohens kappa score: 0.743\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 287, 1\n",
      "KNN fn, tp: 6, 3\n",
      "KNN f1 score: 0.462\n",
      "KNN cohens kappa score: 0.451\n",
      "\n",
      "\n",
      "------ Step 4/5: Slice 3/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1117 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",
      "LR tn, fp: 282, 6\n",
      "LR fn, tp: 4, 5\n",
      "LR f1 score: 0.500\n",
      "LR cohens kappa score: 0.483\n",
      "LR average precision score: 0.703\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 284, 4\n",
      "GB fn, tp: 3, 6\n",
      "GB f1 score: 0.632\n",
      "GB cohens kappa score: 0.619\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 283, 5\n",
      "KNN fn, tp: 3, 6\n",
      "KNN f1 score: 0.600\n",
      "KNN cohens kappa score: 0.586\n",
      "\n",
      "\n",
      "------ Step 4/5: Slice 4/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1117 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",
      "LR tn, fp: 285, 3\n",
      "LR fn, tp: 6, 3\n",
      "LR f1 score: 0.400\n",
      "LR cohens kappa score: 0.385\n",
      "LR average precision score: 0.654\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 288, 0\n",
      "GB fn, tp: 4, 5\n",
      "GB f1 score: 0.714\n",
      "GB cohens kappa score: 0.708\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 288, 0\n",
      "KNN fn, tp: 6, 3\n",
      "KNN f1 score: 0.500\n",
      "KNN cohens kappa score: 0.492\n",
      "\n",
      "\n",
      "------ Step 4/5: Slice 5/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1116 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",
      "LR tn, fp: 286, 2\n",
      "LR fn, tp: 4, 4\n",
      "LR f1 score: 0.571\n",
      "LR cohens kappa score: 0.561\n",
      "LR average precision score: 0.756\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 286, 2\n",
      "GB fn, tp: 2, 6\n",
      "GB f1 score: 0.750\n",
      "GB cohens kappa score: 0.743\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 288, 0\n",
      "KNN fn, tp: 4, 4\n",
      "KNN f1 score: 0.667\n",
      "KNN cohens kappa score: 0.661\n",
      "\n",
      "\n",
      "====== Step 5/5 =======\n",
      "-> Shuffling data\n",
      "-> Spliting data to slices\n",
      "\n",
      "------ Step 5/5: Slice 1/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1117 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",
      "LR tn, fp: 283, 5\n",
      "LR fn, tp: 2, 7\n",
      "LR f1 score: 0.667\n",
      "LR cohens kappa score: 0.655\n",
