{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p  style=\"z-index: 101;background: #fde073;text-align: center;line-height: 2.5;overflow: hidden;font-size:22px;\">Please <a href=\"https://www.pycm.io/doc/#Cite\" target=\"_blank\">cite us</a> if you use the software</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example-6 (Unbalanced data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Environment check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking that the notebook is running on Google Colab or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "try:\n",
    "  import google.colab\n",
    "  !{sys.executable} -m pip -q -q install pycm\n",
    "except:\n",
    "  pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Binary classification for unbalanced data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pycm import ConfusionMatrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Case1 (Both classes have a good result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$Case_1=\\begin{bmatrix}26900 & 40 \\\\25 & 500 \\end{bmatrix}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predict       Class1        Class2        \n",
      "Actual\n",
      "Class1        0.99852       0.00148       \n",
      "\n",
      "Class2        0.04762       0.95238       \n",
      "\n",
      "\n",
      "ACC: {'Class2': 0.9976333515383216, 'Class1': 0.9976333515383216}\n",
      "MCC: {'Class2': 0.9378574017402594, 'Class1': 0.9378574017402594}\n",
      "CEN: {'Class2': 0.30489006849060607, 'Class1': 0.012858728415908176}\n",
      "MCEN: {'Class2': 0.46949279678726225, 'Class1': 0.023280122318969122}\n",
      "DP: {'Class2': 2.276283896527635, 'Class1': 2.276283896527635}\n",
      "Kappa: 0.9377606597584491\n",
      "RCI: 0.8682877002417864\n",
      "SOA1: Almost Perfect\n"
     ]
    }
   ],
   "source": [
    "case1 = ConfusionMatrix(matrix={\"Class1\": {\"Class1\": 26900, \"Class2\": 40}, \"Class2\": {\"Class1\": 25, \"Class2\": 500}})\n",
    "case1.print_normalized_matrix()\n",
    "print('ACC:', case1.ACC)\n",
    "print('MCC:', case1.MCC)\n",
    "print('CEN:', case1.CEN)\n",
    "print('MCEN:', case1.MCEN)\n",
    "print('DP:', case1.DP)\n",
    "print('Kappa:', case1.Kappa)\n",
    "print('RCI:', case1.RCI)\n",
    "print('SOA1:', case1.SOA1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Case2 (The first class has a good result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$Case_2=\\begin{bmatrix}26900 & 40 \\\\500 & 25 \\end{bmatrix}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predict       Class1        Class2        \n",
      "Actual\n",
      "Class1        0.99865       0.00135       \n",
      "\n",
      "Class2        0.95238       0.04762       \n",
      "\n",
      "\n",
      "ACC: {'Class2': 0.982098458478369, 'Class1': 0.982098458478369}\n",
      "MCC: {'Class2': 0.13048897476798949, 'Class1': 0.13048897476798949}\n",
      "CEN: {'Class2': 0.4655917826576813, 'Class1': 0.06481573363174531}\n",
      "MCEN: {'Class2': 0.4264929996758212, 'Class1': 0.11078640690031397}\n",
      "DP: {'Class2': 0.864594924328404, 'Class1': 0.864594924328404}\n",
      "Kappa: 0.08122239707598865\n",
      "RCI: 0.022375346499017443\n",
      "SOA1: Slight\n"
     ]
    }
   ],
   "source": [
    "case2 = ConfusionMatrix(matrix={\"Class1\": {\"Class1\": 29600, \"Class2\": 40}, \"Class2\": {\"Class1\": 500, \"Class2\": 25}})\n",
    "case2.print_normalized_matrix()\n",
    "print('ACC:', case2.ACC)\n",
    "print('MCC:', case2.MCC)\n",
    "print('CEN:', case2.CEN)\n",
    "print('MCEN:', case2.MCEN)\n",
    "print('DP:', case2.DP)\n",
    "print('Kappa:', case2.Kappa)\n",
    "print('RCI:', case2.RCI)\n",
