confusion_matrix.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "63808aab",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import pandas as pd\n",
    "from sklearn.metrics import classification_report\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bed3009f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Source code credit for this function: https://gist.github.com/shaypal5/94c53d765083101efc0240d776a23823\n",
    "def print_confusion_matrix(confusion_matrix, class_names, figsize = (10,7), fontsize=14):\n",
    "    \"\"\"Prints a confusion matrix, as returned by sklearn.metrics.confusion_matrix, as a heatmap.\n",
    "    \n",
    "    Arguments\n",
    "    ---------\n",
    "    confusion_matrix: numpy.ndarray\n",
    "        The numpy.ndarray object returned from a call to sklearn.metrics.confusion_matrix. \n",
    "        Similarly constructed ndarrays can also be used.\n",
    "    class_names: list\n",
    "        An ordered list of class names, in the order they index the given confusion matrix.\n",
    "    figsize: tuple\n",
    "        A 2-long tuple, the first value determining the horizontal size of the ouputted figure,\n",
    "        the second determining the vertical size. Defaults to (10,7).\n",
    "    fontsize: int\n",
    "        Font size for axes labels. Defaults to 14.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    matplotlib.figure.Figure\n",
    "        The resulting confusion matrix figure\n",
    "    \"\"\"\n",
    "    df_cm = pd.DataFrame(\n",
    "        confusion_matrix, index=class_names, columns=class_names, \n",
    "    )\n",
    "    fig = plt.figure(figsize=figsize)\n",
    "    try:\n",
    "        heatmap = sns.heatmap(df_cm, annot=True, fmt=\"d\")\n",
    "    except ValueError:\n",
    "        raise ValueError(\"Confusion matrix values must be integers.\")\n",
    "    heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=fontsize)\n",
    "    heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=fontsize)\n",
    "    plt.ylabel('Truth')\n",
    "    plt.xlabel('Prediction')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dbeb1460",
   "metadata": {},
   "outputs": [],
   "source": [
    "truth =      [\"Dog\",\"Not a dog\",\"Dog\",\"Dog\",      \"Dog\", \"Not a dog\", \"Not a dog\", \"Dog\",       \"Dog\", \"Not a dog\"]\n",
    "prediction = [\"Dog\",\"Dog\",      \"Dog\",\"Not a dog\",\"Dog\", \"Not a dog\", \"Dog\",       \"Not a dog\", \"Dog\", \"Dog\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1ef50340",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(truth,prediction)\n",
    "print_confusion_matrix(cm,[\"Dog\",\"Not a dog\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8d964bc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         Dog       0.57      0.67      0.62         6\n",
      "   Not a dog       0.33      0.25      0.29         4\n",
      "\n",
      "    accuracy                           0.50        10\n",
      "   macro avg       0.45      0.46      0.45        10\n",
      "weighted avg       0.48      0.50      0.48        10\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(truth, prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f13911cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6159677419354839"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#for dog(f1 score)\n",
    "\n",
    "2*(0.57*0.67/(0.57+0.67))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "13d3f926",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2844827586206896"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#for not a dog score(f1 score)\n",
    "2*(0.33*0.25/(0.33+0.25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83331eff",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}