      "LR average precision score: 0.724\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 284, 4\n",
      "GB fn, tp: 1, 8\n",
      "GB f1 score: 0.762\n",
      "GB cohens kappa score: 0.753\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 288, 0\n",
      "KNN fn, tp: 2, 7\n",
      "KNN f1 score: 0.875\n",
      "KNN cohens kappa score: 0.872\n",
      "\n",
      "\n",
      "------ Step 5/5: Slice 2/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1117 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",
      "LR tn, fp: 288, 0\n",
      "LR fn, tp: 6, 3\n",
      "LR f1 score: 0.500\n",
      "LR cohens kappa score: 0.492\n",
      "LR average precision score: 0.769\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 288, 0\n",
      "GB fn, tp: 4, 5\n",
      "GB f1 score: 0.714\n",
      "GB cohens kappa score: 0.708\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 288, 0\n",
      "KNN fn, tp: 5, 4\n",
      "KNN f1 score: 0.615\n",
      "KNN cohens kappa score: 0.608\n",
      "\n",
      "\n",
      "------ Step 5/5: Slice 3/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1117 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",
      "LR tn, fp: 284, 4\n",
      "LR fn, tp: 2, 7\n",
      "LR f1 score: 0.700\n",
      "LR cohens kappa score: 0.690\n",
      "LR average precision score: 0.787\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 288, 0\n",
      "GB fn, tp: 3, 6\n",
      "GB f1 score: 0.800\n",
      "GB cohens kappa score: 0.795\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 287, 1\n",
      "KNN fn, tp: 5, 4\n",
      "KNN f1 score: 0.571\n",
      "KNN cohens kappa score: 0.562\n",
      "\n",
      "\n",
      "------ Step 5/5: Slice 4/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1117 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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VhUfSJ06c0IABAyrcwKBBg3T8+PFqDQUAuKLCkg4KClJ+fn6FGygtLVWDBg2uO8iFCxeUlJSkHj16qG/fvnrxxReve5sAUNtVWNJRUVFav359hRtISUlRx44drztIUlKSnE6n/v73v+utt97Su+++q40bN173dgGgNqtwTXrixIkaM2aMQkNDNX78eAUHB5c9dunSJb3++utauXKlli1bdl0hDh48qFOnTmn16tXy9/dXq1attGLFimo5QgeA2qzCku7YsaNeeuklzZw5U4sXL1Z4eLhCQ0OVk5OjjIwMhYWF6c9//rN69ux5XSEOHTqk9u3ba8GCBUpOTlaDBg00ZswYPfTQQ9e1XQCo7Sr9xmFMTIy2b9+u1NRUHT58WDk5OWrSpIm6dOmifv36VcvRbnZ2tvbu3avo6Gjt2LFDJ06cUGJiopxOp4YMGeLRNpo2tf/6ITWhoq+Lwh1z5TnmqmrsnK8KS7qgoECzZ8/Wli1bFBAQoIEDB+rJJ5+s9gsqBQQEKCQkRFOmTJEkdejQQSNHjtS2bds8Lmmu3VG/MVeeY66qxuhrd8yfP1+pqalKTEyUw+HQqlWrlJ2dXe1nXrRt21YFBQUqKipSQECApCtnjQBAfVdhSW/ZskVz585V7969JUnR