From 210c8f9f4ba9a61f0977b4df5883901ada175872 Mon Sep 17 00:00:00 2001 From: 6e87c72f0873f418a8ed0c3d2877ebfe <6e87c72f0873f418a8ed0c3d2877ebfe@app-learninglab.inria.fr> Date: Wed, 31 Aug 2022 16:40:00 +0000 Subject: [PATCH] Analyse avec fichier .csv local incidence-PAY-3 --- module2/exo1/Untitled2.ipynb | 6 + module2/exo1/Untitled3.ipynb | 6 + module2/exo1/challenger.ipynb.txt | 780 +++++++ module2/exo1/prueba2.ipynb | 91 + module2/exo1/prueba_aprendido.ipynb | 2101 ++++++++++++++++++ module2/exo1/src_Python3_challenger.ipynb | 780 +++++++ module2/exo1/toy_notebook_fr.ipynb | 15 +- module3/exo1/analyse-syndrome-grippal.ipynb | 2212 ++++++++++++++++++- module3/exo1/incidence-PAY-3.csv | 1974 +++++++++++++++++ 9 files changed, 7926 insertions(+), 39 deletions(-) create mode 100644 module2/exo1/Untitled2.ipynb create mode 100644 module2/exo1/Untitled3.ipynb create mode 100644 module2/exo1/challenger.ipynb.txt create mode 100644 module2/exo1/prueba2.ipynb create mode 100644 module2/exo1/prueba_aprendido.ipynb create mode 100644 module2/exo1/src_Python3_challenger.ipynb create mode 100644 module3/exo1/incidence-PAY-3.csv diff --git a/module2/exo1/Untitled2.ipynb b/module2/exo1/Untitled2.ipynb new file mode 100644 index 0000000..7fec515 --- /dev/null +++ b/module2/exo1/Untitled2.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/module2/exo1/Untitled3.ipynb b/module2/exo1/Untitled3.ipynb new file mode 100644 index 0000000..7fec515 --- /dev/null +++ b/module2/exo1/Untitled3.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/module2/exo1/challenger.ipynb.txt b/module2/exo1/challenger.ipynb.txt new file mode 100644 index 0000000..da173e6 --- /dev/null +++ b/module2/exo1/challenger.ipynb.txt @@ -0,0 +1,780 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Risk Analysis of the Space Shuttle: Pre-Challenger Prediction of Failure" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this document we reperform some of the analysis provided in \n", + "*Risk Analysis of the Space Shuttle: Pre-Challenger Prediction of Failure* by *Siddhartha R. Dalal, Edward B. Fowlkes, Bruce Hoadley* published in *Journal of the American Statistical Association*, Vol. 84, No. 408 (Dec., 1989), pp. 945-957 and available at http://www.jstor.org/stable/2290069. \n", + "\n", + "On the fourth page of this article, they indicate that the maximum likelihood estimates of the logistic regression using only temperature are: $\\hat{\\alpha}=5.085$ and $\\hat{\\beta}=-0.1156$ and their asymptotic standard errors are $s_{\\hat{\\alpha}}=3.052$ and $s_{\\hat{\\beta}}=0.047$. The Goodness of fit indicated for this model was $G^2=18.086$ with 21 degrees of freedom. Our goal is to reproduce the computation behind these values and the Figure 4 of this article, possibly in a nicer looking way." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Technical information on the computer on which the analysis is run" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will be using the python3 language using the pandas, statsmodels, numpy, matplotlib and seaborn libraries." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.6.4 |Anaconda, Inc.