{
 "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., 1., 0., 0., 0., 0., 0., 0., 1., 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.])), (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",
      "       [0.38, 0.4 , 0.39, ..., 0.  , 0.46, 0.62],\n",
      "       [0.58, 0.56, 0.38, ..., 0.  , 0.54, 0.57],\n",
      "       [0.67, 0.57, 0.36, ..., 0.  , 0.56, 0.22]]), array([0., 0., 0., ..., 0., 0., 1.]), array([[0.66, 0.55, 0.45, ..., 0.  , 0.46, 0.22],\n",
      "       [0.58, 0.47, 0.54, ..., 0.  , 0.51, 0.26],\n",
      "       [0.45, 0.4 , 0.5 , ..., 0.  , 0.5 , 0.22],\n",
      "       ...,\n",
      "       [0.28, 0.48, 0.36, ..., 0.  , 0.53, 0.22],\n",
      "       [0.49, 0.56, 0.37, ..., 0.  , 0.53, 0.22],\n",
      "       [0.47, 0.5 , 0.6 , ..., 0.  , 0.38, 0.16]]), array([0., 0., 0., 0., 0., 0., 0., 0., 1., 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., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       1., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 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., 1., 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., 1., 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., 1., 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., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0.]))], 'db_size': 1484, 'db_n_attr': 10, 'imbalanced_ratio': 28.098039215686274}\n"
     ]
    }
   ],
   "source": [
    "datasetName = \"folding_yeast4\"\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",
       "        [0.38, 0.4 , 0.39, ..., 0.  , 0.46, 0.62],\n",
       "        [0.58, 0.56, 0.38, ..., 0.  , 0.54, 0.57],\n",
       "        [0.67, 0.57, 0.36, ..., 0.  , 0.56, 0.22]]),\n",
       " array([0., 0., 0., ..., 0., 0., 1.]),\n",
       " array([[0.4 , 0.39, 0.6 , ..., 0.  , 0.58, 0.3 ],\n",
       "        [0.58, 0.47, 0.54, ..., 0.  , 0.51, 0.26],\n",
       "        [0.45, 0.4 , 0.5 , ..., 0.  , 0.5 , 0.22],\n",
       "        ...,\n",
       "        [0.28, 0.48, 0.36, ..., 0.  , 0.53, 0.22],\n",
       "        [0.49, 0.56, 0.37, ..., 0.  , 0.53, 0.22],\n",
       "        [0.51, 0.59, 0.46, ..., 0.  , 0.48, 0.22]]),\n",
       " array([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",
       "        1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0., 1., 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., 0., 0., 0., 1., 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., 1., 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., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 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., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 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",
       "       [0.28, 0.48, 0.36, ..., 0.  , 0.53, 0.22],\n",
       "       [0.49, 0.56, 0.37, ..., 0.  , 0.53, 0.22],\n",
       "       [0.51, 0.59, 0.46, ..., 0.  , 0.48, 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 1106 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, 2\n",
      "LR fn, tp: 8, 3\n",
      "LR f1 score: 0.375\n",
      "LR cohens kappa score: 0.360\n",
      "LR average precision score: 0.428\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 284, 3\n",
      "GB fn, tp: 10, 1\n",
      "GB f1 score: 0.133\n",
      "GB cohens kappa score: 0.116\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 287, 0\n",