    "print('SOA1:', case2.SOA1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Case3 (Second class has a good result )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$Case_3=\\begin{bmatrix}40 & 26900 \\\\25 & 500 \\end{bmatrix}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predict       Class1        Class2        \n",
      "Actual\n",
      "Class1        0.00148       0.99852       \n",
      "\n",
      "Class2        0.04762       0.95238       \n",
      "\n",
      "\n",
      "ACC: {'Class2': 0.019661387220098307, 'Class1': 0.019661387220098307}\n",
      "MCC: {'Class2': -0.13000800945464058, 'Class1': -0.13000800945464058}\n",
      "CEN: {'Class2': 0.06103563616795208, 'Class1': 0.014927427128936136}\n",
      "MCEN: {'Class2': 0.03655796690365652, 'Class1': 0.01281422838054554}\n",
      "DP: {'Class2': -0.8416930356875597, 'Class1': -0.8416930356875597}\n",
      "Kappa: -0.0017678372492452412\n",
      "RCI: 0.02192606003351106\n",
      "SOA1: Poor\n"
     ]
    }
   ],
   "source": [
    "case3 = ConfusionMatrix(matrix={\"Class1\": {\"Class1\": 40, \"Class2\": 26900}, \"Class2\": {\"Class1\": 25, \"Class2\": 500}})\n",
    "case3.print_normalized_matrix()\n",
    "print('ACC:', case3.ACC)\n",
    "print('MCC:', case3.MCC)\n",
    "print('CEN:', case3.CEN)\n",
    "print('MCEN:', case3.MCEN)\n",
    "print('DP:', case3.DP)\n",
    "print('Kappa:', case3.Kappa)\n",
    "print('RCI:', case3.RCI)\n",
    "print('SOA1:', case3.SOA1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi-class classification for unbalanced data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Case1 (All classes have good result  and are unbalanced)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$Case_1=\\begin{bmatrix}4 & 0 &0&1 \\\\0 & 4&1&0\\\\0&1&4&0\\\\0&0&1&4000 \\end{bmatrix}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predict       Class1        Class2        Class3        Class4        \n",
      "Actual\n",
      "Class1        0.8           0.0           0.0           0.2           \n",
      "\n",
      "Class2        0.0           0.8           0.2           0.0           \n",
      "\n",
      "Class3        0.0           0.2           0.8           0.0           \n",
      "\n",
      "Class4        0.0           0.0           2e-05         0.99998       \n",
      "\n",
      "\n",
      "ACC: {'Class3': 0.9999250299880048, 'Class4': 0.9999500199920032, 'Class2': 0.9999500199920032, 'Class1': 0.9999750099960016}\n",
      "MCC: {'Class3': 0.7302602381427055, 'Class4': 0.9333083339583177, 'Class2': 0.7999750068731099, 'Class1': 0.8944160139432883}\n",
      "CEN: {'Class3': 0.3649884090288471, 'Class4': 0.0001575200922489127, 'Class2': 0.25701944178769376, 'Class1': 0.13625493172565745}\n",
      "MCEN: {'Class3': 0.4654427710721536, 'Class4': 0.00029569133318617423, 'Class2': 0.3333333333333333, 'Class1': 0.17964888034078544}\n",
      "DP: {'Class3': 2.7032690544190636, 'Class4': 3.1691421556058055, 'Class2': 2.869241573973406, 'Class1': 'None'}\n",
      "Kappa: 0.8666333383326446\n",
      "RCI: 0.8711441699127427\n",
      "SOA1: Almost Perfect\n"
     ]
    }
   ],
   "source": [
    "case1 = ConfusionMatrix(\n",
    "    matrix={\n",
    "        \"Class1\": {\"Class1\": 4, \"Class2\": 0, \"Class3\": 0, \"Class4\": 1},\n",
    "        \"Class2\": {\"Class1\": 0, \"Class2\": 4, \"Class3\": 1, \"Class4\": 0},\n",
    "        \"Class3\": {\"Class1\": 0, \"Class2\": 1, \"Class3\": 4, \"Class4\": 0},\n",
    "        \"Class4\": {\"Class1\": 0, \"Class2\": 0, \"Class3\": 1, \"Class4\": 40000}})\n",
    "case1.print_normalized_matrix()\n",
    "print('ACC:', case1.ACC)\n",
    "print('MCC:', case1.MCC)\n",
    "print('CEN:', case1.CEN)\n",
    "print('MCEN:', case1.MCEN)\n",
