{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "7644f259", "metadata": {}, "outputs": [], "source": [ "from sklearn import metrics\n", "import pandas as pd\n", "import seaborn as sn\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "id": "4e4955d3", "metadata": {}, "outputs": [], "source": [ "C = \"Cat\"\n", "F = \"Fish\"\n", "\n", "# True values\n", "y_true = [C, C, C, C, C, C, F, F, F, F, F, F, F, F, F, F]\n", "# Predicted values\n", "y_pred = [C, C, C, C, F, C, C, C, C, C, C, F, F, C, C, C,]" ] }, { "cell_type": "code", "execution_count": 3, "id": "7a333d39", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[5 1]\n", " [8 2]]\n" ] } ], "source": [ "print(metrics.confusion_matrix(y_true, y_pred))" ] }, { "cell_type": "code", "execution_count": 4, "id": "e7dea59b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " Cat 0.385 0.833 0.526 6\n", " Fish 0.667 0.200 0.308 10\n", "\n", " accuracy 0.438 16\n", " macro avg 0.526 0.517 0.417 16\n", "weighted avg 0.561 0.438 0.390 16\n", "\n", "0.4375\n" ] }, { "data": { "text/plain": [ "list" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(metrics.classification_report(y_true, y_pred, digits=3))\n", "print(metrics.accuracy_score(y_true, y_pred))\n", "type(y_true)" ] }, { "cell_type": "code", "execution_count": 5, "id": "2afe2477", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " y_true y_pred\n", "0 Cat Cat\n", "1 Cat Cat\n", "2 Cat Cat\n", "3 Cat Cat\n", "4 Cat Fish\n", "5 Cat Cat\n", "6 Fish Cat\n", "7 Fish Cat\n", "8 Fish Cat\n", "9 Fish Cat\n", "10 Fish Cat\n", "11 Fish Fish\n", "12 Fish Fish\n", "13 Fish Cat\n", "14 Fish Cat\n", "15 Fish Cat\n" ] } ], "source": [ "data = {'y_true': [C, C, C, C, C, C, F, F, F, F, F, F, F, F, F, F],\n", " 'y_pred': [C, C, C, C, F, C, C, C, C, C, C, F, F, C, C, C,]}\n", "df = pd.DataFrame(data)\n", "print(df)" ] }, { "cell_type": "code", "execution_count": 10, "id": "73ea1d96", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predicted Cat Fish\n", "Actual \n", "Cat 5 1\n", "Fish 8 2\n" ] } ], "source": [ "confusion_matrix = pd.crosstab(df['y_true'], df['y_pred'], rownames=['Actual'], colnames=['Predicted'])\n", "print(confusion_matrix)" ] }, { "cell_type": "code", "execution_count": 11, "id": "979b506f", "metadata": {}, "outputs": [ { "data": { "image/png": 