0dGKj49XcXGx/P39qy3EbbfdprCwMM2ZM0czZsxQRkaG1q5dq1mzZlXbewBAbVThKXjnz59X27Zty+537txZlmUpKyurWkM0aNBAK1eu1KlTp9S/f38lJiYqMTGRbzECqPcqPJIuLS2Vr69v2X0fHx/5+/uruLi42oO0atVKr776arVvFwBqM48uVQoAsEelp+AlJye7fYmltLRU69evL/dLaePj46s/HQDUcxWWdMuWLbV69Wq3sWbNmik5OdltzMfHh5IGgBpQYUl/8MEH3soBALgK1qQBwGCUNAAYjJIGAINR0gBgMEoaAAxGSQOAwShpADAYJQ0ABqOkAcBglDQAGIySBgCDUdIAYDBKGgAMRkkDgMEoaQAwGCUNAAajpAHAYJQ0ABiMkgYAg1HSAGAwShoADEZJA4DBKGkAMBglDQAGo6QBwGCUNAAYjJIGAIMZV9I5OTm6/fbblZycbHcUALCdcSX929/+VufPn7c7BgAYwaiSTklJUV5entq3b293FAAwgjElferUKS1YsECzZ8+2OwoAGMPP7gCSVFpaqunTp2vGjBlyOp0/ahtNm4ZUcyozOJ2hdkeoNZgrzzFXVWPnfBlR0osWLVJ4eLgGDRr0o7eRlZUnl8uqxlT2czpDlZmZa3eMWoG58hxzVTXemK+KfggYUdKbN2/WhQsXtG3bNknSpUuX9Mwzzyg9PV2zZs2yNxwA2MiIkn7//ffd7g8bNkzjxo3TfffdZ1MiADCDMR8cAgDKM+JI+ofWr19vdwQAMAJH0gBgMEoaAAxGSQOAwShpADAYJQ0ABqOkAcBglDQAGIySBgCDUdIAYDBKGgAMRkkDgMEoaQAwGCUNAAajpAHAYJQ0ABiMkgYAg1HSAGAwShoADEZJA4DBKGkAMBglDQAGo6QBwGCUNAAYjJIGAINR0gBgMEoaAAxGSQOAwShpADAYJQ0ABqOkAcBglDQAGIySBgCDGVPSn3zyie677z51795dcXFxWrNmjd2RAMB2fnYHkKSzZ89qypQpmjNnjgYOHKhDhw4pMTFRN954o/r37293PACwjRFH0qdPn9bgwYMVFxcnh8OhLl26KDo6Wvv27bM7GgDYyogj6aioKEVFRZXdv3jxovbs2aNhw4bZmAoA7OdjWZZld4jvy83N1SOPPKKGDRtq8eLFcjiMONgHAFsYcST9bxkZGUpKSlJERITmzp1bpYLOysqTy2XUz5vr5nSGKjMz1+4YtQJz5Tnmqmq8MV9OZ+g1HzPmMDUtLU2jRo1SbGys5s+frwYNGtgdCQBsZ8SR9Ndff62JEydq6tSpSkhIsDsOABjDiCPpVatW6dKlS5o3b54iIyPL/rzwwgt2RwMAWxn3weGPxZp0/cZceY65qhrWpAEA10RJA4DBKGkAMBglDQAGo6QBwGCUNAAYjJIGAINR0gBgMEoaAAxGSQOAwShpADAYJQ0ABqOkAcBglDQAGIySBgCDUdIAYDBKGgAMRkkDgMEoaQAwGCUNAAajpAHAYJQ0ABiMkgYAg1HSAGAwShoADEZJA4DBKGkAMBglDQAGo6QBwGCUNAAYjJIGAIMZU9JffPGF7r//fnXr1k1DhgxRenq63ZEAwHZGlHRRUZGSkpJ01113KS0tTZMmTdLDDz+svLw8u6MBgK2MKOndu3eruLhY48ePl7+/v+655x5FRETovffeszsaANjKz+4AknT8+HG1a9fObaxt27Y6evRojb3nw5+MLru99LY1NfY+AHA9jCjp/Px8BQYGuo0FBQWpoKDA4200bRryo9/f6Qz90a+taSZnMw1z5TnmqmrsnC8jSjo4OFiFhYVuYwUFBQoODvZ4G1lZeXK5rB/1/pmZuT/qdTXN6Qw1Nptp6uNcle7YJtdfV8gxJkG+A+M8fl19nKvr4Y35quiHgBEl3a5dOy1fvtxt7MSJExo+fHiNvSdLHKjtXH9dIevkSbn+uuKaJV3857my1r0tnxGj5D/1V2Xj/y5468J5/a+jmTZFD9Pw0QPlSj+olZ9/J5WWqmX2eR1qeYsGHf5QCXuS9b+tI7WpU6xCCvJ0oFUnhVzOU0TmV9rdJlLy8XF7X4erVEM/26qEPclXBho2ku9vf1elHya4woiS7tWrlyzL0vLlyxUfH6+tW7fqyJEjiovjHyhwLY4xCWVH0tdirXtbunTpyt/fK+l/F7z+L1Ob7hmts34h2nDwrFxHvtXZkP+SJJ1p3EKWfLT1ltuVsCdZmzrF6mzD5spuHiGXj0PfhoRpzw2NyxW0JLkcvtp6y+1qn5mhTZ1i1f78lzr6wTcaPD9evU7ur96JCAqSSkokPz/JsuRz/xi3H0i1nRFndwQEBOi1117Tli1bFB0drVdeeUULFy5UWFiY3dEAY/kOjJP/0jcrPDr1GTFKuuGGK39/j2NMgnxat5ZatdLgQ9vVoiRPQ7u20NAOYWqZl6kW2ecUdfKAgoova9DhDyXpyvNyzivqqwMKKClS07xvFfXVAckqv8zocJVq0OEPy4p96y2362zD5trUKbY6p+CKggKpuPjK35cvX/mBVIf4WNZVZrgWup41