From 1e241dd3729f2c28d190f8a2417a6f4cb43fec9d Mon Sep 17 00:00:00 2001 From: 19cb9366d9071c7a3249219d01022f01 <19cb9366d9071c7a3249219d01022f01@app-learninglab.inria.fr> Date: Sun, 16 Oct 2022 18:33:15 +0000 Subject: [PATCH] Paradoxe_de_Sympson.ipynb --- module3/exo3/Paradoxe_de_Sympson.ipynb | 1182 ++++++++++++++++++++++++ 1 file changed, 1182 insertions(+) create mode 100644 module3/exo3/Paradoxe_de_Sympson.ipynb diff --git a/module3/exo3/Paradoxe_de_Sympson.ipynb b/module3/exo3/Paradoxe_de_Sympson.ipynb new file mode 100644 index 0000000..b03fa30 --- /dev/null +++ b/module3/exo3/Paradoxe_de_Sympson.ipynb @@ -0,0 +1,1182 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Autour du paradoxe de Simpson" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import roc_auc_score \n", + "from sklearn.metrics import roc_curve\n", + "import pandas as pd\n", + "import numpy as np\n", + "import isoweek" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"https://gitlab.inria.fr/learninglab/mooc-rr/mooc-rr-ressources/-/raw/master/module3/Practical_session/Subject6_smoking.csv\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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SmokerStatusAge
0YesAlive21.0
1YesAlive19.3
2NoDead57.5
3NoAlive47.1
4YesAlive81.4
5NoAlive36.8
6NoAlive23.8
7YesDead57.5
8YesAlive24.8
9YesAlive49.5
10YesAlive30.0
11NoDead66.0
12YesAlive49.2
13NoAlive58.4
14NoDead60.6
15NoAlive25.1
16NoAlive43.5
17NoAlive27.1
18NoAlive58.3
19YesAlive65.7
20NoDead73.2
21YesAlive38.3
22NoAlive33.4
23YesDead62.3
24NoAlive18.0
25NoAlive56.2
26YesAlive59.2
27NoAlive25.8
28NoDead36.9
29NoAlive20.2
............
1284YesDead36.0
1285YesAlive48.3
1286NoAlive63.1
1287NoAlive60.8
1288YesDead39.3
1289NoAlive36.7
1290NoAlive63.8
1291NoDead71.3
1292NoAlive57.7
1293NoAlive63.2
1294NoAlive46.6
1295YesDead82.4
1296YesAlive38.3
1297YesAlive32.7
1298NoAlive39.7
1299YesDead60.0
1300NoDead71.0
1301NoAlive20.5
1302NoAlive44.4
1303YesAlive31.2
1304YesAlive47.8
1305YesAlive60.9
1306NoDead61.4
1307YesAlive43.0
1308NoAlive42.1
1309YesAlive35.9
1310NoAlive22.3
1311YesDead62.1
1312NoDead88.6
1313NoAlive39.1
\n", + "

1314 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " Smoker Status Age\n", + "0 Yes Alive 21.0\n", + "1 Yes Alive 19.3\n", + "2 No Dead 57.5\n", + "3 No Alive 47.1\n", + "4 Yes Alive 81.4\n", + "5 No Alive 36.8\n", + "6 No Alive 23.8\n", + "7 Yes Dead 57.5\n", + "8 Yes Alive 24.8\n", + "9 Yes Alive 49.5\n", + "10 Yes Alive 30.0\n", + "11 No Dead 66.0\n", + "12 Yes Alive 49.2\n", + "13 No Alive 58.4\n", + "14 No Dead 60.6\n", + "15 No Alive 25.1\n", + "16 No Alive 43.5\n", + "17 No Alive 27.1\n", + "18 No Alive 58.3\n", + "19 Yes Alive 65.7\n", + "20 No Dead 73.2\n", + "21 Yes Alive 38.3\n", + "22 No Alive 33.4\n", + "23 Yes Dead 62.3\n", + "24 No Alive 18.0\n", + "25 No Alive 56.2\n", + "26 Yes Alive 59.2\n", + "27 No Alive 25.8\n", + "28 No Dead 36.9\n", + "29 No Alive 20.2\n", + "... ... ... ...