{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Autour du Paradoxe de Simpson" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn import metrics\n", "from sklearn.metrics import accuracy_score" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En 1972-1974, à Whickham, une ville du nord-est de l'Angleterre, située à environ 6,5 kilomètres au sud-ouest de Newcastle upon Tyne, un sondage d'un sixième des électeurs a été effectué afin d'éclairer des travaux sur les maladies thyroïdiennes et cardiaques (Tunbridge et al. 1977). Une suite de cette étude a été menée vingt ans plus tard (Vanderpump et al. 1995). Certains des résultats avaient trait au tabagisme et cherchaient à savoir si les individus étaient toujours en vie lors de la seconde étude. Par simplicité, nous nous restreindrons aux femmes et parmi celles-ci aux 1314 qui ont été catégorisées comme \"fumant actuellement\" ou \"n'ayant jamais fumé\". Il y avait relativement peu de femmes dans le sondage initial ayant fumé et ayant arrêté depuis (162) et très peu pour lesquelles l'information n'était pas disponible (18). La survie à 20 ans a été déterminée pour l'ensemble des femmes du premier sondage." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données de ces études sont disponibles sur le gitlab de l'inria dans un [document csv](https://gitlab.inria.fr/learninglab/mooc-rr/mooc-rr-ressources/-/blob/master/module3/Practical_session/Subject6_smoking.csv). Dans ce document, chaque ligne indique si la personne fume ou non, si elle est vivante ou décédée au moment de la seconde étude, et son âge lors du premier sondage. Nous téléchargeons toujours l'ensemble complet des données du document.\n" ] }, { "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": "markdown", "metadata": {}, "source": [ "Pour nous protéger contre une éventuelle disparition ou modification du serveur du gitlab, nous faisons une copie locale de ce jeux de données que nous préservons avec notre analyse. Il est inutile et même risquée de télécharger les données à chaque exécution, car dans le cas d'une panne nous pourrions remplacer nos données par un fichier défectueux. Pour cette raison, nous téléchargeons les données seulement si la copie locale n'existe pas." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "data_file = \"survey-data-subject6.csv\"\n", "\n", "import os\n", "import urllib.request\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le document comporte trois colonnes : la première colonne indique leur habitude de tabagisme, la deuxième renseigne si la personne est vivante ou décédée au moment de la seconde étude et enfin, la troisième colonne indique leur âge lors de la première étude" ] }, { "cell_type": "code", "execution_count": 4, "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": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_file)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour nous assurer que le jeu de données est complet, nous vérifions qu'il n'y a pas d'informations manquantes conernant l'une des personnes du sondage. Après vérification, il n'y a pas de données manquantes." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SmokerStatusAge
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [Smoker, Status, Age]\n", "Index: []" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Effectif et taux de mortalite" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous calculons le nombre total de femmes vivantes et décédées sur la période en fonction de leur habitude de tabagisme" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "alive_and_smoker = 0\n", "alive_and_non_smoker = 0\n", "dead_and_smoker = 0\n", "dead_and_non_smoker = 0\n", "for i in range(len(raw_data)):\n", " if raw_data.iloc[i][0] == \"Yes\":\n", " if raw_data.iloc[i][1] == \"Alive\":\n", " alive_and_smoker += 1\n", " else :\n", " dead_and_smoker += 1\n", " else :\n", " if raw_data.iloc[i][1] == \"Alive\":\n", " alive_and_non_smoker += 1\n", " else :\n", " dead_and_non_smoker += 1\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "D'apres nos calculs, dans l'etude il y avait 582 fumeuses dont 139 sont mortes et 732 non-fumeuses dont 230 sont decedees. Nous représentons ensuite ces données sous la forme d'un tableau." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SmokerNon-SmokerTotal
Alive443502945
Dead139230369
Total5827321314
