{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Analyse de risque : Navette Challenger" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "D'après l'article servant de base à cet exercice, les auteurs estiment plusieurs paramètres : $s_{\\hat{\\alpha}} = 3.052$ et $s_{\\hat{\\beta}} = 0.047$. La qualité de l'ajustement est caractérisée par un coefficient $G^2 = 18.086$ avec 21 degrés de liberté." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Informations techniques sur la machine et l'installation python" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Importation des librairies" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import sys\n", "import platform\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import statsmodels.api as sm\n", "import seaborn as sn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Détermination de la version des différentes librairies ainsi que de l'OS" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.6.4 |Anaconda, Inc.| (default, Mar 13 2018, 01:15:57) \n", "[GCC 7.2.0]\n", "uname_result(system='Linux', node='d8fc5e21ebcb', release='4.4.0-164-generic', version='#192-Ubuntu SMP Fri Sep 13 12:02:50 UTC 2019', machine='x86_64', processor='x86_64')\n", "IPython \t 7.12.0\n", "IPython.core.release \t 7.12.0\n", "PIL \t 7.0.0\n", "PIL.Image \t 7.0.0\n", "PIL._version \t 7.0.0\n", "_csv \t 1.0\n", "_ctypes \t 1.1.0\n", "_curses \t b'2.2'\n", "decimal \t 1.70\n", "argparse \t 1.1\n", "backcall \t 0.1.0\n", "cffi \t 1.13.2\n", "csv \t 1.0\n", "ctypes \t 1.1.0\n", "cycler \t 0.10.0\n", "dateutil \t 2.8.1\n", "decimal \t 1.70\n", "decorator \t 4.4.1\n", "distutils \t 3.6.4\n", "ipaddress \t 1.0\n", "ipykernel \t 5.1.4\n", "ipykernel._version \t 5.1.4\n", "ipython_genutils \t 0.2.0\n", "ipython_genutils._version \t 0.2.0\n", "ipywidgets \t 7.2.1\n", "ipywidgets._version \t 7.2.1\n", "jedi \t 0.16.0\n", "json \t 2.0.9\n", "jupyter_client \t 6.0.0\n", "jupyter_client._version \t 6.0.0\n", "jupyter_core \t 4.6.3\n", "jupyter_core.version \t 4.6.3\n", "kiwisolver \t 1.1.0\n", "logging \t 0.5.1.2\n", "matplotlib \t 2.2.3\n", "matplotlib.backends.backend_agg \t 2.2.3\n", "numpy \t 1.15.2\n", "numpy.core \t 1.15.2\n", "numpy.core.multiarray \t 3.1\n", "numpy.lib \t 1.15.2\n", "numpy.linalg._umath_linalg \t b'0.1.5'\n", "numpy.matlib \t 1.15.2\n", "optparse \t 1.5.3\n", "pandas \t 0.22.0\n", "_libjson \t 1.33\n", "parso \t 0.6.0\n", "patsy \t 0.5.1\n", "patsy.version \t 0.5.1\n", "pexpect \t 4.8.0\n", "pickleshare \t 0.7.5\n", "platform \t 1.0.8\n", "prompt_toolkit \t 3.0.3\n", "ptyprocess \t 0.6.0\n", "pygments \t 2.5.2\n", "pyparsing \t 2.4.6\n", "pytz \t 2019.3\n", "re \t 2.2.1\n", "scipy \t 1.1.0\n", "scipy._lib.decorator \t 4.0.5\n", "scipy._lib.six \t 1.2.0\n", "scipy.fftpack._fftpack \t b'$Revision: $'\n", "scipy.fftpack.convolve \t b'$Revision: $'\n", "scipy.integrate._dop \t b'$Revision: $'\n", "scipy.integrate._ode \t $Id$\n", "scipy.integrate._odepack \t 1.9 \n", "scipy.integrate._quadpack \t 1.13 \n", "scipy.integrate.lsoda \t b'$Revision: $'\n", "scipy.integrate.vode \t b'$Revision: $'\n", "scipy.interpolate._fitpack \t 1.7 \n", "scipy.interpolate.dfitpack \t b'$Revision: $'\n", "scipy.linalg \t 0.4.9\n", "scipy.linalg._fblas \t b'$Revision: $'\n", "scipy.linalg._flapack \t b'$Revision: $'\n", "scipy.linalg._flinalg \t b'$Revision: $'\n", "scipy.ndimage \t 2.0\n", "scipy.optimize._cobyla \t b'$Revision: $'\n", "scipy.optimize._lbfgsb \t b'$Revision: $'\n", "scipy.optimize._minpack \t 1.10 \n", "scipy.optimize._nnls \t b'$Revision: $'\n", "scipy.optimize._slsqp \t b'$Revision: $'\n", "scipy.optimize.minpack2 \t b'$Revision: $'\n", "scipy.signal.spline \t 0.2\n", "scipy.sparse.linalg.eigen.arpack._arpack \t b'$Revision: $'\n", "scipy.sparse.linalg.isolve._iterative \t b'$Revision: $'\n", "scipy.special.specfun \t b'$Revision: $'\n", "scipy.stats.mvn \t b'$Revision: $'\n", "scipy.stats.statlib \t b'$Revision: $'\n", "seaborn \t 0.8.1\n", "seaborn.external.husl \t 2.1.0\n", "seaborn.external.six \t 1.10.0\n", "six \t 1.14.0\n", "statsmodels \t 0.9.0\n", "statsmodels.