\n", + "1284 Yes Dead 36.0\n", + "1285 Yes Alive 48.3\n", + "1286 No Alive 63.1\n", + "1287 No Alive 60.8\n", + "1288 Yes Dead 39.3\n", + "1289 No Alive 36.7\n", + "1290 No Alive 63.8\n", + "1291 No Dead 71.3\n", + "1292 No Alive 57.7\n", + "1293 No Alive 63.2\n", + "1294 No Alive 46.6\n", + "1295 Yes Dead 82.4\n", + "1296 Yes Alive 38.3\n", + "1297 Yes Alive 32.7\n", + "1298 No Alive 39.7\n", + "1299 Yes Dead 60.0\n", + "1300 No Dead 71.0\n", + "1301 No Alive 20.5\n", + "1302 No Alive 44.4\n", + "1303 Yes Alive 31.2\n", + "1304 Yes Alive 47.8\n", + "1305 Yes Alive 60.9\n", + "1306 No Dead 61.4\n", + "1307 Yes Alive 43.0\n", + "1308 No Alive 42.1\n", + "1309 Yes Alive 35.9\n", + "1310 No Alive 22.3\n", + "1311 Yes Dead 62.1\n", + "1312 No Dead 88.6\n", + "1313 No Alive 39.1\n", + "\n", + "[1314 rows x 3 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv(data_url)\n", + "raw_data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data = raw_data.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fumeuse_vivante 443\n", + "fumeuse_decedee 139\n", + "non_fumeuse_vivante 502\n", + "non_fumeuse_decedee 230\n", + "nb de fumeuses 582\n", + "nb de non fumeuses 732\n", + "nb femmes 1314\n" + ] + } + ], + "source": [ + "fumeuse_vivante=0\n", + "fumeuse_decedee=0\n", + "non_fumeuse_vivante=0\n", + "non_fumeuse_decedee=0\n", + "\n", + "\n", + "for i in range(1314):\n", + " if (data['Smoker'][i]== 'Yes') and (data['Status'][i] == 'Alive'): # si elle est fumeuse et qu'elle est vivante \n", + " fumeuse_vivante= fumeuse_vivante+1\n", + " \n", + " elif (data['Smoker'][i]== 'Yes') and (data['Status'][i] == 'Dead'): # si elle est fumeuse et qu'elle est décédée \n", + " fumeuse_decedee= fumeuse_decedee+1 \n", + " \n", + " elif (data['Smoker'][i]== 'No') and (data['Status'][i] == 'Alive'): # si elle est non fumeuse et qu'elle est vivante \n", + " non_fumeuse_vivante= non_fumeuse_vivante+1\n", + " \n", + " elif (data['Smoker'][i]== 'No') and (data['Status'][i] == 'Dead'): # si elle est non fumeuse et qu'elle est décédée\n", + " non_fumeuse_decedee= non_fumeuse_decedee+1\n", + " \n", + "print('fumeuse_vivante',fumeuse_vivante)\n", + "print('fumeuse_decedee',fumeuse_decedee)\n", + "print('non_fumeuse_vivante',non_fumeuse_vivante)\n", + "print('non_fumeuse_decedee',non_fumeuse_decedee)\n", + "\n", + "print('nb de fumeuses', fumeuse_vivante+ fumeuse_decedee)\n", + "print('nb de non fumeuses', non_fumeuse_vivante+ non_fumeuse_decedee)\n", + "\n", + "\n", + "\n", + "\n", + "nb_femmes= fumeuse_vivante+fumeuse_decedee+non_fumeuse_vivante+non_fumeuse_decedee\n", + "print('nb femmes', nb_femmes)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Effectif dans chaque catégorie \n", + "Nombres = {\"Smoker_Alive\": fumeuse_vivante, \"Smoker_Dead\": fumeuse_decedee, \"No_Smoker_Alive\": non_fumeuse_vivante, \"No_Smoker_Dead\": non_fumeuse_decedee}\n", + "\n", + "x = [1,2,3,4]\n", + "height = [fumeuse_vivante, fumeuse_decedee, non_fumeuse_vivante, non_fumeuse_decedee]\n", + "width = 0.05\n", + "BarName = ['Fumeuse vivante', 'Fumeuse décédée', 'Non fumeuse vivante', 'Non fumeuse décédée']\n", + "\n", + "plt.bar(x, height , width )\n", + "\n", + "plt.xlim(0,4.5)\n", + "plt.ylim(0,510)\n", + "plt.ylabel('Effectifs ')\n", + "plt.xticks(x, BarName, rotation=40)\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "taux de mortalité des fumeuses 0.23883161512027493\n", + "taux de mortalité des non fumeuses 0.31420765027322406\n" + ] + } + ], + "source": [ + "#Taux de mortalité\n", + "\n", + "taux_mortalite_fumeuse= Nombres[\"Smoker_Dead\"]/ (Nombres[\"Smoker_Dead\"]+Nombres[\"Smoker_Alive\"])\n", + "taux_mortalite_non_fumeuse= Nombres[\"No_Smoker_Dead\"]/ (Nombres[\"No_Smoker_Dead\"]+Nombres[\"No_Smoker_Alive\"])\n", + "\n", + "print('taux de mortalité des fumeuses',taux_mortalite_fumeuse)\n", + "print('taux de mortalité des non fumeuses',taux_mortalite_non_fumeuse)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x = [1,2]\n", + "height = [taux_mortalite_fumeuse, taux_mortalite_non_fumeuse]\n", + "width = 0.05\n", + "BarName = ['Fumeuse', 'Non fumeuse']\n", + "\n", + "plt.bar(x, height , width )\n", + "\n", + "plt.xlim(0,2.5)\n", + "plt.ylim(0,0.35)\n", + "\n", + "plt.ylabel('Taux de mortalité ')\n", + "\n", + "plt.xticks(x, BarName, rotation=40)\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intervalle de confiance des fumeuses: [0.21124475 0.26641848]\n", + "Intervalle de confiance des non fumeuses: [0.28662079 0.34179451]\n" + ] + } + ], + "source": [ + "#Intervalles de confiance par rapport aux taux de mortalité\n", + "\n", + "print('Intervalle de confiance des fumeuses:', np.array( [taux_mortalite_fumeuse-1/np.sqrt(nb_femmes), taux_mortalite_fumeuse + 1/np.sqrt(nb_femmes)]))\n", + "\n", + "print('Intervalle de confiance des non fumeuses:', np.array( [taux_mortalite_non_fumeuse-1/np.sqrt(nb_femmes), taux_mortalite_non_fumeuse + 1/np.sqrt(nb_femmes)]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "L'intervalle de confiance de la mortalité chez les non fumeuses a des valeurs plus élévées que celui chez les fumeuses. Intuitivement, on pourrait penser le contraire. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Classes d'âge" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "catégorie1_fumeuse 181\n", + "catégorie2_fumeuse 237\n", + "catégorie3_fumeuse 115\n", + "catégorie4_fumeuse 49\n", + "nb_fumeuse 582\n", + "catégorie1_non_fumeuse 219\n", + "catégorie2_non_fumeuse 199\n", + "catégorie3_non_fumeuse 121\n", + "catégorie4_fumeuse 193\n", + "nb de non fumeuse 732\n" + ] + } + ], + "source": [ + "# Effectifs selon les tranches d'âge \n", + "\n", + "catégorie1_fumeuse=0 #18-34 ans\n", + "catégorie2_fumeuse=0 # 34-54 ans\n", + "catégorie3_fumeuse=0 # 55-64 ans\n", + "catégorie4_fumeuse=0 # plus de 65 ans\n", + "\n", + "\n", + "catégorie1_non_fumeuse=0\n", + "catégorie2_non_fumeuse=0\n", + "catégorie3_non_fumeuse=0\n", + "catégorie4_non_fumeuse=0\n", + "\n", + "\n", + " \n", + "for