confusion_matrix.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "117f9987",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b309aa80",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_confusion_matrix(y_test, y_pred):\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    tn, fp, fn, tp = cm.ravel()\n",
    "\n",
    "    accuracy = (tp + tn) / (tn + fp + fn + tp)\n",
    "    recall = tp / (tp + fn)\n",
    "    precision = tp / (tp + fp)\n",
    "    f1 = 2 * precision * recall / (precision + recall)\n",
    "    \n",
    "    return cm, (tn,fp,fn,tp), (accuracy, recall, precision, f1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a8b73ac4",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "not enough values to unpack (expected 4, got 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_confusion_matrix\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[8], line 3\u001b[0m, in \u001b[0;36mget_confusion_matrix\u001b[0;34m(y_test, y_pred)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_confusion_matrix\u001b[39m(y_test, y_pred):\n\u001b[1;32m      2\u001b[0m     cm \u001b[38;5;241m=\u001b[39m confusion_matrix(y_test, y_pred)\n\u001b[0;32m----> 3\u001b[0m     tn, fp, fn, tp \u001b[38;5;241m=\u001b[39m cm\u001b[38;5;241m.\u001b[39mravel()\n\u001b[1;32m      5\u001b[0m     accuracy \u001b[38;5;241m=\u001b[39m (tp \u001b[38;5;241m+\u001b[39m tn) \u001b[38;5;241m/\u001b[39m (tn \u001b[38;5;241m+\u001b[39m fp \u001b[38;5;241m+\u001b[39m fn \u001b[38;5;241m+\u001b[39m tp)\n\u001b[1;32m      6\u001b[0m     recall \u001b[38;5;241m=\u001b[39m tp \u001b[38;5;241m/\u001b[39m (tp \u001b[38;5;241m+\u001b[39m fn)\n",
      "\u001b[0;31mValueError\u001b[0m: not enough values to unpack (expected 4, got 1)"
     ]
    }
   ],
   "source": [
    "get_confusion_matrix(np.array([0,0,0,0,0]),np.array([0,0,0,0,0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3c07673c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(np.array([1,1,1]),np.array([1,1,1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "478e34f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(9,9))\n",
    "sns.heatmap(rf_cm, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r');\n",
    "plt.ylabel('Actual label');\n",
    "plt.xlabel('Predicted label');\n",
    "all_sample_title = 'Accuracy: {0:.5f}\\n Recall: {1:.5f}\\n Precision: {2:.5f}\\n F1: {3:.5f}'.format(accuracy,recall, precision, f1)\n",
    "plt.title(all_sample_title, size=15)\n",
    "plt.show()"
   ]
  }
 ],
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