__init__ \t 0.9.0\n", "traitlets \t 4.3.3\n", "traitlets._version \t 4.3.3\n", "urllib.request \t 3.6\n", "zlib \t 1.0\n", "zmq \t 17.1.2\n", "zmq.sugar \t 17.1.2\n", "zmq.sugar.version \t 17.1.2\n" ] } ], "source": [ "print(sys.version)\n", "print(platform.uname())\n", "\n", "for name, val in sorted(sys.modules.items()):\n", " try:\n", " print(val.__name__, \"\\t\", val.__version__)\n", " except Exception as e:\n", " pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Début de l'étude" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Chargement des données" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous commençons par charger les données provenant du lien [https://app-learninglab.inria.fr/moocrr/gitlab/moocrr-session3/moocrr-reproducibility-study/raw/master/data/shuttle.csv](https://app-learninglab.inria.fr/moocrr/gitlab/moocrr-session3/moocrr-reproducibility-study/raw/master/data/shuttle.csv). __Soulignons que le lien donné par [l'exemple](https://app-learninglab.inria.fr/moocrr/gitlab/moocrr-session3/moocrr-reproducibility-study/blob/master/src/Python3/challenger.ipynb) ne peut pas être lu correctement par la librairie pandas__." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Date Count Temperature Pressure Malfunction\n", "0 4/12/81 6 66 50 0\n", "1 11/12/81 6 70 50 1\n", "2 3/22/82 6 69 50 0\n", "3 11/11/82 6 68 50 0\n", "4 4/04/83 6 67 50 0\n", "5 6/18/82 6 72 50 0\n", "6 8/30/83 6 73 100 0\n", "7 11/28/83 6 70 100 0\n", "8 2/03/84 6 57 200 1\n", "9 4/06/84 6 63 200 1\n", "10 8/30/84 6 70 200 1\n", "11 10/05/84 6 78 200 0\n", "12 11/08/84 6 67 200 0\n", "13 1/24/85 6 53 200 2\n", "14 4/12/85 6 67 200 0\n", "15 4/29/85 6 75 200 0\n", "16 6/17/85 6 70 200 0\n", "17 7/2903/85 6 81 200 0\n", "18 8/27/85 6 76 200 0\n", "19 10/03/85 6 79 200 0\n", "20 10/30/85 6 75 200 2\n", "21 11/26/85 6 76 200 0\n", "22 1/12/86 6 58 200 1\n" ] } ], "source": [ "d = pd.read_csv(\"https://app-learninglab.inria.fr/moocrr/gitlab/moocrr-session3/moocrr-reproducibility-study/raw/master/data/shuttle.csv\")\n", "print(d)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualisation graphique des données" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le fichier ne possédant aucune ligne _nulle_ , nous pouvons continuer en toute tranquillité. Nous voulons obtenir un aperçu graphique du nombre d'accidents comme une fonction de la température ambiante." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "d['Occurence'] = d.Malfunction / d.Count\n", "d.plot(x='Temperature', y='Occurence', kind='scatter')\n", "plt.grid(True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyse statistique des risques" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous cherchons maintenant à déterminer la probabilité qu'un joint soit détruit. Pour cela, nous utilisons un outil de régression logistique disponible dans la librairie statsmodels." ] }, { "cell_type": "code", "execution_count": 5, "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", "
Generalized Linear Model Regression Results
Dep. Variable: Occurence No. Observations: 23
Model: GLM Df Residuals: 21
Model Family: Binomial Df Model: 1
Link Function: logit Scale: 1.0000
Method: IRLS Log-Likelihood: -23.526
Date: Thu, 24 Sep 2020 Deviance: 18.086
Time: 12:33:06 Pearson chi2: 30.0
No. Iterations: 6 Covariance Type: nonrobust
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err z P>|z| [0.025 0.975]
Intercept 5.0850 3.052 1.666 0.096 -0.898 11.068
Temperature -0.1156 0.047 -2.458 0.014 -0.208 -0.023