    "print('DP:', case1.DP)\n",
    "print('Kappa:', case1.Kappa)\n",
    "print('RCI:', case1.RCI)\n",
    "print('SOA1:', case1.SOA1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Case2 (All classes have same result and are balanced)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$Case_2=\\begin{bmatrix}1 & 1 &1&1 \\\\1 & 1&1&1\\\\1&1&1&1\\\\1&1&1&1 \\end{bmatrix}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predict      Class1       Class2       Class3       Class4       \n",
      "Actual\n",
      "Class1       0.25         0.25         0.25         0.25         \n",
      "\n",
      "Class2       0.25         0.25         0.25         0.25         \n",
      "\n",
      "Class3       0.25         0.25         0.25         0.25         \n",
      "\n",
      "Class4       0.25         0.25         0.25         0.25         \n",
      "\n",
      "\n",
      "ACC: {'Class3': 0.625, 'Class4': 0.625, 'Class2': 0.625, 'Class1': 0.625}\n",
      "MCC: {'Class3': 0.0, 'Class4': 0.0, 'Class2': 0.0, 'Class1': 0.0}\n",
      "CEN: {'Class3': 0.8704188162777186, 'Class4': 0.8704188162777186, 'Class2': 0.8704188162777186, 'Class1': 0.8704188162777186}\n",
      "MCEN: {'Class3': 0.9308855421443073, 'Class4': 0.9308855421443073, 'Class2': 0.9308855421443073, 'Class1': 0.9308855421443073}\n",
      "DP: {'Class3': 0.0, 'Class4': 0.0, 'Class2': 0.0, 'Class1': 0.0}\n",
      "Kappa: 0.0\n",
      "RCI: 0.0\n",
      "SOA1: Slight\n"
     ]
    }
   ],
   "source": [
    "case2 = ConfusionMatrix(\n",
    "    matrix={\n",
    "        \"Class1\": {\"Class1\": 1, \"Class2\": 1, \"Class3\": 1, \"Class4\": 1},\n",
    "        \"Class2\": {\"Class1\": 1, \"Class2\": 1, \"Class3\": 1, \"Class4\": 1},\n",
    "        \"Class3\": {\"Class1\": 1, \"Class2\": 1, \"Class3\": 1, \"Class4\": 1},\n",
    "        \"Class4\": {\"Class1\": 1, \"Class2\": 1, \"Class3\": 1, \"Class4\": 1}})\n",
    "case2.print_normalized_matrix()\n",
    "print('ACC:', case2.ACC)\n",
    "print('MCC:', case2.MCC)\n",
    "print('CEN:', case2.CEN)\n",
    "print('MCEN:', case2.MCEN)\n",
    "print('DP:', case2.DP)\n",
    "print('Kappa:', case2.Kappa)\n",
    "print('RCI:', case2.RCI)\n",
    "print('SOA1:', case2.SOA1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Case3 (A class has a bad result and is a bit unbalanced)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$Case_3=\\begin{bmatrix}1 & 1 &1&1 \\\\1 & 1&1&1\\\\1&1&1&1\\\\10&1&1&1 \\end{bmatrix}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predict       Class1        Class2        Class3        Class4        \n",
      "Actual\n",
      "Class1        0.25          0.25          0.25          0.25          \n",
      "\n",
      "Class2        0.25          0.25          0.25          0.25          \n",
      "\n",
      "Class3        0.25          0.25          0.25          0.25          \n",
      "\n",
      "Class4        0.76923       0.07692       0.07692       0.07692       \n",
      "\n",
      "\n",
      "ACC: {'Class3': 0.76, 'Class4': 0.4, 'Class2': 0.76, 'Class1': 0.4}\n",
      "MCC: {'Class3': 0.10714285714285714, 'Class4': -0.2358640882624316, 'Class2': 0.10714285714285714, 'Class1': -0.2358640882624316}\n",
      "CEN: {'Class3': 0.8704188162777186, 'Class4': 0.6392779429225796, 'Class2': 0.8704188162777186, 'Class1': 0.6392779429225794}\n",
      "MCEN: {'Class3': 0.9308855421443073, 'Class4': 0.647512271542988, 'Class2': 0.9308855421443073, 'Class1': 0.647512271542988}\n",
      "DP: {'Class3': 0.16596653499824943, 'Class4': -0.3319330699964992, 'Class2': 0.16596653499824943, 'Class1': -0.33193306999649924}\n",
      "Kappa: -0.07361963190184047\n",
      "RCI: 0.11603030564493627\n",
      "SOA1: Poor\n"
     ]
    }
   ],
