precision and recall

Author: codebasics

DeepLearningML/12_precision_recall/12_precision_recall.ipynb

@@ -0,0 +1,194 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "from sklearn.metrics import confusion_matrix , classification_report\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "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')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "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": 31,
+   "metadata": {
+    "scrolled": false
+   },
+   "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": 32,
+   "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": "markdown",
+   "metadata": {},
+   "source": [
+    "**f1 score for Dog class**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6159677419354839"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2*(0.57*0.67/(0.57+0.67))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**f1 score for Not a dog class**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.2844827586206896"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2*(0.33*0.25/(0.33+0.25))"
+   ]
+  }
+ ],
+ "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}