Document.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PYCM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Version : 0.1\n",
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pycm import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_actu = [2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2]\n",
    "y_pred = [0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = ConfusionMatrix(y_actu, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pycm.ConfusionMatrix([0, 1, 2])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm.actual_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm.predict_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 3, 1: 1, 2: 3}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm.TP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 7, 1: 8, 2: 4}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm.TN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 2, 1: 1, 2: 2}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm.FP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0, 1: 2, 2: 3}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm.FN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TPR  (sensitivity, recall, hit rate, or true positive rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$TPR=\\frac{TP}{P}=\\frac{TP}{TP+FN}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 1.0, 1: 0.33333, 2: 0.5}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm.TPR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TNR (specificity or true negative rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$TNR=\\frac{TN}{N}=\\frac{TN}{TN+FP}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.77778, 1: 0.88889, 2: 0.66667}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm.TNR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PPV (precision or positive predictive value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$PPV=\\frac{TP}{TP+FP}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.6, 1: 0.5, 2: 0.6}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm.PPV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NPV (negative predictive value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$NPV=\\frac{TN}{TN+FN}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 1.0, 1: 0.8, 2: 0.57143}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm.NPV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FNR (miss rate or false negative rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$FNR=\\frac{FN}{P}=\\frac{FN}{FN+TP}=1-TPR$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.0, 1: 0.66667, 2: 0.5}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm.FNR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FPR (fall-out or false positive rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$FPR=\\frac{FP}{N}=\\frac{FP}{FP+TN}=1-TNR$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.22222, 1: 0.11111, 2: 0.33333}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm.FPR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FDR (false discovery rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$FDR=\\frac{FP}{FP+TP}=1-PPV$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.6, 1: 0.5, 2: 0.6}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm.PPV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FOR (false omission rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$FOR=\\frac{FN}{FN+TN}=1-NPV$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.0, 1: 0.2, 2: 0.42857}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm.FOR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ACC (accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ACC=\\frac{TP+TN}{P+N}=\\frac{TP+TN}{TP+TN+FP+FN}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.83333, 1: 0.75, 2: 0.58333}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm.ACC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## F1-Score (harmonic mean of precision and sensitivity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$F1=2\\frac{PPV.TPR}{PPV+TPR}=\\frac{2TP}{2TP+FP+FN}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.75, 1: 0.4, 2: 0.54545}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm.F1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MCC (Matthews correlation coefficient)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$MCC=\\frac{TP \\times TN-FP \\times FN}{\\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.68313, 1: 0.2582, 2: 0.16903}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm.MCC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BM (nformedness or Bookmaker Informedness)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$BM=TPR+TNR-1$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.77778, 1: 0.22222, 2: 0.16667}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm.BM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MK (Markedness)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$MK=PPV+NPV-1$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.6, 1: 0.3, 2: 0.17143}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm.MK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LR+ (Positive likelihood ratio)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$(LR+)=\\frac{TPR}{FPR}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 4.50005, 1: 3.0, 2: 1.50002}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm.PLR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LR- (Negative likelihood ratio)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$(LR-)=\\frac{FNR}{TNR}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.0, 1: 0.75, 2: 0.75}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm.NLR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DOR (Diagnostic odds ratio)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$DOR=\\frac{LR+}{LR-}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'None', 1: 4.0, 2: 2.00003}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm.DOR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Full Run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predict          0    1    2    \n",
      "Actual\n",
      "0                3    0    0    \n",
      "1                0    1    2    \n",
      "2                2    1    3    \n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Classes                                                          0                       1                       2                       \n",
      "ACC(accuracy)                                                    0.83333                 0.75                    0.58333                 \n",
      "BM(Informedness or Bookmaker Informedness)                       0.77778                 0.22222                 0.16667                 \n",
      "DOR(Diagnostic odds ratio)                                       None                    4.0                     2.00003                 \n",
      "F1(F1 Score - harmonic mean of precision and sensitivity)        0.75                    0.4                     0.54545                 \n",
      "FDR(false discovery rate)                                        0.4                     0.5                     0.4                     \n",
      "FN(false negative/miss/Type II error)                            0                       2                       3                       \n",
      "FNR(miss rate or false negative rate)                            0.0                     0.66667                 0.5                     \n",
      "FOR(false omission rate)                                         0.0                     0.2                     0.42857                 \n",
      "FP(false positive/Type I error/false alarm)                      2                       1                       2                       \n",
      "FPR(fall-out or false positive rate)                             0.22222                 0.11111                 0.33333                 \n",
      "LR+(Positive likelihood ratio)                                   4.50005                 3.0                     1.50002                 \n",
      "LR-(Negative likelihood ratio)                                   0.0                     0.75                    0.75                    \n",
      "MCC(Matthews correlation coefficient)                            0.68313                 0.2582                  0.16903                 \n",
      "MK(Markedness)                                                   0.6                     0.3                     0.17143                 \n",
      "NPV(negative predictive value)                                   1.0                     0.8                     0.57143                 \n",
      "PPV(precision or positive predictive value)                      0.6                     0.5                     0.6                     \n",
      "TN(true negative/correct rejection)                              7                       8                       4                       \n",
      "TNR(specificity or true negative rate)                           0.77778                 0.88889                 0.66667                 \n",
      "TP(true positive/hit)                                            3                       1                       3                       \n",
      "TPR(sensitivity, recall, hit rate, or true positive rate)        1.0                     0.33333                 0.5                     \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Input Errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "pycmError",
     "evalue": "Input Vectors Must Be List",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mpycmError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-28-572bf15e689d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcm2\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mConfusionMatrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_actu\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python35-32\\lib\\site-packages\\pycm\\pycm.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, actual_vector, predict_vector)\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mactual_vector\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpredict_vector\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mactual_vector\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpredict_vector\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mpycmError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Input Vectors Must Be List\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mactual_vector\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m!=\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpredict_vector\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mpycmError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Input Vectors Must Be The Same Length\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mpycmError\u001b[0m: Input Vectors Must Be List"
     ]
    }
   ],
   "source": [
    "cm2=ConfusionMatrix(y_actu, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "pycmError",
     "evalue": "Input Vectors Must Be The Same Length",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mpycmError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-29-fe0a030b981a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcm3\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mConfusionMatrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_actu\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python35-32\\lib\\site-packages\\pycm\\pycm.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, actual_vector, predict_vector)\u001b[0m\n\u001b[0;32m     10\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mpycmError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Input Vectors Must Be List\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mactual_vector\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m!=\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpredict_vector\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mpycmError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Input Vectors Must Be The Same Length\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     13\u001b[0m         \u001b[0mmatrix_param\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmatrix_params_calc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mactual_vector\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpredict_vector\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mactual_vector\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mactual_vector\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mpycmError\u001b[0m: Input Vectors Must Be The Same Length"
     ]
    }
   ],
   "source": [
    "cm3=ConfusionMatrix(y_actu, [1,2,3])"
   ]
  }
 ],
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