      "KNN fn, tp: 10, 1\n",
      "KNN f1 score: 0.167\n",
      "KNN cohens kappa score: 0.162\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 1106 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, 1\n",
      "LR fn, tp: 7, 4\n",
      "LR f1 score: 0.500\n",
      "LR cohens kappa score: 0.488\n",
      "LR average precision score: 0.656\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 283, 4\n",
      "GB fn, tp: 5, 6\n",
      "GB f1 score: 0.571\n",
      "GB cohens kappa score: 0.556\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 287, 0\n",
      "KNN fn, tp: 11, 0\n",
      "KNN f1 score: 0.000\n",
      "KNN cohens kappa score: 0.000\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 1106 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, 2\n",
      "LR fn, tp: 10, 1\n",
      "LR f1 score: 0.143\n",
      "LR cohens kappa score: 0.129\n",
      "LR average precision score: 0.390\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 286, 1\n",
      "GB fn, tp: 9, 2\n",
      "GB f1 score: 0.286\n",
      "GB cohens kappa score: 0.274\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 287, 0\n",
      "KNN fn, tp: 11, 0\n",
      "KNN f1 score: 0.000\n",
      "KNN cohens kappa score: 0.000\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 1106 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, 3\n",
      "LR fn, tp: 9, 2\n",
      "LR f1 score: 0.250\n",
      "LR cohens kappa score: 0.232\n",
      "LR average precision score: 0.213\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 282, 5\n",
      "GB fn, tp: 8, 3\n",
      "GB f1 score: 0.316\n",
      "GB cohens kappa score: 0.294\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 286, 1\n",
      "KNN fn, tp: 11, 0\n",
      "KNN f1 score: 0.000\n",
      "KNN cohens kappa score: -0.006\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 1104 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, 1\n",
      "LR fn, tp: 6, 1\n",
      "LR f1 score: 0.222\n",
      "LR cohens kappa score: 0.214\n",
      "LR average precision score: 0.493\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 284, 1\n",
      "GB fn, tp: 5, 2\n",
      "GB f1 score: 0.400\n",
      "GB cohens kappa score: 0.391\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 284, 1\n",
      "KNN fn, tp: 7, 0\n",
      "KNN f1 score: 0.000\n",
      "KNN cohens kappa score: -0.006\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 1106 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, 3\n",
      "LR fn, tp: 10, 1\n",
      "LR f1 score: 0.133\n",
      "LR cohens kappa score: 0.116\n",
      "LR average precision score: 0.231\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 284, 3\n",
      "GB fn, tp: 9, 2\n",
      "GB f1 score: 0.250\n",
      "GB cohens kappa score: 0.232\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 287, 0\n",
      "KNN fn, tp: 11, 0\n",
      "KNN f1 score: 0.000\n",
      "KNN cohens kappa score: 0.000\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 1106 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, 3\n",
      "LR fn, tp: 5, 6\n",
      "LR f1 score: 0.600\n",
      "LR cohens kappa score: 0.586\n",
      "LR average precision score: 0.473\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 281, 6\n",
      "GB fn, tp: 4, 7\n",
      "GB f1 score: 0.583\n",