\n", "
" ], "text/plain": [ " Smoker Non-Smoker Total\n", "Alive 443 502 945\n", "Dead 139 230 369\n", "Total 582 732 1314" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = [[alive_and_smoker,alive_and_non_smoker,(alive_and_smoker+alive_and_non_smoker)],[dead_and_smoker, dead_and_non_smoker,(dead_and_non_smoker+dead_and_smoker)], [(dead_and_smoker+alive_and_smoker),(dead_and_non_smoker+alive_and_non_smoker),(alive_and_smoker+alive_and_non_smoker + dead_and_non_smoker+dead_and_smoker)]]\n", "\n", "pd.DataFrame(data, columns=[\"Smoker\", \"Non-Smoker\", \"Total\"], index = [\"Alive\", \"Dead\",\"Total\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A present, nous allons calculer le taux de mortalite dans chacun de ces deux groupes. Pour cela, nous allons determiner le rapport entre le nombre de femmes décédées dans un groupe et le nombre total de femmes dans ce groupe.\n", "\n", "Le taux de mortalite chez les fumeuses etait de 24% tandis que celui des non-fumeuses etait de 31%. Nous obtenons un resultat assez surprenant car d'apres ces etudes, les femmes non-fumeuses meurent plus que les femmes qui fument, ce qui est contraire aux campagnes de prevention que l'on peut croiser un peu partout." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "mortality_rate_smoker = dead_and_smoker/(alive_and_smoker+dead_and_smoker)\n", "mortality_rate_non_smoker = dead_and_non_smoker /(alive_and_non_smoker + dead_and_non_smoker)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous representons les taux de mortalite calcules precedemment dans un diagramme de barres afin d'illustrer visuellement nos resultats et le fait que, d'apres ces sondages, les femmes qui fument meurent moins que celle qui ne fument pas." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Mortality Rate')" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "mortality_rate = [mortality_rate_smoker,mortality_rate_non_smoker]\n", "smoking = ['smoker', 'non-smoker']\n", "plt.bar(smoking, mortality_rate,color=['#E69F00', '#56B4E9'],width = 0.25)\n", "plt.xticks(smoking)\n", "plt.yticks(mortality_rate)\n", "plt.xlabel('Smoking')\n", "plt.ylabel('Mortality Rate')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Enfin, nous allons estimer le taux de mortalite chez les femmes du au tabagisme a cette epoque sur la population anglaise d'apres les resultats de ces deux etudes. \n", "\n", "Pour faire cela, nous allons calculer des intervalles de confiance a 95% pour chaque categorie (fumeuses et non-fumeuses). La formule generale pour calculer un intervalle de\n", "confiance au niveau de confiance 0,95 est : $ [f-1/\\sqrt{n} ; f+1/\\sqrt{n}]$ si $n\\le30$ et si $nf\\le5$ et $n(1-f)\\le5$ avec $f$ la fréquence observée dans un échantillon de taille $n$. \n", "\n", "Dans notre cas, la frequence observee $f$ correspond au taux de mortalite et la taille $n$ de l'echantillon correspond au nombre de femmes ayant repondu aux sondages soit 1314." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Apres avoir verifie que les conditions pour calculer les intervalles de confiance de chacune des deux categories etaient respectees, nous effectuons les calculs.\n", "\n", "Pour les fumeuses, l'intervalle de confiance a 95% du taux de mortalite chez les femmes est $[0.21 ; 0.27]$. Pour les non-fumeuses, l'intervalle de confiance a 95% du taux de mortalite chez les femmes est $[0.29 ; 0.34]$. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "import math\n", "n = 1314\n", "if (n*mortality_rate_smoker <= 5) and (n*(1-mortality_rate_smoker)<=5):\n", " confidence_interval_smoker_low = mortality_rate_smoker-(1/math.sqrt(n))\n", " confidence_interval_smoker_high = mortality_rate_smoker+(1/math.sqrt(n))\n", "if (n*mortality_rate_non_smoker <= 5) and (n*(1-mortality_rate_non_smoker)<=5):\n", " confidence_interval_non_smoker_low = mortality_rate_non_smoker-(1/math.sqrt(n))\n", " confidence_interval_non_smoker_high = mortality_rate_non_smoker+(1/math.sqrt(n))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Effectif et taux de mortalite par tranches d'age" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous allons reprendre les calculs d'effectif et de taux de mortalite calcules precedemment, mais nous allons les categoriser par tranche d'age. Les femmes ayant participe a ces etudes seront reparties dans quatre categories en fonction de leur age : 18-35 ans, 35-55 ans, 55-64 ans, plus de 65 ans." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "582 732\n" ] } ], "source": [ "#class_18_to_35 = []\n", "#class_35_to_55 = []\n", "#class_55_to_64 = []\n", "#class_over_65 = []\n", "\n", "smoker = []\n", "non_smoker = []\n", "\n", "raw_data[\"Status\"].replace({\"Dead\": \"1\", \"Alive\": \"0\"}, inplace=True)\n", "#raw_data[\"Age\"] = raw_data[\"Age\"].astype(str)\n", "\n", "#raw_data\n", "\n", "for i in range(len(raw_data)):\n", " if raw_data.iloc[i][0] == \"Yes\":\n", " smoker.append(raw_data.iloc[i])\n", " else :\n", " non_smoker.append(raw_data.iloc[i])\n", " #if raw_data.iloc[i][2] < 35:\n", " # class_18_to_35.append(raw_data.iloc[i])\n", " #elif 35 <= raw_data.iloc[i][2] < 55:\n", " # class_35_to_55.append(raw_data.iloc[i])\n", " #elif 55 <= raw_data.iloc[i][2] < 65 :\n", " # class_55_to_64.append(raw_data.iloc[i])\n", " #else :\n", " # class_over_65.append(raw_data.iloc[i])\n", "print(len(smoker), len(non_smoker))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "alive_and_smoker_18to35 = 0\n", "dead_and_smoker_18to35 = 0\n", "alive_and_smoker_35to55 = 0\n", "dead_and_smoker_35to55 = 0\n", "alive_and_smoker_55to64 = 0\n", "dead_and_smoker_55to64 = 0\n", "alive_and_smoker_over65 = 0\n", "dead_and_smoker_over65 = 0\n", "\n", "for i in range(len(smoker)):\n", " if smoker[i][1] == \"0\" :\n", " if smoker[i][2] < 35:\n", " alive_and_smoker_18to35 += 1\n", " elif 35 <= smoker[i][2] < 55:\n", " alive_and_smoker_35to55 += 1\n", " elif 55 <= smoker[i][2] < 65 :\n", " alive_and_smoker_55to64 += 1\n", " else :\n", " alive_and_smoker_over65 += 1\n", " else :\n", " if smoker[i][2] < 35:\n", " dead_and_smoker_18to35 += 1\n", " elif 35 <= smoker[i][2] < 55:\n", " dead_and_smoker_35to55 += 1\n", " elif 55 <= smoker[i][2] < 65 :\n", " dead_and_smoker_55to64 += 1\n", " else :\n", " dead_and_smoker_over65 += 1\n", " \n", "alive_and_non_smoker_18to35 = 0\n", "dead_and_non_smoker_18to35 = 0\n", "alive_and_non_smoker_35to55 = 0\n", "dead_and_non_smoker_35to55 = 0\n", "alive_and_non_smoker_55to64 = 0\n", "dead_and_non_smoker_55to64 = 0\n", "alive_and_non_smoker_over65 = 0\n", "dead_and_non_smoker_over65 = 0\n", " \n", "for i in range(len(non_smoker)):\n", " if non_smoker[i][1] == \"0\" :\n", " if non_smoker[i][2] < 35:\n", " alive_and_non_smoker_18to35 += 1\n", " elif 35 <= non_smoker[i][2] < 55:\n", " alive_and_non_smoker_35to55 += 1\n", " elif 55 <= non_smoker[i][2] < 65 :\n", " alive_and_non_smoker_55to64 += 1\n", " else :\n", " alive_and_non_smoker_over65 += 1\n", " else :\n", " if non_smoker[i][2] < 35:\n", " dead_and_non_smoker_18to35 += 1\n", " elif 35 <= non_smoker[i][2] < 55:\n", " dead_and_non_smoker_35to55 += 1\n", " elif 55 <= non_smoker[i][2] < 65 :\n", " dead_and_non_smoker_55to64 += 1\n", " else :\n", " dead_and_non_smoker_over65 += 1\n", " \n", " \n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Dead763919514042165
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" ], "text/plain": [ " Smoker [18-35] Non-Smoker [18-35] Smoker [35-55] Non-Smoker [35-55] \\\n", "Alive 182 221 190 172 \n", "Dead 7 6 39 19 \n", "\n", " Smoker [55-64] Non-Smoker [55-64] Smoker [65+] Non-Smoker [65+] \n", "Alive 64 81 7 28 \n", "Dead 51 40 42 165 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_18to35 = [[alive_and_smoker_18to35,alive_and_non_smoker_18to35],[dead_and_smoker_18to35, dead_and_non_smoker_18to35]]\n", "data_35to55 = [[alive_and_smoker_35to55,alive_and_non_smoker_35to55],[dead_and_smoker_35to55, dead_and_non_smoker_35to55]]\n", "data_55to64 = [[alive_and_smoker_55to64,alive_and_non_smoker_55to64],[dead_and_smoker_55to64, dead_and_non_smoker_55to64]]\n", "data_over65 = [[alive_and_smoker_over65,alive_and_non_smoker_over65],[dead_and_smoker_over65, dead_and_non_smoker_over65]]\n", "\n", "df1 = pd.DataFrame(data_18to35, columns=[\"Smoker [18-35]\", \"Non-Smoker [18-35]\"], index = [\"Alive\", \"Dead\"])\n", "df2 = pd.DataFrame(data_35to55, columns=[\"Smoker [35-55]\", \"Non-Smoker [35-55]\"], index = [\"Alive\", \"Dead\"])\n", "df3 = pd.DataFrame(data_55to64, columns=[\"Smoker [55-64]\", \"Non-Smoker [55-64]\"], index = [\"Alive\", \"Dead\"])\n", "df4 = pd.DataFrame(data_over65, columns=[\"Smoker [65+]\", \"Non-Smoker [65+]\"], index = [\"Alive\", \"Dead\"])\n", "\n", "df_total = pd.concat([df1,df2,df3,df4],axis=1)\n", "df_total" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.037037037037037035 