" ], "text/plain": [ "\n", "\"\"\"\n", " Generalized Linear Model Regression Results \n", "==============================================================================\n", "Dep. Variable: Occurence No. Observations: 23\n", "Model: GLM Df Residuals: 21\n", "Model Family: Binomial Df Model: 1\n", "Link Function: logit Scale: 1.0000\n", "Method: IRLS Log-Likelihood: -23.526\n", "Date: Thu, 24 Sep 2020 Deviance: 18.086\n", "Time: 12:33:06 Pearson chi2: 30.0\n", "No. Iterations: 6 Covariance Type: nonrobust\n", "===============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "-------------------------------------------------------------------------------\n", "Intercept 5.0850 3.052 1.666 0.096 -0.898 11.068\n", "Temperature -0.1156 0.047 -2.458 0.014 -0.208 -0.023\n", "===============================================================================\n", "\"\"\"" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d['ok'] = d.Count - d.Malfunction\n", "d['Intercept'] = 1\n", "\n", "LogisticModel = sm.GLM(d['Occurence'], d[['Intercept','Temperature']], family=sm.families.Binomial(sm.families.links.logit()), var_weights=d['Count']).fit()\n", "LogisticModel.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous obtenons $G^2 = 18.086$ ainsi que les erreurs standards sur les coefficients $s_\\hat{\\alpha} = 3.052$ et $s_\\hat{\\beta} = 0.047$. Les résultats de l'article sont donc correctement reproductibles." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Probabilité d'accident" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous utilisons le modèle logisitique afin de prévoir la probabilité d'occurrence d'un accident en fonction de la température initiale." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Intercept Temperature Occurence\n", "0 1 30.0 0.834373\n", "1 1 31.0 0.817482\n", "2 1 32.0 0.799283\n", "3 1 33.0 0.779759\n", "4 1 34.0 0.758908\n", "5 1 35.0 0.736749\n", "6 1 36.0 0.713323\n", "7 1 37.0 0.688694\n", "8 1 38.0 0.662948\n", "9 1 39.0 0.636197\n", "10 1 40.0 0.608578\n", "11 1 41.0 0.580244\n", "12 1 42.0 0.551372\n", "13 1 43.0 0.522149\n", "14 1 44.0 0.492774\n", "15 1 45.0 0.463449\n", "16 1 46.0 0.434374\n", "17 1 47.0 0.405744\n", "18 1 48.0 0.377741\n", "19 1 49.0 0.350531\n", "20 1 50.0 0.324259\n", "21 1 51.0 0.299049\n", "22 1 52.0 0.275002\n", "23 1 53.0 0.252193\n", "24 1 54.0 0.230674\n", "25 1 55.0 0.210474\n", "26 1 56.0 0.191602\n", "27 1 57.0 0.174050\n", "28 1 58.0 0.157792\n", "29 1 59.0 0.142789\n", ".. ... ... ...\n", "31 1 61.0 0.116353\n", "32 1 62.0 0.104800\n", "33 1 63.0 0.094272\n", "34 1 64.0 0.084702\n", "35 1 65.0 0.076021\n", "36 1 66.0 0.068164\n", "37 1 67.0 0.061066\n", "38 1 68.0 0.054663\n", "39 1 69.0 0.048896\n", "40 1 70.0 0.043710\n", "41 1 71.0 0.039052\n", "42 1 72.0 0.034871\n", "43 1 73.0 0.031124\n", "44 1 74.0 0.027768\n", "45 1 75.0 0.024764\n", "46 1 76.0 0.022078\n", "47 1 77.0 0.019678\n", "48 1 78.0 0.017533\n", "49 1 79.0 0.015619\n", "50 1 80.0 0.013911\n", "51 1 81.0 0.012387\n", "52 1 82.0 0.011028\n", "53 1 83.0 0.009817\n", "54 1 84.0 0.008738\n", "55 1 85.0 0.007776\n", "56 1 86.0 0.006920\n", "57 1 87.0 0.006157\n", "58 1 88.0 0.005478\n", "59 1 89.0 0.004873\n", "60 1 90.0 NaN\n", "\n", "[61 rows x 3 columns]\n" ] } ], "source": [ "dLogistic = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=61), 'Intercept': 1})\n", "sm.add_constant(dLogistic)\n", "dLogistic['Occurence'] = LogisticModel.predict(X)\n", "print(dLogistic)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dLogistic.plot(x='Temperature', y='Occurence', kind='line')\n", "plt.scatter(x=d['Temperature'], y=d['Occurence'])\n", "plt.grid(True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Bien que l'utilisation d'un tel modèle pour prédire la probabilité d'un accident soit hautement discutable (trop peu de points et plusieurs points pathologiques), il en résulte sûrement un résultat surestimé. Ainsi, pour une valeur de $T = 31$ °F, la probabilité d'accident serait très importante." ] }, { "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": 4 }