      "GB cohens kappa score: 0.566\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 286, 1\n",
      "KNN fn, tp: 9, 2\n",
      "KNN f1 score: 0.286\n",
      "KNN cohens kappa score: 0.274\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 1106 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, 2\n",
      "LR fn, tp: 9, 2\n",
      "LR f1 score: 0.267\n",
      "LR cohens kappa score: 0.252\n",
      "LR average precision score: 0.402\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 286, 1\n",
      "GB fn, tp: 8, 3\n",
      "GB f1 score: 0.400\n",
      "GB cohens kappa score: 0.388\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 286, 1\n",
      "KNN fn, tp: 11, 0\n",
      "KNN f1 score: 0.000\n",
      "KNN cohens kappa score: -0.006\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 1106 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, 1\n",
      "LR fn, tp: 10, 1\n",
      "LR f1 score: 0.154\n",
      "LR cohens kappa score: 0.144\n",
      "LR average precision score: 0.388\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 285, 2\n",
      "GB fn, tp: 6, 5\n",
      "GB f1 score: 0.556\n",
      "GB cohens kappa score: 0.542\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 287, 0\n",
      "KNN fn, tp: 11, 0\n",
      "KNN f1 score: 0.000\n",
      "KNN cohens kappa score: 0.000\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 1104 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, 1\n",
      "LR fn, tp: 6, 1\n",
      "LR f1 score: 0.222\n",
      "LR cohens kappa score: 0.214\n",
      "LR average precision score: 0.505\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 283, 2\n",
      "GB fn, tp: 5, 2\n",
      "GB f1 score: 0.364\n",
      "GB cohens kappa score: 0.352\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 285, 0\n",
      "KNN fn, tp: 7, 0\n",
      "KNN f1 score: 0.000\n",
      "KNN cohens kappa score: 0.000\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 1106 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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tXbpUK1asOK8CAQDV8+nNLKdOnZLX663UtmvXLoWHh2vixIlauXJlnRQHAI3dOUN6zpw56tevn3bs2FGpffr06UpOTj7vt4QDAM6uxpBesWKFnn/+eU2aNElXXXVVpW2vvPKKJk+erOnTp2vNmjV1WiQANFY1rknPnTtXDz30kO66664q2yIjI3XPPfeovLxcc+bM0aBBg+qsSABorGqcSe/fv1/9+vWrsYOBAwdq3759F7QoAMAZNYZ0eHi4iouLa+zA4/EoNDT0ghYFADijxpBOTEzU0qVLa+wgIyND7du3v6BFAQDOqHFNevz48Ro1apSioqI0duxYRUREVGwrKirSG2+8ofnz52vOnDl1XigANEY1hnT79u31wgsv6NFHH9WsWbPUqlUrRUVFKT8/X9nZ2YqJidGf//xndevWzV/1AkCjcs53HCYnJ2vdunXKzMzU7t27lZ+fr+joaHXq1Em9evViPRoA6lCNIV1SUqJp06ZpzZo1CgkJUf/+/fXwww8bcUMlAGgMagzpmTNnKjMzU2lpaXI4HFqwYIFOnz6tv/zlL34qDwAatxpDes2aNZoxY4auueYaSVJSUpJGjx4tt9ut4OBgvxQIAI1ZjZfgHTt2TK1bt6543LFjR1mWpby8vDovDABwjpD2eDwKDAyseBwQEKDg4GC53e46LwwA4OOtSgEA9jjnJXjp6emV3sTi8Xi0dOnSKh9KO3r06AtfHQA0cjWGdPPmzbVw4cJKbZdcconS09MrtQUEBBDSAFAHagzpDz/80F91AACqwZo0ABjMmJA+fvy4Jk2apISEBPXs2ZM3zACADArpSZMmyel06l//+pfefvttvffee1q+fLndZQGArc55dYc/7NixQ4cOHdLChQsVHBysK664QvPmzePmTQAaPSNm0jt37lTbtm310ksvqXfv3kpJSdEHH3ygH/3oR3aXBgC2MmImffr0aW3ZskVJSUlav3699u/fr7S0NDmdTt18880+9dGsWcO7M5/TGWV3CfUK4+U7xqp27BwvI0I6JCREkZGRmjx5siSpXbt2uv322/XBBx/4HNJ5eYXyeq26LNOvnM4o5eYW2F1GvcF4+Y6xqh1/jFdNLwJGLHe0bt1aJSUlcrlcFW0ej8fGigDADEaE9LXXXquYmBhNnz5dLpdLn3/+uRYvXqzBgwfbXRoA2MqIkA4NDdX8+fN16NAh9e7dW2lpaUpLS9OgQYPsLg0AbGXEmrQkXXHFFXrttdfsLgMAjGLETBoAUD1CGgAMRkgDgMEIaQAwGCENAAYjpAHAYIQ0ABiMkAYAgxHSAGAwQhoADEZIA4DBCGkAMBghDQAGI6QBwGCENAAYjJAGAIMR0gBgMEIaAAxGSAOAwQhpADAYIQ0ABiOkAcBghDQAGIyQBgCDEdIAYDBCGgAMZlxI5+fnq2/fvkpPT7e7FACwnXEh/cQTT+jYsWN2lwEARjAqpDMyMlRYWKi2bdvaXQoAGMGYkD506JBeeuklTZs2ze5SAMAYRoS0x+PRQw89pEceeUROp9PucgDAGEF2FyBJr7zyilq1aqWBAwf+4D6aNYu8gBWZwemMsruEeoXx8h1jVTt2jleAZVmWbUf/n+uvv17Hjx+Xw3FmYl9UVKSQkBANGzZMTz75pE995OUVyuu1/VQuGKczSrm5BXaXUW8wXr5jrGrHH+NV04uAETPp1atXV3o8dOhQ3XXXXbr11lttqggAzGDEmjQAoHpGzKS/b+nSpXaXAABGYCYNAAYjpAHAYIQ0ABiMkAYAgxHSAGAwQhoADEZIA4DBCGkAMBghDQAGI6QBwGCENAAYjJAGAIMR0gBgMEIaAAxGSAOAwQhpADAYIQ0ABiOkAcBghDQAGIyQBgCDEdIAYDBCGgAMRkgDgMEIaQAwGCENAAYjpAHAYIQ0ABjMmJD++OOPdeutt6pr164aMGCAFi1aZHdJAGC7ILsLkKQjR45o8uTJmj59uvr376+dO3cqLS1Nl19+uXr37m13eQBgGyNm0ocPH9ZNN92kAQMGyOFwqFOnTkpKStLWrVvtLg0AbGXETDoxMVGJiYkVj0+dOqXNmzdr6NChNlYFAPYLsCzLsruI7yooKNC4cePUpEkTzZo1Sw6HEZN9ALCFETPpb2RnZ2vSpEmKi4vTjBkzahXQeXmF8nqNer05L05nlHJzC+wuo95gvHzHWNWOP8bL6Yw66zZjpqmbNm3SiBEjlJKSopkzZyo0NNTukgDAdkbMpL/66iuNHz9eDz74oFJTU+0uBwCMYcRMesGCBSoqKtLzzz+v+Pj4iq/nnnvO7tIAwFbG/eHwh2JNunFjvHzHWNUOa9IAgLMipAHAYIQ0ABiMkAYAgxHSAGAwQhoADEZIA4DBCGkAMBghDQAGI6QBwGCENAAYjJAGAIMR0gBgMEIaAAxGSAOAwQhpADAYIQ0ABiOkAcBghDQAGIyQBgCDEdIAYDBCGgAMRkgDgMEIaQAwGCENAAYjpAHAYMaE9J49e3THHXeoS5cuuvnmm5WVlWV3SQBgOyNC2uVyadKkSbrhhhu0adMmTZgwQXfffbcKCwvtLg0AbGVESH/66adyu90aO3asgoODNXjwYMXFxen999+3uzQAsFWQ3QVI0r59+9SmTZtKba1bt9bevXvr7Jh3fzzyrNvCAsLllUcRQZEa1WasEpol1VkdAFATI0K6uLhYYWFhldrCw8NVUlJiSz2l1pnjutwn9PrnL+pf0Z31+eldSr40Ra2jrtS6nFVKaX6DJGnhl3NVWF6o/s0HSZLW56xWSGComgQ31bAWdyihWZLePbBAG46u01VNf6ri8iK1jorTv3M/VqG7QP2bX6/hLUdrS96neu/g27IkdY7pql07tsvj8apTTFftL9inlOY38GIBNEJGhHRERITKysoqtZWUlCgiIsLnPpo1i7xw9TgiVOItkSVLHsujrBNbZcnSR8fW61BptnLLjuqj3LWyJJ10n5AkfXRsvSTJbbnlLner1FOij3LX6vp2/fXRv9er1FOqrBNb1TS0qb4uPqji8uKK503qNkEb9qzVsdKjFW1lnrKKf4cGhlb09X1zd76hFfuXqUlIE93Tabx6NO9ZafsnOf/S/N3zJMvSz9qPUY/mPTV35xtafeB9Xd/yRo3t8PNK+y77cqmGtBla0c8nOf/S/M/ekgIC9LOrU6v0bxKnM8ruEiRJJatWqfBvsxWZdrfCb7jB5/1KVq3Sqd89Ie+Jk7roF2MV2rWrCv82W9tuvVsZZc10xzU/Ud/2l1bbR3DXeLm3bjvrMb97LN1wgzFjVV/YOV5GhHSbNm00d+7cSm379+/XLbfc4nMfeXmF8notn/effe2iGrefmdm+I0uWYsMv0+end6lPbP8zM2n3KvVxDpQk5eTnqLC8UH1izwTod2fSfZwDlZtboD6x/c86k+4T21+5uQVKdg7UyeJTsmSdmUmf3i6Px1KnmHjtL9hX0df3rcpeKZfXpf+W/ldL9qQrLrhjpe1L9qQrp+Bwxb/jgjtqVfZKlXpKtSp7pQbH3l5p32MlRyr1s2RPunIKcyo9v7qxWpezSq2j4rS/YJ9aR8X974XtzG8F3/9NYEvep8o4+LZKy0sUHhSuTjFdtePEVuWWHFO5yiv6DQsI18s936jx+/QNpzOq2vGxg3vWa7IOHtTJWa+pMLGXz/u5Z70mK+eIJKnorfkq/vdmWQcPatGWozoSG6r5/8zWT50XVduHa+cuKSxcJx77rf79zN+0ov11anvqsPY6W6rt8f3a7GwrNb9Bo56ape5p4/TvFvH6e8JQSQFK/GqH9sa2UdtjX2rzTzrpvxfFqDQkXPJ6dVFZkUI9bkW4S5X41Q5t/klnFQeHyh0UqrKgEP0k75AOOFvIsqSbd36gnCax2tyyi1ofz1ZpSJhOhTeRKyhUN+5ar9TN6dWOw5labpFkadSWpep+cNuZDQ6HAlLHKvjBX12A78oP54+frZpeBIwI6e7du8uyLM2dO1ejR4/W2rVr9fnnn2vAgAG21ZTQLOmsywvfbf/+PsNbjq6y//CWo6u0f//x9483yTnBpx+M5EtTtD5ntSKDoyqWYL4rpfkNKnQXyJJVsT350hRtOLpOyZemVNn3u0s537QVuPMVoIBq+5ekdTmrdKzkiA4VHVCII1SHig7I5XFJkjYcXacQR6jW5ayqOL91Oat0vOSoPPIo3x2oDUfXyeVxySNPpX6/WXaqbxyjUuX9+zw5RqXWaj/HqFR5vv5aOnVSAbeNkKNTZ3n/Pk9D2sVohRWuIZ0vO2sf6tBJ2pkla9dOreiWrCMXXaIDTZsr1OPSgZbdVRYUIkla0SFF3Q9u04oOKTrS9MysfO3VfRVa7tKBmCtUFhQijyPwf507VBQepSJJp70erb26b+XtkvbFfvu3pLVX91VpcKi8AQ7ti22jQK+nYt+1V/c9a0ifqSW2Un3/bhGvFR1SdNM/1qn7m+0lSfMSb9Xaq/uqw+HdKgyP1E07130b6P+rV0FBksslBQdLliWFhEgej9TjWumzXdLJE1J0jAIfeUyS5Hl5piQp8P/dp8D+1edNyapVcs96TY5RqWfdpy4FWJbl+/SzDu3du1dPPPGE9uzZox//+Md67LHH1KNHD5+fX9