sujut6

parent f983203d
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Titre du document "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"2+2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10\n"
]
}
],
"source": [
"x=10\n",
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"20\n"
]
}
],
"source": [
"x=x+10\n",
"print(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Petit exemple de completion"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": "r",
"file_extension": ".r",
"mimetype": "text/x-r-source",
"name": "R",
"pygments_lexer": "r",
"version": "3.4.1"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
{ {
"cells": [], "cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sujet 6 : Autour du Paradoxe de Simpson"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Taux de mortalité "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"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": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 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"
]
}
],
"source": [
"df = pd.read_csv (data_url)\n",
"print(df.head())"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Status Alive Dead Total Mortality Rate\n",
"Smoker \n",
"No 502 230 732 0.314208\n",
"Yes 443 139 582 0.238832\n"
]
}
],
"source": [
"summary_table = df.groupby(['Smoker', 'Status']).size().unstack(fill_value=0)\n",
"\n",
"summary_table['Total'] = summary_table.sum(axis=1)\n",
"summary_table['Mortality Rate'] = summary_table['Dead'] / summary_table['Total']\n",
"\n",
"print(summary_table)\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"summary_table.reset_index(inplace=True)\n",
"\n",
"sns.barplot(x='Smoker', y='Mortality Rate', data=summary_table)\n",
"plt.title('Mortality rates for smokers and non-smokers')\n",
"plt.ylabel('Mortality Rate')\n",
"plt.xlabel('Smoking Status')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The graph shows mortality rates for smokers and non-smokers. Mortality appears to be higher in non-smokers than in smokers. This goes against the conventional wisdom that smoking is generally associated with an increased risk of mortality. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Taux de mortalité par tranches d'âge"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Status Alive Dead Total Mortality Rate\n",
"Age Group Smoker \n",
"18-34 No 213 6 219 0.027397\n",
" Yes 174 5 179 0.027933\n",
"35-54 No 180 19 199 0.095477\n",
" Yes 198 41 239 0.171548\n",
"55-64 No 80 39 119 0.327731\n",
" Yes 64 51 115 0.443478\n",
"65+ No 29 166 195 0.851282\n",
" Yes 7 42 49 0.857143\n"
]
}
],
"source": [
"\n",
"age_bins = [18, 34, 54, 64, 100] \n",
"age_labels = ['18-34', '35-54', '55-64', '65+']\n",
"df['Age Group'] = pd.cut(df['Age'], bins=age_bins, labels=age_labels, right=False)\n",
"\n",
"\n",
"age_smoking_table = df.groupby(['Age Group', 'Smoker', 'Status']).size().unstack(fill_value=0)\n",
"\n",
"age_smoking_table['Total'] = age_smoking_table.sum(axis=1)\n",
"age_smoking_table['Mortality Rate'] = age_smoking_table['Dead'] / age_smoking_table['Total']\n",
"\n",
"\n",
"print(age_smoking_table)\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"age_smoking_table.reset_index(inplace=True)\n",
"\n",
"plt.figure(figsize=(10, 6))\n",
"sns.barplot(x='Age Group', y='Mortality Rate', hue='Smoker', data=age_smoking_table)\n",
"plt.title('Mortality rates by age group and smoking status')\n",
"plt.ylabel('Mortality Rate')\n",
"plt.xlabel('Age Group')\n",
"plt.legend(title='Smoking Status')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This graph shows that mortality rates increase with age, for both smokers and non-smokers. Smokers have slightly higher mortality rates in the intermediate age groups (35-64). However, in the 65+ age group, mortality rates are almost identical for smokers and non-smokers, which may be explained by other causes of death. The impact of smoking on mortality appears to be more marked in the middle-aged group, but less so in the elderly."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Régression logistique"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimization terminated successfully.\n",
" Current function value: 0.381244\n",
" Iterations 7\n",
" Logit Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Death No. Observations: 1314\n",
"Model: Logit Df Residuals: 1311\n",
"Method: MLE Df Model: 2\n",
"Date: Sat, 12 Oct 2024 Pseudo R-squ.: 0.3579\n",
"Time: 13:47:48 Log-Likelihood: -500.95\n",
"converged: True LL-Null: -780.16\n",
" LLR p-value: 5.534e-122\n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const -6.3519 0.360 -17.637 0.000 -7.058 -5.646\n",
"Age 0.0998 0.006 17.290 0.000 0.089 0.111\n",
"Smoking 0.2787 0.165 1.689 0.091 -0.045 0.602\n",
"==============================================================================\n"
]
}
],
"source": [
"import pandas as pd\n",
"import statsmodels.api as sm\n",
"\n",
"url = \"https://gitlab.inria.fr/learninglab/mooc-rr/mooc-rr-ressources/-/raw/master/module3/Practical_session/Subject6_smoking.csv\"\n",
"df = pd.read_csv(url)\n",
"\n",
"df['Death'] = df['Status'].apply(lambda x: 1 if x == 'Dead' else 0) \n",
"df['Smoking'] = df['Smoker'].apply(lambda x: 1 if x == 'Yes' else 0) \n",
"\n",
"X = sm.add_constant(df[['Age', 'Smoking']]) \n",
"y = df['Death'] \n",
"\n",
"model = sm.Logit(y, X)\n",
"result = model.fit()\n",