   "source": [
    "case3 = ConfusionMatrix(\n",
    "    matrix={\n",
    "        \"Class1\": {\"Class1\": 1, \"Class2\": 1, \"Class3\": 1, \"Class4\": 1},\n",
    "        \"Class2\": {\"Class1\": 1, \"Class2\": 1, \"Class3\": 1, \"Class4\": 1},\n",
    "        \"Class3\": {\"Class1\": 1, \"Class2\": 1, \"Class3\": 1, \"Class4\": 1},\n",
    "        \"Class4\": {\"Class1\": 10, \"Class2\": 1, \"Class3\": 1, \"Class4\": 1}})\n",
    "case3.print_normalized_matrix()\n",
    "print('ACC:', case3.ACC)\n",
    "print('MCC:', case3.MCC)\n",
    "print('CEN:', case3.CEN)\n",
    "print('MCEN:', case3.MCEN)\n",
    "print('DP:', case3.DP)\n",
    "print('Kappa:', case3.Kappa)\n",
    "print('RCI:', case3.RCI)\n",
    "print('SOA1:', case3.SOA1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Case4 (A class is very unbalaned and get bad result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$Case_4=\\begin{bmatrix}1 & 1 &1&1 \\\\1 & 1&1&1\\\\1&1&1&1\\\\10000&1&1&1 \\end{bmatrix}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predict       Class1        Class2        Class3        Class4        \n",
      "Actual\n",
      "Class1        0.25          0.25          0.25          0.25          \n",
      "\n",
      "Class2        0.25          0.25          0.25          0.25          \n",
      "\n",
      "Class3        0.25          0.25          0.25          0.25          \n",
      "\n",
      "Class4        0.76923       0.07692       0.07692       0.07692       \n",
      "\n",
      "\n",
      "ACC: {'Class3': 0.999400898652022, 'Class4': 0.000998502246630055, 'Class2': 0.999400898652022, 'Class1': 0.000998502246630055}\n",
      "MCC: {'Class3': 0.24970032963739885, 'Class4': -0.43266656861311537, 'Class2': 0.24970032963739885, 'Class1': -0.43266656861311537}\n",
      "CEN: {'Class3': 0.8704188162777186, 'Class4': 0.0029588592520426657, 'Class2': 0.8704188162777186, 'Class1': 0.0029588592520426657}\n",
      "MCEN: {'Class3': 0.9308855421443073, 'Class4': 0.002903385725603509, 'Class2': 0.9308855421443073, 'Class1': 0.002903385725603509}\n",
      "DP: {'Class3': 1.6794055876913858, 'Class4': -1.9423127303715728, 'Class2': 1.6794055876913858, 'Class1': -1.9423127303715728}\n",
      "Kappa: -0.0003990813465900262\n",
      "RCI: 0.5536610475678804\n",
      "SOA1: Poor\n"
     ]
    }
   ],
   "source": [
    "case4 = ConfusionMatrix(\n",
    "    matrix={\n",
    "        \"Class1\": {\"Class1\": 1, \"Class2\": 1, \"Class3\": 1, \"Class4\": 1},\n",
    "        \"Class2\": {\"Class1\": 1, \"Class2\": 1, \"Class3\": 1, \"Class4\": 1},\n",
    "        \"Class3\": {\"Class1\": 1, \"Class2\": 1, \"Class3\": 1, \"Class4\": 1},\n",
    "        \"Class4\": {\"Class1\": 10000, \"Class2\": 1, \"Class3\": 1, \"Class4\": 1}})\n",
    "case3.print_normalized_matrix()\n",
    "print('ACC:', case4.ACC)\n",
    "print('MCC:', case4.MCC)\n",
    "print('CEN:', case4.CEN)\n",
    "print('MCEN:', case4.MCEN)\n",
    "print('DP:', case4.DP)\n",
    "print('Kappa:', case4.Kappa)\n",
    "print('RCI:', case4.RCI)\n",
    "print('SOA1:', case4.SOA1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Case5 (A class is very unbalaned and get bad result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$Case_5=\\begin{bmatrix}1 & 1 &1&1 \\\\1 & 1&1&1\\\\1&1&1&1\\\\10&10&10&10 \\end{bmatrix}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predict      Class1       Class2       Class3       Class4       \n",
      "Actual\n",
      "Class1       0.25         0.25         0.25         0.25         \n",
      "\n",
      "Class2       0.25         0.25         0.25         0.25         \n",
      "\n",
      "Class3       0.25         0.25         0.25         0.25         \n",
      "\n",