uZtOlMmhmeS/Uz6W0VvxXUPJOOUKeY+AY1k7aT+88z9H8f76wyk97ibCtLlkbtWKnuX26qmEkHKEAJ55hJh/1vJp3w1Q5t+UlnFdXBTHphwi1n6vvfTPq3gx/SkSaxuiz/mJ5a+ZwkKTV1pkqCwxQgS01LCiptOyeHQ/J6Kx4GJCRKkqwd28887txFwbPfqv65E34u1779CmjR4uz7nKeaZtLGhPT5IqQbN8bLd6aOleuBe6V/fChJ386kvzNbtmsmHbl5o07W8UyakK6HTP2PZCrGy3eMVe3YvSZtxJtZAADVI6QBwGCENAAYjJAGAIMR0gBgMEIaAAxGSAOAwQhpADAYIQ0ABiOkAcBgRtwF70JwOALsLuGCa4jnVJcYL98xVrVj53g1mHt3AEBDxHIHABiMkAYAgxHSAGAwQhoADEZIA4DBCGkAMBghDQAGI6QBwGCENAAYjJA22Jtvvqn+/fsrISFBt99+uzZv3mx3SUbZs2eP7rjjDnXp0kU333yzsrKy7C7JaB9//LFuvfVWde3aVQMGDNCiRYvsLsl4+fn56tu3r9LT0+0rwoKR1qxZY/Xq1cv64osvLI/HYy1evNjq2rWrlZeXZ3dpRigrK7P69etnvfHGG5bL5bJWrFhhJSYmWgUFBXaXZqScnBwrPj7eWrt2reXxeKwdO3ZY3bp1sz766CO7SzPaAw88YLVr185asmSJbTUwkzZUbm6uJkyYoLi4ODkcDt12220KDAzU559/bndpRvj000/ldrs1duxYBQcHa/DgwYqLi9P7779vd2lGOnz4sG666SYNGDBADodDnTp1UlJSkrZu3Wp3acbKyMhQYWGh2rZta2sdDeYuePWRy+XS6dOnq7QHBARo9OjRldo2bdqk4uJiXXnllf4qz2j79u1TmzZtKrW1bt1ae/futakisyUmJioxMbHi8alTp7R582YNHTrUxqrMdejQIb300ktatGiR0tLSbK2FkLbRtm3bNGbMmCrtgYGB+uyzzyoe7927Vw8++KDuv/9+XXLJJf4s0VjFxcUKCwur1BYeHq6SkhKbKqo/CgoKNHHiRHXu3Fn9+/e3uxzjeDwePfTQQ3rkkUfkdDrtLoeQtlP37t3PuXyRmZmphx9+WPfcc4/uueceP1VmvoiICJWVlVVqKykpUUREhE0V1Q/Z2dmaNGmS4uLiNGPGDDkcrHh+3yuvvKJWrVpp4MCBdpciias7jPbmm29qypQpevrppzVu3Di7yzFKmzZtlJ2dXalt//79iouLs6ki823atEkjRoxQSkqKZs6cqdDQULtLMtLKlSu1Zs2aiiWivXv36ve//72efPJJewqy7U+WqNHKlSutzp07W9u3b7e7FCOVlZVZffr0qXR1R3x8PFe/nMXBgwet+Ph466233rK7lHpnyJAhXN2Bql5//XW5XC6NHTtW8fHxFV+ZmZl2l2aEkJAQvf7661qzZo2SkpL017/+VS+//LJiYmLsLs1ICxYsUFFRkZ5//vlKP0/PPfec3aXhHPj4LAAwGDNpADAYIQ0ABiOkAcBghDQAGIyQBgCDEdIAYDBCGg3addddp6uuuqriq3379urXr5+mT5+u4uLiiv0+++wz3XffferZs6fi4+N12223afny5dX2+emnn+qqq67S7373u2q3f/nll0pNTVWXLl10/fXXa+3atXVybmgcCGk0eFOmTNHGjRu1ceNGZWZmatq0aVq2bJmmTp0qSdqwYYPuvPNOXXbZZfrb3/6mjIwM3XzzzXr00Uc1e/bsKv0tW7ZMLVu21MqVK1VaWlppW1lZmdLS0tSqVSstWbJEI0aM0JQpU7R7926/nCsaINve6wj4Qb9+/ax58+ZVaf/rX/9qJSQkWIWFhdY111xjvfjii1X2efXVV61OnTpVeqt5WVmZlZiYaKWnp1sdO3a0MjIyKj0nIyPD6tmzp+V2uyva7r33XuvRRx+9cCeFRoWZNBqlwMBAhYSEKDMzUwUFBRo7dmyVfUaNGqU5c+aoSZMmFW2ZmZkqLCxUcnKyrr32Wi1ZsqTSc7Zu3aouXbooKOjbG0x269ZNW7ZsqbNzQcNGSKNR8Xq9ysrK0vz585WSkqLdu3erVatWioyMrLJvZGSkEhISKgXusmXL1LVrV8XExGjAgAHatGmTDh06VLH9+PHjio2NrdSP0+nUsWPH6u6k0KAR0mjwnnnmmYobCnXs2FGjRo1Sp06d9Ktf/Ur5+fnVBnR1Tp8+rQ0bNmjAgAGSzvxRMjAwsNJsuqSkRCEhIZWeFxISIpfLdeFOCI0KN/1Hgzd+/HgNGTJEkhQcHKxLLrmkIkijo6OVn5/vUz+rVq2S2+2uuBn8xRdfrKSkJGVkZOi+++6Tw+FQWFhYlUB2uVxVPkUG8BUzaTR40dHRatGihVq0aKHmzZtXmul26tRJ2dnZKiwsrPK8goICpaam6j//+Y+kM0sdkpSSkqL27durffv2+uSTT3T06FFt3LhRkhQbG6vc3NxK/eTm5lZZAgF8RUijUevVq5diYmL0xhtvVNm2aNEibdu2TZdffrkOHz6srVu3avLkyXrvvfcqvtLT03XRRRdVLHl07dpV27ZtU3l5eUU/mzZtUnx8vN/OCQ0Lyx1o1MLCwvTkk0/qgQceUFFRkYYNG6agoCCtXbtWL7/8sh566CHFxMRo1qxZCg0N1ZgxYypd7SFJw4YN09tvv62TJ09q4MCBev755/Wb3/xGaWlp+uc//6l//OMfevfdd206Q9R3zKTR6KWkpGj27Nn64osvdNddd+n222/Xhx9+qGeffVZ33XWXJGn58uW68cYbqwS0JI0ePVrl5eVavny5IiIi9Nprr+nAgQMaNmyY3nnnHf3lL39Ru3bt/H1aaCD4ZBYAMBgzaQAwGCENAAYjpAHAYIQ0ABiMkAYAgxHSAGAwQhoADEZIA4DBCGkAMNj/B53qkBLu5Eo1AAAAAElFTkSuQmCC\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, 2\n",
      "LR fn, tp: 8, 3\n",
      "LR f1 score: 0.375\n",
      "LR cohens kappa score: 0.360\n",
      "LR average precision score: 0.408\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 284, 3\n",
      "GB fn, tp: 9, 2\n",
      "GB f1 score: 0.250\n",
      "GB cohens kappa score: 0.232\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 286, 1\n",