aVOxdug55spzNTVX/15aUXGx9K/PmP69RNL+/Jc62rydBh/aXuuOpO1ek6akDUbxeI658hxzVTV2l7QRyx0AgKujpAHAYJQ0ABiMkgYAg1HSAGAwShoADEZJA4DBKGkAMBglDQAGo6QBwGBGXAWvOjgc5a/EVRfU1f2qCcyV55irqrFzvurMtTsAoC5iuQMADEZJA4DBKGkAMBglDQAGo6QBwGCUNAAYjJIGAINR0gBgMEoaAAxGSRvujTfe0MCBA9WjRw+NHDlSe/bssTuSUb744gvdf//96tatm4YMGaL09HS7Ixnrk08+0X333afu3bsrLi5Oa9assTuS8XJycnT77bcrOTnZvhAWjLVlyxarX79+1rFjx6zS0lJr7dq1Vvfu3a2srCy7oxmhsLDQGjBggPX6669bRUVF1qZNm6yoqCgrNzfX7mjGOXPmjBUZGWlt3brVKi0ttQ4ePGj17NnT+uijj+yOZrQnnnjC6tChg7Vu3TrbMnAkbbDMzExNmjRJERERcjgcGjFihHx9fXXkyBG7oxlh9+7dKi4u1vjx4+Xv76977rlHEREReu+99+yOZpzTp09r8ODBiouLk8PhUJcuXRQdHa19+/bZHc1YKSkpysvLU/v27W3NUWeugldbFRUVKTs7u9y4j4+P4uPj3cbS0tKUn5+vm266yVvxjHb8+HG1a9fObaxt27Y6evSoTYnMFRUVpaioqLL7Fy9e1J49ezRs2DAbU5nr1KlTWrBggdasWaPExERbs1DSNtu/f7/Gjh1bbtzX11eff/552f2jR49q6tSpevzxx9WsWTNvRjRWfn6+AgMD3caCgoJUUFBgU6LaITc3V5MnT1bXrl01cOBAu+MYp7S0VNOnT9eMGTPkdDrtjkNJ261Xr16VLl+kpqbqySef1IQJEzRhwgQvJTNfcHCwCgsL3cYKCgoUHBxsUyLzZWRkKCkpSREREZo7d64cDlY8f2jRokUKDw/XoEGD7I4iibM7jPfGG29o2rRp+sMf/qBHHnnE7jhGadeunTIyMtzGTpw4oYiICJsSmS0tLU2jRo1SbGys5s+frwYNGtgdyUibN2/Wli1bypaIjh49qmeeeUazZs2yJ5BtH1miUps3b7a6du1qHThwwO4oRiosLLR+9rOfuZ3dERkZydkvV3Hy5EkrMjLSevPNN+2OUusMHTqUsztwda+99pqKioo0fvx4RUZGlv1JTU21O5oRAgIC9Nprr2nLli2Kjo7WK6+8ooULFyosLMzuaMZZtWqVLl26pHnz5rn9u/TCCy/YHQ2V4NdnAYDBOJIGAINR0gBgMEoaAAxGSQOAwShpADAYJQ0ABqOkUafdcccduvnmm8v+dOzYUQMGDNCcOXOUn59f9rzPP/9cjz32mPr27avIyEiNGDFCGzduvOo2d+/erZtvvln/8z//c9XHv/zySyUkJKhbt276+c9/rq1bt9bIvqF+oKRR502bNk27du3Srl27lJqaqtmzZ2vDhg169tlnJUk7d+7UAw88oBYtWugvf/mLUlJSNGTIEM2cOVNLly4tt70NGzaoTZs22rx5sy5fvuz2WGFhoRITExUeHq5169Zp1KhRmjZtmg4fPuyVfUUdZNt3HQEvGDBggLVixYpy46+88orVo0cPKy8vz+rdu7f18ssvl3vOkiVLrC5durh9zbywsNCKioqykpOTrc6dO1spKSlur0lJSbH69u1rFRcXl409+uij1syZM6tvp1CvcCSNesnX11cBAQFKTU1Vbm6uxo8fX+45Y8aM0bJly9SwYcOysdTUVOXl5SkmJka33Xab1q1b5/aaffv2qVu3bvLz+88FJnv27Km9e/fW2L6gbqOkUa+4XC6lp6dr5cqVio2N1eHDhxUeHq6QkJByzw0JCVGPHj3cCnfDhg3q3r27wsLCFBcXp7S0NJ06dars8QsXLqh58+Zu23E6nTp//nzN7RTqNEoadd4f//jHsgsKde7cWWPGjFGXLl30q1/9Sjk5OVct6KvJzs7Wzp07FRcXJ+nKh5K+vr5uR9MFBQUKCAhwe11AQICKioqqb4dQr3DRf9R5EydO1NChQyVJ/v7+atasWVmRNmnSRDk5OR5t529/+5uKi4vLLgbfuHFjRUdHKyUlRY899pgcDocCAwPLFXJRUVG53yADeIojadR5TZo0UevWrdW6dWu1bNnS7Ui3S5cuysjIUF5eXrnX5ebmKiEhQZ999pmkK0sdkhQbG6uOHTuqY8eO+vTTT3Xu3Dnt2rVLktS8eXNlZma6bSczM7PcEgjgKUoa9Vq/fv0UFham119/vdxja9as0f79+3XjjTfq9OnT2rdvn6ZMmaJ333237E9ycrJuuOGGsiWP7t27a//+/SopKSnbTlpamiIjI722T6hbWO5AvRYYGKhZs2bpiSee0KVLl3TvvffKz89PW7du1cKFCzV9+nSFhYVp8eLFatCggcaOHet2tock3XvvvXrrrbf03XffadCgQZo3b55+/etfKzExUR9//LE+/PBDvfPOOzbtIWo7jqRR78XGxmrp0qU6duyYxo0bp5EjR+qDDz7Q888/r3HjxkmSNm7cqLvvvrtcQUtSfHy8SkpKtHHjRgUHB+vVV1/VV199pXvvvVdvv/22XnzxRXXo0MHbu4U6gt/MAgAG40gaAAxGSQOAwShpADAYJQ0ABqOkAcBglDQAGIySBgCDUdIAYDBKGgAM9v8ByP67rR4NqfQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "LR tn, fp: 286, 2\n",