0.02643171806167401\n", "0.1703056768558952 0.09947643979057591\n", "0.4434782608695652 0.3305785123966942\n", "0.8571428571428571 0.8549222797927462\n" ] } ], "source": [ "mortality_rate_smoker_18to35 = dead_and_smoker_18to35/(alive_and_smoker_18to35 + dead_and_smoker_18to35)\n", "mortality_rate_non_smoker_18to35 = dead_and_non_smoker_18to35/(alive_and_non_smoker_18to35 + dead_and_non_smoker_18to35)\n", "\n", "mortality_rate_smoker_35to55 = dead_and_smoker_35to55/(alive_and_smoker_35to55 + dead_and_smoker_35to55)\n", "mortality_rate_non_smoker_35to55 = dead_and_non_smoker_35to55/(alive_and_non_smoker_35to55 + dead_and_non_smoker_35to55)\n", "\n", "mortality_rate_smoker_55to64 = dead_and_smoker_55to64/(alive_and_smoker_55to64 + dead_and_smoker_55to64)\n", "mortality_rate_non_smoker_55to64 = dead_and_non_smoker_55to64/(alive_and_non_smoker_55to64 + dead_and_non_smoker_55to64)\n", "\n", "mortality_rate_smoker_over65 = dead_and_smoker_over65/(alive_and_smoker_over65 + dead_and_smoker_over65)\n", "mortality_rate_non_smoker_over65 = dead_and_non_smoker_over65/(alive_and_non_smoker_over65 + dead_and_non_smoker_over65)\n", "\n", "print(mortality_rate_smoker_18to35,mortality_rate_non_smoker_18to35)\n", "print(mortality_rate_smoker_35to55,mortality_rate_non_smoker_35to55)\n", "print(mortality_rate_smoker_55to64,mortality_rate_non_smoker_55to64)\n", "print(mortality_rate_smoker_over65,mortality_rate_non_smoker_over65)\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "mortality_rate_smoker = (mortality_rate_smoker_18to35,mortality_rate_smoker_35to55,mortality_rate_smoker_55to64,mortality_rate_smoker_over65)\n", "mortality_rate_non_smoker = (mortality_rate_non_smoker_18to35,mortality_rate_non_smoker_35to55,mortality_rate_non_smoker_55to64,mortality_rate_non_smoker_over65)\n", "age = ['18-35','35-55','55-64','65+']\n", "indices = range(len(mortality_rate_smoker))\n", "width = np.min(np.diff(indices))/3.\n", "\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "ax.bar(indices-width/2.,mortality_rate_smoker,width,color='#E69F00',label='Smoker')\n", "ax.bar(indices+width/2.,mortality_rate_non_smoker,width,color='#56B4E9',label='Non-Smoker')\n", "#tiks = ax.get_xticks().tolist()\n", "plt.xticks(indices + width / 2, age)\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Regression logistique" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Status Smoker\n", "0 No 502\n", " Yes 443\n", "1 No 230\n", " Yes 139\n", "dtype: int64\n" ] } ], "source": [ "#raw_data[\"Status\"].replace({\"Dead\": \"1\", \"Alive\": \"0\"}, inplace=True)\n", "#raw_data\n", "\n", "count = raw_data.groupby(['Status', 'Smoker']).size() \n", "print(count)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.countplot(x='Status',hue='Smoker',data=raw_data, palette=[\"#E69F00\",\"#56B4E9\"])" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "raw_data[\"Status\"] = raw_data[\"Status\"].astype(int)\n", "\n", "df_smoker = raw_data[raw_data['Smoker'] == 'Yes']\n", "df_non_smoker = raw_data[raw_data['Smoker'] == 'No']\n", " \n", "df_smoker.plot(kind='scatter',x='Age',y='Status',color='#E69F00')\n", "df_non_smoker.plot(kind='scatter',x='Age',y='Status',color='#56B4E9')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x = df_smoker['Age']\n", "y = df_smoker['Status']\n", "\n", "sns.regplot(x='Age', y='Status', data=df_smoker, logistic=True)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "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" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "X = df_smoker['Age']\n", "y = df_smoker['Status']\n", "\n", "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=0)\n", "\n", "X_train= X_train.values.reshape(-1, 1)\n", "y_train= y_train.values.reshape(-1, 1)\n", "X_test = X_test.values.reshape(-1, 1)\n", "\n", "logistic_regression = LogisticRegression()\n", "logistic_regression.fit(X_train,y_train)\n", "y_pred = logistic_regression.predict(X_test)\n", "confusion_matrix = pd.crosstab(y_test,y_pred, rownames=['Actual'],colnames=['Predicted'])\n", "sns.heatmap(confusion_matrix, annot=True)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(X,y)\n", "plt.scatter(X.values.reshape(-1, 1),logistic_regression.predict_proba(X.values.reshape(-1, 1))[:,1])\n", "plt.show()" ] }, { "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 }