      "KNN fn, tp: 11, 0\n",
      "KNN f1 score: 0.000\n",
      "KNN cohens kappa score: -0.006\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 1106 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, 3\n",
      "LR fn, tp: 11, 0\n",
      "LR f1 score: 0.000\n",
      "LR cohens kappa score: -0.016\n",
      "LR average precision score: 0.382\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 283, 4\n",
      "GB fn, tp: 10, 1\n",
      "GB f1 score: 0.125\n",
      "GB cohens kappa score: 0.104\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 287, 0\n",
      "KNN fn, tp: 11, 0\n",
      "KNN f1 score: 0.000\n",
      "KNN cohens kappa score: 0.000\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 1106 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, 4\n",
      "LR fn, tp: 10, 1\n",
      "LR f1 score: 0.125\n",
      "LR cohens kappa score: 0.104\n",
      "LR average precision score: 0.247\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 284, 3\n",
      "GB fn, tp: 10, 1\n",
      "GB f1 score: 0.133\n",
      "GB cohens kappa score: 0.116\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 287, 0\n",
      "KNN fn, tp: 11, 0\n",
      "KNN f1 score: 0.000\n",
      "KNN cohens kappa score: 0.000\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 1106 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, 1\n",
      "LR fn, tp: 8, 3\n",
      "LR f1 score: 0.400\n",
      "LR cohens kappa score: 0.388\n",
      "LR average precision score: 0.502\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 283, 4\n",
      "GB fn, tp: 5, 6\n",
      "GB f1 score: 0.571\n",
      "GB cohens kappa score: 0.556\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 287, 0\n",
      "KNN fn, tp: 10, 1\n",
      "KNN f1 score: 0.167\n",
      "KNN cohens kappa score: 0.162\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 1104 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, 0\n",
      "LR fn, tp: 5, 2\n",
      "LR f1 score: 0.444\n",
      "LR cohens kappa score: 0.438\n",
      "LR average precision score: 0.444\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 282, 3\n",
      "GB fn, tp: 4, 3\n",
      "GB f1 score: 0.462\n",
      "GB cohens kappa score: 0.449\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 285, 0\n",
      "KNN fn, tp: 5, 2\n",
      "KNN f1 score: 0.444\n",
      "KNN cohens kappa score: 0.438\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 1106 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, 0\n",
      "LR fn, tp: 8, 3\n",
      "LR f1 score: 0.429\n",
      "LR cohens kappa score: 0.419\n",
      "LR average precision score: 0.471\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 285, 2\n",
      "GB fn, tp: 8, 3\n",
      "GB f1 score: 0.375\n",
      "GB cohens kappa score: 0.360\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 287, 0\n",
      "KNN fn, tp: 10, 1\n",
      "KNN f1 score: 0.167\n",
      "KNN cohens kappa score: 0.162\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 1106 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, 0\n",
      "LR fn, tp: 11, 0\n",
      "LR f1 score: 0.000\n",
      "LR cohens kappa score: 0.000\n",
      "LR average precision score: 0.436\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 286, 1\n",
      "GB fn, tp: 7, 4\n",