      "LR fn, tp: 6, 3\n",
      "LR f1 score: 0.429\n",
      "LR cohens kappa score: 0.416\n",
      "LR average precision score: 0.649\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 287, 1\n",
      "GB fn, tp: 4, 5\n",
      "GB f1 score: 0.667\n",
      "GB cohens kappa score: 0.658\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 287, 1\n",
      "KNN fn, tp: 6, 3\n",
      "KNN f1 score: 0.462\n",
      "KNN cohens kappa score: 0.451\n",
      "\n",
      "\n",
      "------ Step 5/5: Slice 5/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1116 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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6qUaNGv7KBQDQJc7ueOedd9SmTRtNnTpVHTp00JAhQ7Ro0SKdOHHCX/kAoErz6f2kXS6XUlJStGbNGm3evFlFRUWSpLFjx2ro0KGKiLD/Dfd5P+mqh3nyHXPlGxPfT/qy3/Q/KytLH330kdasWaMdO3YoNDRU9957r1588cWrDno1KOmqh3nyHXPlGxNL+rKf+atWrZoGDhyogQMH6vjx41q1apU+/PDDqwoIALgwn15xeObMGbndbq+x//znPwoLC9Po0aO1du3acgkHAFXdJUt6wYIF6tKli3bt2uU1Pm3aNHXq1ImXhANAOSq1pD/88EPNmDFDY8aM0c033+x12Zw5czR27FhNmzZN69atK9eQAFBVlbonvXDhQk2cOFHDhg0rcVlERIRGjhypoqIiLViwQHfffXe5hQSAqqrUlfShQ4fUpUuXUg/QrVs3HThwoExDAQDOKbWkw8LClJeXV+oBiouLFRISUqahAADnlFrSsbGxWrVqVakHSE5OVtOmTcs0FADgnFL3pB999FENGjRIkZGRGj58uMLDwz2X5ebm6q233tLixYu1YMGCcg8KAFVRqSXdtGlTvfrqq5o8ebLmzp2rBg0aKDIyUllZWTp8+LCioqL097//XW3atPFXXgCoUi75isNOnTpp48aNSklJ0Z49e5SVlaUaNWqoefPm6tChA/vRAFCOSi3p/Px8TZ06VevWrVNwcLC6du2qJ5980og3VAKAqqDUkp45c6ZSUlKUmJgop9OpJUuWKDMzU6+88oqf4gFA1VZqSa9bt07Tp09X27ZtJUlxcXEaPHiwCgsLFRQU5JeAAFCVlXoK3smTJ9WwYUPP982aNZNlWcrIyCj3YACAS5R0cXGxAgICPN87HA4FBQWpsLCwzIOcOnVKY8aMUevWrdW+fXu2VABAPr5VqT+MGTNG0dHR+te//qX33ntPH3zwgdasWWN3LACw1SVPwUtKSvJ6EUtxcbFWrVpV4kNpBw8efMUhdu3apaNHj2rp0qUKCgrSDTfcoEWLFnF6H4Aqr9SSrlu3rpYuXeo1VqtWLSUlJXmNORyOqyrp3bt366abbtLs2bOVlJSkkJAQDRo0SA8//PAVHxMAKoNSS/rTTz/1S4jMzExt375dcXFx2rRpkw4dOqTExERFR0erZ8+ePh2jZs3Kde52aZ95hl8xT75jrnxj2jxd9mcclofg4GBFRERo7NixkqQmTZqof//+2rBhg88lzQfRVj3Mk++YK9+Y+EG0Rjxx2LBhQ+Xn58vlcnnGiouLbUwEAGYwoqTvuOMORUVFadq0aXK5XNq3b59WrFih7t272x0NAGxlREmHhIRo8eLFOnr0qDp27KjExEQlJibykVwAqjwj9qQl6YYbbtC8efPsjgEARjFiJQ0AuDBKGgAMRkkDgMEoaQAwGCUNAAajpAHAYJQ0ABiMkgYAg1HSAGAwShoADEZJA4DBKGkAMBglDQAGo6QBwGCUNAAYjJIGAINR0gBgMEoaAAxGSQOAwShpADAYJQ0ABqOkAcBglDQAGIySBgCDUdIAYDBKGgAMRkkDgMEoaQAwmHElnZWVpc6dOyspKcnuKABgO+NK+rnnntPJkyftjgEARjCqpJOTk5WTk6ObbrrJ7igAYARjSvro