      "Class4       0.25         0.25         0.25         0.25         \n",
      "\n",
      "\n",
      "ACC: {'Class3': 0.7115384615384616, 'Class4': 0.36538461538461536, 'Class2': 0.7115384615384616, 'Class1': 0.7115384615384616}\n",
      "MCC: {'Class3': 0.0, 'Class4': 0.0, 'Class2': 0.0, 'Class1': 0.0}\n",
      "CEN: {'Class3': 0.6392779429225794, 'Class4': 0.6522742127953861, 'Class2': 0.6392779429225794, 'Class1': 0.6392779429225794}\n",
      "MCEN: {'Class3': 0.647512271542988, 'Class4': 0.7144082229288313, 'Class2': 0.647512271542988, 'Class1': 0.647512271542988}\n",
      "DP: {'Class3': 0.0, 'Class4': 0.0, 'Class2': 0.0, 'Class1': 0.0}\n",
      "Kappa: 0.0\n",
      "RCI: 0.0\n",
      "SOA1: Slight\n"
     ]
    }
   ],
   "source": [
    "case5 = ConfusionMatrix(\n",
    "    matrix={\n",
    "        \"Class1\": {\"Class1\": 1, \"Class2\": 1, \"Class3\": 1, \"Class4\": 1},\n",
    "        \"Class2\": {\"Class1\": 1, \"Class2\": 1, \"Class3\": 1, \"Class4\": 1},\n",
    "        \"Class3\": {\"Class1\": 1, \"Class2\": 1, \"Class3\": 1, \"Class4\": 1},\n",
    "        \"Class4\": {\"Class1\": 10, \"Class2\": 10, \"Class3\": 10, \"Class4\": 10}})\n",
    "case5.print_normalized_matrix()\n",
    "print('ACC:', case5.ACC)\n",
    "print('MCC:', case5.MCC)\n",
    "print('CEN:', case5.CEN)\n",
    "print('MCEN:', case5.MCEN)\n",
    "print('DP:', case5.DP)\n",
    "print('Kappa:', case5.Kappa)\n",
    "print('RCI:', case5.RCI)\n",
    "print('SOA1:', case5.SOA1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Case6 (A class is very unbalaned and get bad result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$Case_6=\\begin{bmatrix}1 & 1 &1&1 \\\\1 & 1&1&1\\\\1&1&1&1\\\\10000&10000&10000&10000 \\end{bmatrix}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predict      Class1       Class2       Class3       Class4       \n",
      "Actual\n",
      "Class1       0.25         0.25         0.25         0.25         \n",
      "\n",
      "Class2       0.25         0.25         0.25         0.25         \n",
      "\n",
      "Class3       0.25         0.25         0.25         0.25         \n",
      "\n",
      "Class4       0.25         0.25         0.25         0.25         \n",
      "\n",
      "\n",
      "ACC: {'Class3': 0.7499500149955014, 'Class4': 0.25014995501349596, 'Class2': 0.7499500149955014, 'Class1': 0.7499500149955014}\n",
      "MCC: {'Class3': 0.0, 'Class4': 0.0, 'Class2': 0.0, 'Class1': 0.0}\n",
      "CEN: {'Class3': 0.0029588592520426657, 'Class4': 0.539296694603886, 'Class2': 0.0029588592520426657, 'Class1': 0.0029588592520426657}\n",
      "MCEN: {'Class3': 0.002903385725603509, 'Class4': 0.580710610324597, 'Class2': 0.002903385725603509, 'Class1': 0.002903385725603509}\n",
      "DP: {'Class3': 0.0, 'Class4': 0.0, 'Class2': 0.0, 'Class1': 0.0}\n",
      "Kappa: 0.0\n",
      "RCI: 0.0\n",
      "SOA1: Slight\n"
     ]
    }
   ],
   "source": [
    "case6 = ConfusionMatrix(\n",
    "    matrix={\n",
    "        \"Class1\": {\"Class1\": 1, \"Class2\": 1, \"Class3\": 1, \"Class4\": 1},\n",
    "        \"Class2\": {\"Class1\": 1, \"Class2\": 1, \"Class3\": 1, \"Class4\": 1},\n",
    "        \"Class3\": {\"Class1\": 1, \"Class2\": 1, \"Class3\": 1, \"Class4\": 1},\n",
    "        \"Class4\": {\"Class1\": 10000, \"Class2\": 10000, \"Class3\": 10000, \"Class4\": 10000}})\n",
    "case6.print_normalized_matrix()\n",
    "print('ACC:', case6.ACC)\n",
    "print('MCC:', case6.MCC)\n",
    "print('CEN:', case6.CEN)\n",
    "print('MCEN:', case6.MCEN)\n",
    "print('DP:', case6.DP)\n",
    "print('Kappa:', case6.Kappa)\n",
    "print('RCI:', case6.RCI)\n",
    "print('SOA1:', case6.SOA1)"
   ]
  },
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