      "GB f1 score: 0.500\n",
      "GB cohens kappa score: 0.488\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 286, 1\n",
      "KNN fn, tp: 11, 0\n",
      "KNN f1 score: 0.000\n",
      "KNN cohens kappa score: -0.006\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 1106 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, 2\n",
      "LR fn, tp: 10, 1\n",
      "LR f1 score: 0.143\n",
      "LR cohens kappa score: 0.129\n",
      "LR average precision score: 0.244\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 281, 6\n",
      "GB fn, tp: 10, 1\n",
      "GB f1 score: 0.111\n",
      "GB cohens kappa score: 0.085\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 287, 0\n",
      "KNN fn, tp: 11, 0\n",
      "KNN f1 score: 0.000\n",
      "KNN cohens kappa score: 0.000\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 1106 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, 4\n",
      "LR fn, tp: 8, 3\n",
      "LR f1 score: 0.333\n",
      "LR cohens kappa score: 0.314\n",
      "LR average precision score: 0.308\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 283, 4\n",
      "GB fn, tp: 9, 2\n",
      "GB f1 score: 0.235\n",
      "GB cohens kappa score: 0.215\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 286, 1\n",
      "KNN fn, tp: 10, 1\n",
      "KNN f1 score: 0.154\n",
      "KNN cohens kappa score: 0.144\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 1104 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, 2\n",
      "LR fn, tp: 4, 3\n",
      "LR f1 score: 0.500\n",
      "LR cohens kappa score: 0.490\n",
      "LR average precision score: 0.586\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 284, 1\n",
      "GB fn, tp: 3, 4\n",
      "GB f1 score: 0.667\n",
      "GB cohens kappa score: 0.660\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 285, 0\n",
      "KNN fn, tp: 5, 2\n",
      "KNN f1 score: 0.444\n",
      "KNN cohens kappa score: 0.438\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 1106 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, 1\n",
      "LR fn, tp: 10, 1\n",
      "LR f1 score: 0.154\n",
      "LR cohens kappa score: 0.144\n",
      "LR average precision score: 0.270\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 282, 5\n",
      "GB fn, tp: 11, 0\n",
      "GB f1 score: 0.000\n",
      "GB cohens kappa score: -0.024\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 287, 0\n",
      "KNN fn, tp: 11, 0\n",
      "KNN f1 score: 0.000\n",
      "KNN cohens kappa score: 0.000\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 1106 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, 2\n",
      "LR fn, tp: 8, 3\n",
      "LR f1 score: 0.375\n",
      "LR cohens kappa score: 0.360\n",
      "LR average precision score: 0.544\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 286, 1\n",
      "GB fn, tp: 9, 2\n",
      "GB f1 score: 0.286\n",
      "GB cohens kappa score: 0.274\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 287, 0\n",
      "KNN fn, tp: 9, 2\n",
      "KNN f1 score: 0.308\n",
      "KNN cohens kappa score: 0.300\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 1106 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, 0\n",
      "LR fn, tp: 10, 1\n",
      "LR f1 score: 0.167\n",
      "LR cohens kappa score: 0.162\n",