i in range(1314):\n", + " \n", + " # tranches d'age pour les fumeuses\n", + " if (data['Smoker'][i]== 'Yes') and (18<=data['Age'][i] <=34): # si elle est fumeuse et qu'elle est dans la catégorie 1\n", + " catégorie1_fumeuse= catégorie1_fumeuse+1\n", + " \n", + " elif (data['Smoker'][i]== 'Yes') and (3464): # si elle est fumeuse et qu'elle est dans la catégorie 4\n", + " catégorie4_fumeuse= catégorie4_fumeuse+1 \n", + " \n", + " \n", + " # tranches d'age pour les non fumeuses\n", + " if (data['Smoker'][i]== 'No') and (18<=data['Age'][i] <=34): # si elle est non fumeuse et qu'elle est dans la catégorie 1\n", + " catégorie1_non_fumeuse= catégorie1_non_fumeuse+1\n", + " \n", + " elif (data['Smoker'][i]== 'No') and (3464): # si elle est non fumeuse et qu'elle est dans la catégorie 4\n", + " catégorie4_non_fumeuse= catégorie4_non_fumeuse+1 \n", + " \n", + " \n", + "print('catégorie1_fumeuse',catégorie1_fumeuse)\n", + "print('catégorie2_fumeuse',catégorie2_fumeuse)\n", + "print('catégorie3_fumeuse',catégorie3_fumeuse)\n", + "print('catégorie4_fumeuse',catégorie4_fumeuse)\n", + "\n", + "print('nb_fumeuse',catégorie1_fumeuse+catégorie2_fumeuse+catégorie3_fumeuse+catégorie4_fumeuse)\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "print('catégorie1_non_fumeuse',catégorie1_non_fumeuse)\n", + "print('catégorie2_non_fumeuse',catégorie2_non_fumeuse)\n", + "print('catégorie3_non_fumeuse',catégorie3_non_fumeuse)\n", + "print('catégorie4_fumeuse',catégorie4_non_fumeuse)\n", + "\n", + "print('nb de non fumeuse',catégorie1_non_fumeuse+catégorie2_non_fumeuse+catégorie3_non_fumeuse+catégorie4_non_fumeuse)\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x = [1,2,3,4]\n", + "height = [catégorie1_fumeuse, catégorie2_fumeuse, catégorie3_fumeuse, catégorie4_fumeuse]\n", + "width = 0.05\n", + "\n", + "BarName = ['18-34 ans', '34-54 ans', '55-64 ans','plus de 65 ans']\n", + "\n", + "plt.bar(x, height , width )\n", + "\n", + "plt.xlim(0,4.5)\n", + "plt.ylim(0,240)\n", + "#plt.grid()\n", + "\n", + "plt.ylabel('Effectifs')\n", + "plt.title(\"Effectifs des fumeuses selon la tranche d'âge \")\n", + "\n", + "plt.xticks(x, BarName, rotation=40)\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x = [1,2,3,4]\n", + "height = [catégorie1_non_fumeuse, catégorie2_non_fumeuse, catégorie3_non_fumeuse, catégorie4_non_fumeuse]\n", + "width = 0.05\n", + "\n", + "BarName = ['18-34 ans', '34-54 ans', '55-64 ans','plus de 65 ans']\n", + "\n", + "plt.bar(x, height , width )\n", + "\n", + "plt.xlim(0,4.5)\n", + "plt.ylim(0,225)\n", + "\n", + "plt.ylabel('Effectifs')\n", + "plt.title(\"Effectifs des non fumeuses selon la tranche d'âge \")\n", + "\n", + "plt.xticks(x, BarName, rotation=40)\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fumeuse décédée dans la catégorie 1 5\n", + "fumeuse décédée dans la catégorie 2 41\n", + "fumeuse décédée dans la catégorie 3 51\n", + "fumeuse décédée dans la catégorie 