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8yj/jx9f8hJO/eQIAH//g8XznvIGLtzSLlvsRpzqI7g9dPHkc8Brgsqq6qF8ffkFVnTTbuatvu7blPxeNqX2f/rrWQ9AY+vebv/2IH3T0ml0OHTrmnHrDipE+WKnTjLiqfg4cM+39jcCsQViSNrSWsya6mkd8cVU9b9p84oe+wnnEksbQmoUWiIHDwPnEkuaPhTiPeMXaF0lcBC9p7LWcvtZVRjy9kL17R31I0sh0OXFhNl0F4prhtSSNpS62wRxWV4H4GUnuopcZb9p/Dd6skzSmFtxTnKtqcRfXlaSuLMSMWJLmlYVYI5akeaWL2RDDMhBLEgtwZZ0kzTfWiCWpsclqV5wwEEsSliYkqbkuNnwfloFYkmi7BLjrRyVJ0rwwRQ19DJLk95Ocn+SHSX6Q5K2z9W1GLEmMdNbEGuDtVXVFki2Ay5N8s6pmfPKvgViSGN2siar6GfCz/uu7k/wQ2BmYMRBbmpAkerMmhv0ryfIkK6cdy9d3zSS7AnsDA5/sakYsScxtr4mqmgAmBrVJsjlwOvC2qrprUFsDsSQx2pV1SR5DLwifUlVnzNbeQCxJjG73tSQBPg38sKqOma09WCOWJAAmmRr6mMW+wF8DL0yyqn8cPOgEM2JJYnQr66rqYn77uZ2zMhBLEu41IUnNudeEJDVmRixJjZkRS1JjbgwvSY1ZmpCkxsqMWJLa8uGhktTYqJY4PxwGYknCjFiSmpucskYsSU05a0KSGrNGLEmNWSOWpMbMiCWpMW/WSVJjliYkqTFLE5LUmNtgSlJjziOWpMbMiCWpsamG22AuatazJI2Rqhr6mE2SzyS5NcnVw/RtIJYkRhuIgc8CLx62bwOxJAE1h2PWa1VdCNwxbN9pOXdOw0myvKomWo9D48Xfi3aSLAeWT/toYt1/F0l2Bc6qqj1nvZ6BePwlWVlVy1qPQ+PF34vxNpdAbGlCkhozEEtSYwbi+cE6oNbH34sxleQ04LvAHkl+muQNA9tbI5aktsyIJakxA7EkNWYgHhNJHpfkC0l+kuSaJP+a5MkztN0qyZs39Bi14SSZTLJq2rFrku/Mcs49G2p8Gi1rxGMgSYDvAJ+rqk/2P9sL2KKqLlpP+10Zcn6i5qck91TV5l2fo/FgRjwe9gdWrw3CAFW1CrgyybeSXJHk+0kO6X/9j8AT+5nSRxqMVw2szXiT7Jjkwv6//6uT/Om0Nv+Q5HtJLkmyQ7vRai7MiMdAkrcAu1XV0et8vhGwWVXdlWQ74BLgScAumBEvaEkmge/3315XVYeuzXiTvB1YUlX/kGQxvd+Ru5MU8PKqOjPJh4G7quoDrX4GDc/9iMdbgP+RZD9gCtgZMMt5dLi/qvaa4bvLgM8keQzwL/0/PQE8CJzVf305cFCnI9TIWJoYDz8AnrWezw8DlgLP6v9HeQuwZAOOS2Oov7PXfsBNwOeTHNH/anX95o+4k5hozRsG4vFwHrBJkiPXfpDk2fRKELdW1eok+/ffA9wNbLHhh6lxkGTt78XxwKeBZzYekh4hA/EY6GcxhwIH9aev/QB4L/CvwLIkK+llxz/qt78d+Lf+jRpv1j36vABYleRK4BXAsW2Ho0fKm3WS1JgZsSQ1ZiCWpMYMxJLUmIFYkhozEEtSYwZidWLa7mFXJ/lyks0ewbU+m+Qv+69PSPLUAW1fkGSfh9HH9f1l5NIGZyBWV+6vqr36+2E8CPzt9C/7eyTMWVX9TVVdM6DJC4A5B2KpJQOxNoSLgD/oZ6vnJzkV+H6SxUk+kuSyJFcleSP0tgVN8r/7+zJ/Ddh+7YWSXJBkWf/1i/s7032vv0vdrvQC/tH9bPxPkyxNcnq/j8uS7Ns/d9sk5yS5Msmn6O3rITXhWnR1qr+D3EuAb/Q/eg6wZ1Vdl2Q5cGdVPTvJJvRWC54D7A3sAfwRvU2OrgE+s851lwLHA/v1r7VNVd2R5JPAPVX10X67U4H/WVUXJ3kCcDbwh8B7gIur6v1JXgos7/QfhDSAgVhd2TTJqv7ri+jtibAP8O9VdV3/8xcBT19b/wV+j942n/sBp1XVJHBzkvPWc/3nAheuvVZV3THDOA4Entrbex+ALZNs0e/jL/rnfi3JLx/ejyk9cgZideV3tnHsB8N7p38EHFVVZ6/T7mBgtrX3GaIN9Mpvf1JV969nLK7v11iwRqyWzgbe1N9XlyRPTvJY4ELgVf0a8o70nmCyru8Cz0+yW//cbfqfr7sz3TnAf1n7pv8IKvp9HNb/7CXA1qP6oaS5MhCrpRPo1X+vSHI18Cl6f0pbAfyY3hMqPgF8e90Tq+oX9Oq6ZyT5HvDF/ldnAoeuvVkHvIXeDnZXJbmG38zeeB+wX5Ir6JVIbuzoZ5Rm5e5rktSYGbEkNWYglqTGDMSS1JiBWJIaMxBLUmMGYklqzEAsSY39f1FB6mkRgAXFAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sn.heatmap(confusion_matrix, annot=True)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 12, "id": "ff05ac96", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predicted Cat Fish All\n", "Actual \n", "Cat 5 1 6\n", "Fish 8 2 10\n", "All 13 3 16\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "confusion_matrix = pd.crosstab(df['y_true'], df['y_pred'], rownames=['Actual'], colnames=['Predicted'], margins = True)\n", "print(confusion_matrix)\n", "sn.heatmap(confusion_matrix, annot=True)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 14, "id": "15286801", "metadata": {}, "outputs": [], "source": [ "H = \"Hen\"\n", "\n", "# True values\n", "y_actual = [C, C, C, C, C, C, F, F, F, F, F, F, F, F, F, F, H, H, H, H, H, H, H, H, H]\n", "# Predicted values\n", "y_predicted = [C, C, C, C, H, F, C, C, C, C, C, C, H, H, F, F, C, C, C, H, H, H, H, H, H]" ] }, { "cell_type": "code", "execution_count": 15, "id": "d0563070", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[4 1 1]\n", " [6 2 2]\n", " [3 0 6]]\n", " precision recall f1-score support\n", "\n", " Cat 0.308 0.667 0.421 6\n", " Fish 0.667 0.200 0.308 10\n", " Hen 0.667 0.667 0.667 9\n", "\n", " accuracy 0.480 25\n", " macro avg 0.547 0.511 0.465 25\n", "weighted avg 0.581 0.480 0.464 25\n", "\n" ] } ], "source": [ "print(metrics.confusion_matrix(y_true, y_pred))\n", "print(metrics.classification_report(y_true, y_pred, digits=3))" ] }, { "cell_type": "code", "execution_count": 18, "id": "7a2ac3bc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " y_actual y_predicted\n", "0 Cat Cat\n", "1 Cat Cat\n", "2 Cat Cat\n", "3 Cat Cat\n", "4 Cat Hen\n", "5 Cat Fish\n", "6 Fish Cat\n", "7 Fish Cat\n", "8 Fish Cat\n", "9 Fish Cat\n", "10 Fish Cat\n", "11 Fish Cat\n", "12 Fish Hen\n", "13 Fish Hen\n", "14 Fish Fish\n", "15 Fish Fish\n", "16 Hen Cat\n", "17 Hen Cat\n", "18 Hen Cat\n", "19 Hen Hen\n", "20 Hen Hen\n", "21 Hen Hen\n", "22 Hen Hen\n", "23 Hen Hen\n", "24 Hen Hen\n" ] } ], "source": [ "data = {'y_actual': [C, C, C, C, C, C, F, F, F, F, F, F, F, F, F, F, H, H, H, H, H, H, H, H, H],\n", " 'y_predicted': [C, C, C, C, H, F, C, C, C, C, C, C, H, H, F, F, C, C, C, H, H, H, H, H, H]}\n", "df = pd.DataFrame(data)\n", "print(df)" ] }, { "cell_type": "code", "execution_count": 19, "id": "28913157", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predicted Cat Fish Hen\n", "Actual \n", "Cat 4 1 1\n", "Fish 6 2 2\n", "Hen 3 0 6\n" ] } ], "source": [ "confusion_matrix = pd.crosstab(df['y_actual'], df['y_predicted'], rownames=['Actual'], colnames=['Predicted'])\n", "print(confusion_matrix)" ] }, { "cell_type": "code", "execution_count": 20, "id": "a0acca2a", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sn.heatmap(confusion_matrix, annot=True)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "6d5ef0e3", "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 }