0aOaPXu2pk6dancUADBGoN0BJKm4uFgTJ07UpEmTFB0dfUXHqFkzooxT2Ss6OtLuCBUC8+Q75so3ps2TESU9Z84cNWjQQN26dbviY2Rk5MjttsowlX2ioyOVnp5tdwzjMU++Y658Y9c8lfbAYERJr127VqdOndKGDRskSbm5uXr++eeVmpqqKVOm2BsOAGxkREl/8sknXt/37t1bw4YNU9++fW1KBABmMOaJQwBASUaspP/bqlWr7I4AAEZgJQ0ABqOkAcBglDQAGIySBgCDUdIAYDBKGgAMRkkDgMEoaQAwGCUNAAajpAHAYJQ0ABiMkgYAg1HSAGAwShoADEZJA4DBKGkAMBglDQAGo6QBwGCUNAAYjJIGAINR0gBgMEoaAAxGSQOAwShpADAYJQ0ABqOkAcBglDQAGIySBgCDGVPSX3zxhfr27atWrVopISFBy5YtszsSANgu0O4AkvTTTz9p7NixmjZtmrp27ardu3crMTFR1113nTp27Gh3PACwjREr6WPHjqlHjx5KSEiQ0+lU8+bNFRcXpx07dtgdDQBsZcRKOjY2VrGxsZ7vz5w5o23btql37942pgIA+zksy7LsDnG+7OxsPfLII6pWrZrmzp0rp9OIxT4A2MKIlfQvDh8+rDFjxqhx48aaPn36ZRV0RkaO3G6jHm+uWHR0pNLTs+2OYTzmyXfMlW/smqfo6MiLXmbMMnXr1q0aMGCA4uPjNXPmTIWEhNgdCQBsZ8RK+ocfftCjjz6q8ePHa8iQIXbHAQBjGLGSXrJkiXJzczVjxgzFxMR4vl5++WW7owGArYx74vBKsSdd9TBPvmOufMOeNADgslDSAGAwShoADEZJA4DBKGkAMBglDQAGo6QBwGCUNAAYjJIGAINR0gBgMEoaAAxGSQOAwShpADAYJQ0ABqOkAcBglDQAGIySBgCDUdIAYDBKGgAMRkkDgMEoaQAwGCUNAAajpAHAYJQ0ABiMkgYAg1HSAGAwShoADEZJA4DBjCnpvXv36oEHHlDLli3Vs2dPpaam2h0JAGxnREm7XC6NGTNG99xzj7Zu3apRo0ZpxIgRysnJsTsaANjKiJL++uuvVVhYqOHDhysoKEjdu3dX48aN9dFHH9kdDQBsFWh3AEk6cOCAGjVq5DXWsGFDpaWlldttjvhioOff59+xrNxuB1VL8aYNcr+7SM5BQxTQNcHuOKgEjCjpvLw8hYaGeo2FhYUpPz/f52PUrBlxxbcfHR15xdctLyZmMpFp85S+/F1ZR39QwPKlih7Y97Kvf+bFF5W3aImC7mgnnclUROIIhd1zjyQp/+OPlfOP+V5jvyjtsl+YNlemMm2ejCjp8PBwFRQUeI3l5+crPDzc52NkZOTI7bau6PbT07Ov6HrlJTo60rhMJjJxnorvHyTr3UUqvv/BK8rmemexlJsr1/qNUlRNnZ47TzmxHSRJhXPnyTpyxGvsy4MZWr3rJ937z/W6/cAhnZ47T5mZ+frXex/rw7qt5HIE6FCN6xX7/TfqfPDferd1H+UFh6rAGaSCkDDJ7VZRYJAkh6rn/qzCoDBVyzujE9V/IzkcCixyyXI4FV54VpYsuQJDdO+x7Rp+T4tK+ZeCXf9PlfbAYERJN2rUSAsXLvQaO3TokPr06VNut8kWB8pDQNeEqyovR78Bsla+L8XGyZGdJeegIZ7LnIOGeLZSfrF61086diZfa29LUNus7z0/82Gdu/RTWJROh1eXJG2r31I5YRH6qXptFTsDfr3B856VyrwmSg7LUt61dTxjRYHBkqTsgF//Uv2gXjsdX7tDOZ/+qB67N+r2Izuv+P56hIcr4IX/WymL/2o5LMu6suVnGXK5XEpISNDvfvc7DR48WOvXr9ezzz6rjRs3KioqyqdjXM1K2jQmrhBNxDz9upLu1aKO2jWqKencvvjFVtJL/3clffYKV9I5oedWfE7Lrer52aqTdVIvrH3Zxhm4iM53KfiV2Zd9NRNX0kaUtCSlpaXpueee0969e3X99dfr6aefVrt27Xy+PiVd9TBPvittroo3bVDxazMlSY6OnWR9vFY6dfKCP7sotq/W39JZtx3bo5ywiLJbSZcDx7CHpd2psiKrSVs+lxwOOQYNkbN5C8/9Dfifx71W7+fPkz+fBK4QJX21KOmqh3nynR1z5Sn/3FzJsqSCAinzjH9uPDBICg2RQsOknzMkt/vceESEHDc3kbXrG0mSo0VLBc1/x3O18+epcMRQWUeOyFGvntfPlAfj96QBVD5Xuz9/pX5ZAeu25udW0rc1+3Ul3W/AuZX06dOS5LW//98u9ByAHVhJG4gVom+YJ98xV74xcU/aiFccAgAujJIGAINR0gBgMEoaAAxGSQOAwShpADAYJQ0ABqOkAcBglDQAGIySBgCDVZr37nA6HXZHKFOV7f6UF+bJd8yVb0ybp0rz3h0AUBmx3QEABqOkAcBglDQAGIySBgCDUdIAYDBKGgAMRkkDgMEoaQAwGCUNAAajpA319ttvq2vXrmrdurX69++vbdu22R3JGHv37tUDDzygli1bqmfPnkpNTbU7krG++OIL9e3bV61atVJCQoKWLVtmdySjZWVlqXPnzkpKSrI7yq8sGGfdunVWhw4drP3791vFxcXWihUrrFatWlkZGRl2R7NdQUGB1aVLF+utt96yXC6X9eGHH1qxsbFWdna23dGMc/z4cSsmJsZav369VVxcbO3atctq06aN9fnnn9sdzVhPPPGE1aRJE2vlypV2R/FgJW2g9PR0jRo1So0bN5bT6VS/fv0UEBCgffv22R3Ndl9//bUKCws1fPhwBQUFqXv37mrcuLE++ugju6MZ59ixY+rRo4cSEhLkdDrVvHlzxcXFaceOHXZHM1JycrJycnJ000032R3FS6V5F7yKxuVyKTMzs8S4w+HQ4MGDvca2bt2qvLw83Xjjjf6KZ6wDBw6oUaNGXmMNGzZUWlqaTYnMFRsbq9jYWM/3Z86c0bZt29S7d28bU5np6NGjmj17tpYtW6bExES743ihpG2yc+dODR06tMR4QECAvvvuO8/3aWlpGj9+vMaNG6datWr5M6KR8vLyFBoa6jUWFham/Px8mxJVDNnZ2Ro9erRatGihrl272h3HKMXFxZo4caImTZqk6Ohou+OUQEnb5Pbbb7/k9kVKSoqefPJJjRw5UiNHjvRTMrOFh4eroKDAayw/P1/h4eE2JTLf4cOHNWbMGDVu3FjTp0+X08ku5/nmzJmjBg0aqFu3bnZHuSD+axnq7bff1oQJE/SXv/xFjzzyiN1xjNGoUSMdPnzYa+zQoUNq3LixTYnMtnXrVg0YMEDx8fGaOXOmQkJC7I5knLVr12rdunWe7aG0tDQ9//zzmjJlit3RzrH7mUuUtHbtWqtFixbWN998Y3cU4xQUFFh33nmn19kdMTExnPlyAUeOHLFiYmKsd955x+4oFUqvXr04uwOle/PNN+VyuTR8+HDFxMR4vlJSUuyOZrvg4GC9+eabWrduneLi4vT666/rtddeU1RUlN3RjLNkyRLl5uZqxowZXv8fvfzyy3ZHw2Xg47MAwGCspAHAYJQ0ABiMkgYAg1HSAGAwShoADEZJA4DBKGlUanfddZduvvlmz1fTpk3VpUsXTZs2TXl5eZ6f++677/T444+rffv2iomJUb9+/bRmzZoLHvPrr7/WzTffrD/96U8XvPzgwYMaMmSIWrZsqd/+9rdav359udw3VA2UNCq9CRMmaMuWLdqyZYtSUlI0depUrV69Wi+++KIkafPmzXrwwQdVp04d/eMf/1BycrJ69uypyZMna/78+SWOt3r1atWvX19r167V2bNnvS4rKChQYmKiGjRooJUrV2rAgAGaMGGC9uzZ45f7ikrI7pc8AuWpS5cu1qJFi0qMv/7661br1q2tnJwcq23bttasWbNK/Mwbb7xhNW/e3Osl5wUFBVZsbKyVlJRkNWvWzEpOTva6TnJystW+fXursLDQM/bYY49ZkydPLrs7hSqFlTSqpICAAAUHByslJUXZ2dkaPnx4iZ8ZNGiQFixYoGrVqnnGUlJSlJOTo06dOumOO+7QypUrva6zY8cOtWzZUoGBv77BZJs2bbR9+/Zyuy+o3ChpVClut1upqalavHix4uPjtWfPHjVo0EARERElfjYiIkKtW7f2KtzVq1erVatWioqKUkJCgrZu3aqjR496Lj916pRq167tdZzo6GidPHmy/O4UKjVKGpXeX//6V8+bCzVr1kyDBg1S8+bN9Yc//EFZWVkXLOgLyczM1ObNm5WQkCDp3JOSAQEBXqvp/Px8BQcHe10vODhYLper7O4QqhTe9B+V3qOPPqpevXpJkoKCglSrVi1PkdaoUUNZWVk+Hefjjz9WYWGh583hr732WsXFxSk5OVmPP/64nE6nQkNDSxSyy+Uq8WkygK9YSaPSq1GjhurVq6d69eqpbt26Xivd5s2b6/Dhw8rJySlxvezsbA0ZMkTffvutpHNbHZIUHx+vpk2bqmnTpvryyy914sQJbdmyRZJUu3Ztpaenex0nPT29xBYI4CtKGlVahw4dFBUVpbfeeqvEZcuWLdPOnTt13XXX6dixY9qxY4fGjh2rDz74wPOVlJSka665xrPl0apVK+3cuVNFRUWe42zdulUxMTF+u0+oXNjuQJUWGhqqKVOm6IknnlBubq7uu+8+BQYGav369Xrttdc0ceJERUVFae7cuQoJCdHQoUO9zvaQpPvuu0/vvfeeTp8+rW7dumnGjBn64x//qMTERP3zn//UZ599puXLl9t0D1HRsZJGlRcfH6/58+dr//79GjZsmPr3769PP/1UL730koYNGyZJWrNmje69994SBS1JgwcPVlFRkdasWaPw8HDNmzdP33//ve677z69//77euWVV9SkSRN/3y1UEnwyCwAYjJU0ABiMkgYAg1HSAGAwShoADEZJA4DBKGkAMBglDQAGo6QBwGCUNAAY7P8DWRfRoqdTihYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "LR tn, fp: 284, 4\n",
      "LR fn, tp: 5, 3\n",
      "LR f1 score: 0.400\n",