4 42\n", + "non fumeuse décédée dans la catégorie 1 6\n", + "non fumeuse décédée dans la catégorie 2 19\n", + "non fumeuse décédée dans la catégorie 3 40\n", + "non fumeuse décédée dans la catégorie 4 165\n" + ] + } + ], + "source": [ + "# Décès des fumeuses et des non fumeuses selon la tranche d'âge\n", + "\n", + "fumeuse_decedee1=0 # fumeuse décédée de la premiere tranche d'age\n", + "fumeuse_decedee2=0 # fumeuse décédée de la premiere tranche d'age \n", + "fumeuse_decedee3=0 # fumeuse décédée de la premiere tranche d'age\n", + "fumeuse_decedee4=0 # fumeuse décédée de la premiere tranche d'age\n", + "\n", + "\n", + "non_fumeuse_decedee1=0 \n", + "non_fumeuse_decedee2=0 \n", + "non_fumeuse_decedee3=0 \n", + "non_fumeuse_decedee4=0 \n", + "\n", + "\n", + " \n", + "for i in range(1314):\n", + " \n", + " # fumeuses décédée selon la tranche d'âge \n", + " if (data['Smoker'][i]== 'Yes') and (data['Status'][i]== 'Dead') and (18<=data['Age'][i] <=34): # si elle est fumeuse et qu'elle est dans la catégorie 1\n", + " fumeuse_decedee1= fumeuse_decedee1+1\n", + " \n", + " elif (data['Smoker'][i]== 'Yes') and (data['Status'][i]== 'Dead') and (3464): # si elle est fumeuse et qu'elle est dans la catégorie 4\n", + " fumeuse_decedee4= fumeuse_decedee4+1 \n", + " \n", + " \n", + " # tranches d'age pour les non fumeuses\n", + " if (data['Smoker'][i]== 'No') and (data['Status'][i]== 'Dead') and (18<=data['Age'][i] <=34): # si elle est non fumeuse et qu'elle est dans la catégorie 1\n", + " non_fumeuse_decedee1= non_fumeuse_decedee1+1\n", + " \n", + " elif (data['Smoker'][i]== 'No') and (data['Status'][i]== 'Dead') and (3464): # si elle est non fumeuse et qu'elle est dans la catégorie 4\n", + " non_fumeuse_decedee4= non_fumeuse_decedee4+1 \n", + " \n", + " \n", + "print('fumeuse décédée dans la catégorie 1',fumeuse_decedee1)\n", + "print('fumeuse décédée dans la catégorie 2',fumeuse_decedee2)\n", + "print('fumeuse décédée dans la catégorie 3',fumeuse_decedee3)\n", + "print('fumeuse décédée dans la catégorie 4',fumeuse_decedee4)\n", + "\n", + "print('non fumeuse décédée dans la catégorie 1',non_fumeuse_decedee1)\n", + "print('non fumeuse décédée dans la catégorie 2',non_fumeuse_decedee2)\n", + "print('non fumeuse décédée dans la catégorie 3',non_fumeuse_decedee3)\n", + "print('non fumeuse décédée dans la catégorie 4',non_fumeuse_decedee4)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# taux de mortalité des fumeuses et des non fumeuses selon la tranche d'âge \n", + "\n", + "taux1_fumeuse= fumeuse_decedee1 / catégorie1_fumeuse\n", + "taux2_fumeuse= fumeuse_decedee2 / catégorie2_fumeuse\n", + "taux3_fumeuse= fumeuse_decedee3 / catégorie3_fumeuse\n", + "taux4_fumeuse= fumeuse_decedee4 / catégorie4_fumeuse\n", + "\n", + "taux1_non_fumeuse= non_fumeuse_decedee1 / catégorie1_non_fumeuse\n", + "taux2_non_fumeuse= non_fumeuse_decedee2 / catégorie2_non_fumeuse\n", + "taux3_non_fumeuse= non_fumeuse_decedee3 / catégorie3_non_fumeuse\n", + "taux4_non_fumeuse= non_fumeuse_decedee4 / catégorie4_non_fumeuse\n", + "\n", + "\n", + "\n", + "x = [1, 2, 4, 5 ,7, 8, 10, 11]\n", + "height = [taux1_fumeuse, taux1_non_fumeuse, taux2_fumeuse,taux2_non_fumeuse,taux3_fumeuse, taux3_non_fumeuse, taux4_fumeuse,taux4_non_fumeuse]\n", + "width = 0.05\n", + "\n", + "BarName = ['fumeuse','non fumeuse', 'fumeuse','non fumeuse', 'fumeuse','non fumeuse','fumeuse','non fumeuse']\n", + "\n", + "plt.bar(x, height , width )\n", + "\n", + "plt.xlim(0,13)\n", + "plt.ylim(0,1)\n", + "#plt.grid()\n", + "\n", + "plt.ylabel('Taux de mortalité')\n", + "\n", + "plt.xticks(x, BarName, rotation=90)\n", + "\n", + "\n", + "plt.show()\n", + "\n", + "#print(taux1_fumeuse>taux1_non_fumeuse, taux4_fumeuse> taux4_non_fumeuse)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lorsqu'on observait le taux de mortalité entre les fumeuses et les non fumeuses sans prendre en compte de tranche d'age, le taux de mortalité était plus élevé chez les non fumeuses. Cette fois, on prend en compte les tranches d'age et le taux de mortalité est plus élevé chez les fumeuses. Cela peut s'expliquer par le fait que parmis l'effectif des non fumeurs est assez bien répartie sur les différentes tranches d'ages alors que celui des fumeurs est principalement concentrée avant 54 ans. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Régression logistique" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "Death = np.zeros((1314,1))\n", + "\n", + "\n", + "# 1 pour vivant \n", + "for i in range(1314):\n", + " if data['Status'][i]=='Alive':\n", + " Death[i][0]=1\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "Age0= data['Age']\n", + "Age_np=np.zeros((1314,1))\n", + "\n", + "for i in range(1314):\n", + " Age_np[i][0]= Age0[i]\n", + "\n", + "\n", + "plt.scatter(Age_np,Death)\n", + "plt.xlabel('Age')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 58 38]\n", + " [ 16 217]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.6/site-packages/sklearn/utils/validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", + " y = column_or_1d(y, warn=True)\n" + ] + } + ], + "source": [ + "x_train, x_test, y_train, y_test = train_test_split(Age_np, Death, random_state=1)\n", + "logReg= LogisticRegression()\n", + "logReg.fit(x_train,y_train)\n", + "y_pred=logReg.predict(x_test)\n", + "# matrice de confusion \n", + "conf = confusion_matrix(y_test, y_pred)\n", + "print(conf)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# courbes de régression\n", + "\n", + "fpr, tpr, thresholds = roc_curve(y_test, logReg.predict_proba(x_test)[:,1])\n", + "\n", + "\n", + "plt.plot(fpr, tpr)\n", + "\n", + "plt.plot([0, 1], [0, 1])\n", + "\n", + " \n", + "plt.xlim([0.0, 1])\n", + "plt.ylim([0.0, 1.05])\n", + "\n", + "plt.xlabel('Taux de faux positifs')\n", + "plt.ylabel('Taux de vrais positifs')\n", + "plt.title('ROC')\n", + "\n", + " \n", + "plt.show() \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} -- 2.18.1