"\n",
"print(result.summary())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3", "display_name": "Python 3",
...@@ -16,10 +288,9 @@ ...@@ -16,10 +288,9 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.3" "version": "3.6.4"
} }
}, },
"nbformat": 4, "nbformat": 4,
"nbformat_minor": 2 "nbformat_minor": 2
} }
{
"cells": [],
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sujet 6 : Autour du Paradoxe de Simpson"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Taux de mortalité "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"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": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 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"
]
}
],
"source": [
"df = pd.read_csv (data_url)\n",
"print(df.head())"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Status Alive Dead Total Mortality Rate\n",
"Smoker \n",
"No 502 230 732 0.314208\n",
"Yes 443 139 582 0.238832\n"
]
}
],
"source": [
"summary_table = df.groupby(['Smoker', 'Status']).size().unstack(fill_value=0)\n",
"\n",
"summary_table['Total'] = summary_table.sum(axis=1)\n",
"summary_table['Mortality Rate'] = summary_table['Dead'] / summary_table['Total']\n",
"\n",
"print(summary_table)\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"summary_table.reset_index(inplace=True)\n",
"\n",
"sns.barplot(x='Smoker', y='Mortality Rate', data=summary_table)\n",
"plt.title('Mortality rates for smokers and non-smokers')\n",
"plt.ylabel('Mortality Rate')\n",
"plt.xlabel('Smoking Status')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Le graphique montre les taux de mortalité pour les fumeurs et les non-fumeurs. La mortalité semble être plus élevée chez les non-fumeurs que chez les fumeurs. Cela va à l'encontre de l'idée reçue que le tabagisme est généralement associé à un risque accru de mortalité. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Taux de mortalité par tranches d'âge"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Status Alive Dead Total Mortality Rate\n",
"Age Group Smoker \n",
"18-34 No 213 6 219 0.027397\n",
" Yes 174 5 179 0.027933\n",
"35-54 No 180 19 199 0.095477\n",
" Yes 198 41 239 0.171548\n",
"55-64 No 80 39 119 0.327731\n",
" Yes 64 51 115 0.443478\n",
"65+ No 29 166 195 0.851282\n",
" Yes 7 42 49 0.857143\n"
]
}
],
"source": [
"\n",
"age_bins = [18, 34, 54, 64, 100] \n",
"age_labels = ['18-34', '35-54', '55-64', '65+']\n",
"df['Age Group'] = pd.cut(df['Age'], bins=age_bins, labels=age_labels, right=False)\n",
"\n",
"\n",
"age_smoking_table = df.groupby(['c', 'Smoker', 'Status']).size().unstack(fill_value=0)\n",
"\n",
"age_smoking_table['Total'] = age_smoking_table.sum(axis=1)\n",
"age_smoking_table['Mortality Rate'] = age_smoking_table['Dead'] / age_smoking_table['Total']\n",
"\n",
"\n",
"print(age_smoking_table)\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"age_smoking_table.reset_index(inplace=True)\n",
"\n",
"plt.figure(figsize=(10, 6))\n",
"sns.barplot(x='Age Group', y='Mortality Rate', hue='Smoker', data=age_smoking_table)\n",
"plt.title('Mortality rates by age group and smoking status')\n",
"plt.ylabel('Mortality Rate')\n",
"plt.xlabel('Age Group')\n",
"plt.legend(title='Smoking Status')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ce graphique montre que les taux de mortalité augmentent avec l'âge, pour les fumeurs comme pour les non-fumeurs. Les fumeurs ont des taux de mortalité légèrement plus élevés dans les groupes d'âge intermédiaires (35-64 ans). Cependant, dans le groupe des 65 ans et plus, les taux de mortalité sont presque identiques pour les fumeurs et les non-fumeurs, ce qui peut s'expliquer par d'autres causes de décès. L'impact du tabagisme sur la mortalité semble plus marqué chez les personnes d'âge moyen, mais diminue chez les personnes âgée"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Régression logistique"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimization terminated successfully.\n",
" Current function value: 0.381244\n",
" Iterations 7\n",
" Logit Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Death No. Observations: 1314\n",
"Model: Logit Df Residuals: 1311\n",
"Method: MLE Df Model: 2\n",
"Date: Sat, 12 Oct 2024 Pseudo R-squ.: 0.3579\n",
"Time: 09:18:51 Log-Likelihood: -500.95\n",
"converged: True LL-Null: -780.16\n",
" LLR p-value: 5.534e-122\n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const -6.3519 0.360 -17.637 0.000 -7.058 -5.646\n",
"Age 0.0998 0.006 17.290 0.000 0.089 0.111\n",
"Smoking 0.2787 0.165 1.689 0.091 -0.045 0.602\n",
"==============================================================================\n"
]
}
],
"source": [
"import pandas as pd\n",
"import statsmodels.api as sm\n",
"\n",
"url = \"https://gitlab.inria.fr/learninglab/mooc-rr/mooc-rr-ressources/-/raw/master/module3/Practical_session/Subject6_smoking.csv\"\n",
"df = pd.read_csv(url)\n",
"\n",
"df['Death'] = df['Status'].apply(lambda x: 1 if x == 'Dead' else 0) \n",
"df['Smoking'] = df['Smoker'].apply(lambda x: 1 if x == 'Yes' else 0) \n",
"\n",
"X = sm.add_constant(df[['Age', 'Smoking']]) \n",
"y = df['Death'] \n",
"\n",
"model = sm.Logit(y, X)\n",
"result = model.fit()\n",
"\n",
"print(result.summary())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"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
}
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