      "LR cohens kappa score: 0.384\n",
      "LR average precision score: 0.511\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 284, 4\n",
      "GB fn, tp: 3, 5\n",
      "GB f1 score: 0.588\n",
      "GB cohens kappa score: 0.576\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 286, 2\n",
      "KNN fn, tp: 4, 4\n",
      "KNN f1 score: 0.571\n",
      "KNN cohens kappa score: 0.561\n",
      "\n",
      "### Exercise is done.\n",
      "\n",
      "-----[ LR ]-----\n",
      "maximum:\n",
      "LR tn, fp: 288, 7\n",
      "LR fn, tp: 7, 7\n",
      "LR f1 score: 0.737\n",
      "LR cohens kappa score: 0.728\n",
      "LR average precision score: 0.900\n",
      "\n",
      "\n",
      "average:\n",
      "LR tn, fp: 284.52, 3.48\n",
      "LR fn, tp: 4.4, 4.4\n",
      "LR f1 score: 0.519\n",
      "LR cohens kappa score: 0.505\n",
      "LR average precision score: 0.670\n",
      "\n",
      "\n",
      "minimum:\n",
      "LR tn, fp: 281, 0\n",
      "LR fn, tp: 2, 2\n",
      "LR f1 score: 0.222\n",
      "LR cohens kappa score: 0.198\n",
      "LR average precision score: 0.358\n",
      "\n",
      "\n",
      "-----[ GB ]-----\n",
      "maximum:\n",
      "GB tn, fp: 288, 4\n",
      "GB fn, tp: 7, 9\n",
      "GB f1 score: 0.842\n",
      "GB cohens kappa score: 0.837\n",
      "\n",
      "\n",
      "average:\n",
      "GB tn, fp: 285.96, 2.04\n",
      "GB fn, tp: 3.0, 5.8\n",
      "GB f1 score: 0.687\n",
      "GB cohens kappa score: 0.679\n",
      "\n",
      "\n",
      "minimum:\n",
      "GB tn, fp: 284, 0\n",
      "GB fn, tp: 0, 1\n",
      "GB f1 score: 0.182\n",
      "GB cohens kappa score: 0.170\n",
      "\n",
      "\n",
      "-----[ KNN ]-----\n",
      "maximum:\n",
      "KNN tn, fp: 288, 5\n",
      "KNN fn, tp: 8, 7\n",
      "KNN f1 score: 0.875\n",
      "KNN cohens kappa score: 0.872\n",
      "\n",
      "\n",
      "average:\n",
      "KNN tn, fp: 286.72, 1.28\n",
      "KNN fn, tp: 4.56, 4.24\n",
      "KNN f1 score: 0.580\n",
      "KNN cohens kappa score: 0.571\n",
      "\n",
      "\n",
      "minimum:\n",
      "KNN tn, fp: 283, 0\n",
      "KNN fn, tp: 2, 1\n",
      "KNN f1 score: 0.200\n",
      "KNN cohens kappa score: 0.195\n",
      "\n"
     ]
    }
   ],
   "source": [
    "exercise = Exercise(shuffleFunction=shuffler, numOfShuffles=5, numOfSlices=5)\n",
    "exercise.run(gan, data)\n",
    "exercise.saveResultsTo(f\"{datasetName}-{ganName}.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b6bac36",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}