      "LR average precision score: 0.548\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 285, 2\n",
      "GB fn, tp: 8, 3\n",
      "GB f1 score: 0.375\n",
      "GB cohens kappa score: 0.360\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 287, 0\n",
      "KNN fn, tp: 10, 1\n",
      "KNN f1 score: 0.167\n",
      "KNN cohens kappa score: 0.162\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 1106 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, 3\n",
      "LR fn, tp: 7, 4\n",
      "LR f1 score: 0.444\n",
      "LR cohens kappa score: 0.428\n",
      "LR average precision score: 0.503\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 280, 7\n",
      "GB fn, tp: 9, 2\n",
      "GB f1 score: 0.200\n",
      "GB cohens kappa score: 0.173\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 287, 0\n",
      "KNN fn, tp: 11, 0\n",
      "KNN f1 score: 0.000\n",
      "KNN cohens kappa score: 0.000\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 1104 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, 2\n",
      "LR fn, tp: 7, 0\n",
      "LR f1 score: 0.000\n",
      "LR cohens kappa score: -0.011\n",
      "LR average precision score: 0.140\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 281, 4\n",
      "GB fn, tp: 5, 2\n",
      "GB f1 score: 0.308\n",
      "GB cohens kappa score: 0.292\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 285, 0\n",
      "KNN fn, tp: 7, 0\n",
      "KNN f1 score: 0.000\n",
      "KNN cohens kappa score: 0.000\n",
      "\n",
      "### Exercise is done.\n",
      "\n",
      "-----[ LR ]-----\n",
      "maximum:\n",
      "LR tn, fp: 287, 4\n",
      "LR fn, tp: 11, 6\n",
      "LR f1 score: 0.600\n",
      "LR cohens kappa score: 0.586\n",
      "LR average precision score: 0.656\n",
      "\n",
      "\n",
      "average:\n",
      "LR tn, fp: 284.8, 1.8\n",
      "LR fn, tp: 8.2, 2.0\n",
      "LR f1 score: 0.270\n",
      "LR cohens kappa score: 0.258\n",
      "LR average precision score: 0.408\n",
      "\n",
      "\n",
      "minimum:\n",
      "LR tn, fp: 283, 0\n",
      "LR fn, tp: 4, 0\n",
      "LR f1 score: 0.000\n",
      "LR cohens kappa score: -0.016\n",
      "LR average precision score: 0.140\n",
      "\n",
      "\n",
      "-----[ GB ]-----\n",
      "maximum:\n",
      "GB tn, fp: 286, 7\n",
      "GB fn, tp: 11, 7\n",
      "GB f1 score: 0.667\n",
      "GB cohens kappa score: 0.660\n",
      "\n",
      "\n",
      "average:\n",
      "GB tn, fp: 283.48, 3.12\n",
      "GB fn, tp: 7.44, 2.76\n",
      "GB f1 score: 0.338\n",
      "GB cohens kappa score: 0.322\n",
      "\n",
      "\n",
      "minimum:\n",
      "GB tn, fp: 280, 1\n",
      "GB fn, tp: 3, 0\n",
      "GB f1 score: 0.000\n",
      "GB cohens kappa score: -0.024\n",
      "\n",
      "\n",
      "-----[ KNN ]-----\n",
      "maximum:\n",
      "KNN tn, fp: 287, 1\n",
      "KNN fn, tp: 11, 2\n",
      "KNN f1 score: 0.444\n",
      "KNN cohens kappa score: 0.438\n",
      "\n",
      "\n",
      "average:\n",
      "KNN tn, fp: 286.32, 0.28\n",
      "KNN fn, tp: 9.68, 0.52\n",
      "KNN f1 score: 0.092\n",
      "KNN cohens kappa score: 0.088\n",
      "\n",
      "\n",
      "minimum:\n",
      "KNN tn, fp: 284, 0\n",
      "KNN fn, tp: 5, 0\n",
      "KNN f1 score: 0.000\n",
      "KNN cohens kappa score: -0.006\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
}