| (default, Jan 16 2018, 18:10:19) \n", + "[GCC 7.2.0]\n", + "uname_result(system='Linux', node='3a716011d2b6', release='4.4.0-116-generic', version='#140-Ubuntu SMP Mon Feb 12 21:23:04 UTC 2018', machine='x86_64', processor='x86_64')\n", + "IPython 6.4.0\n", + "IPython.core.release 6.4.0\n", + "PIL 5.2.0\n", + "PIL.Image 5.2.0\n", + "PIL._version 5.2.0\n", + "_csv 1.0\n", + "_ctypes 1.1.0\n", + "_curses b'2.2'\n", + "decimal 1.70\n", + "argparse 1.1\n", + "backcall 0.1.0\n", + "cffi 1.11.5\n", + "csv 1.0\n", + "ctypes 1.1.0\n", + "cycler 0.10.0\n", + "dateutil 2.7.3\n", + "decimal 1.70\n", + "decorator 4.3.0\n", + "distutils 3.6.4\n", + "ipaddress 1.0\n", + "ipykernel 4.8.2\n", + "ipykernel._version 4.8.2\n", + "ipython_genutils 0.2.0\n", + "ipython_genutils._version 0.2.0\n", + "ipywidgets 7.2.1\n", + "ipywidgets._version 7.2.1\n", + "jedi 0.12.1\n", + "json 2.0.9\n", + "jupyter_client 5.2.3\n", + "jupyter_client._version 5.2.3\n", + "jupyter_core 4.4.0\n", + "jupyter_core.version 4.4.0\n", + "kiwisolver 1.0.1\n", + "logging 0.5.1.2\n", + "matplotlib 2.2.2\n", + "matplotlib.backends.backend_agg 2.2.2\n", + "numpy 1.13.3\n", + "numpy.core 1.13.3\n", + "numpy.core.multiarray 3.1\n", + "numpy.core.umath b'0.4.0'\n", + "numpy.lib 1.13.3\n", + "numpy.linalg._umath_linalg b'0.1.5'\n", + "numpy.matlib 1.13.3\n", + "optparse 1.5.3\n", + "pandas 0.22.0\n", + "_libjson 1.33\n", + "parso 0.3.0\n", + "patsy 0.5.0\n", + "patsy.version 0.5.0\n", + "pexpect 4.6.0\n", + "pickleshare 0.7.4\n", + "platform 1.0.8\n", + "prompt_toolkit 1.0.15\n", + "ptyprocess 0.6.0\n", + "pygments 2.2.0\n", + "pyparsing 2.2.0\n", + "pytz 2018.5\n", + "re 2.2.1\n", + "scipy 1.1.0\n", + "scipy._lib.decorator 4.0.5\n", + "scipy._lib.six 1.2.0\n", + "scipy.fftpack._fftpack b'$Revision: $'\n", + "scipy.fftpack.convolve b'$Revision: $'\n", + "scipy.integrate._dop b'$Revision: $'\n", + "scipy.integrate._ode $Id$\n", + "scipy.integrate._odepack 1.9 \n", + "scipy.integrate._quadpack 1.13 \n", + "scipy.integrate.lsoda b'$Revision: $'\n", + "scipy.integrate.vode b'$Revision: $'\n", + "scipy.interpolate._fitpack 1.7 \n", + "scipy.interpolate.dfitpack b'$Revision: $'\n", + "scipy.linalg 0.4.9\n", + "scipy.linalg._fblas b'$Revision: $'\n", + "scipy.linalg._flapack b'$Revision: $'\n", + "scipy.linalg._flinalg b'$Revision: $'\n", + "scipy.ndimage 2.0\n", + "scipy.optimize._cobyla b'$Revision: $'\n", + "scipy.optimize._lbfgsb b'$Revision: $'\n", + "scipy.optimize._minpack 1.10 \n", + "scipy.optimize._nnls b'$Revision: $'\n", + "scipy.optimize._slsqp b'$Revision: $'\n", + "scipy.optimize.minpack2 b'$Revision: $'\n", + "scipy.signal.spline 0.2\n", + "scipy.sparse.linalg.eigen.arpack._arpack b'$Revision: $'\n", + "scipy.sparse.linalg.isolve._iterative b'$Revision: $'\n", + "scipy.special.specfun b'$Revision: $'\n", + "scipy.stats.mvn b'$Revision: $'\n", + "scipy.stats.statlib b'$Revision: $'\n", + "seaborn 0.8.1\n", + "seaborn.external.husl 2.1.0\n", + "seaborn.external.six 1.10.0\n", + "six 1.11.0\n", + "statsmodels 0.9.0\n", + "statsmodels.__init__ 0.9.0\n", + "traitlets 4.3.2\n", + "traitlets._version 4.3.2\n", + "urllib.request 3.6\n", + "zlib 1.0\n", + "zmq 17.1.0\n", + "zmq.sugar 17.1.0\n", + "zmq.sugar.version 17.1.0\n" + ] + } + ], + "source": [ + "def print_imported_modules():\n", + " import sys\n", + " for name, val in sorted(sys.modules.items()):\n", + " if(hasattr(val, '__version__')): \n", + " print(val.__name__, val.__version__)\n", + "# else:\n", + "# print(val.__name__, \"(unknown version)\")\n", + "def print_sys_info():\n", + " import sys\n", + " import platform\n", + " print(sys.version)\n", + " print(platform.uname())\n", + "\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 sns\n", + "\n", + "print_sys_info()\n", + "print_imported_modules()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading and inspecting data\n", + "Let's start by reading data." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DateCountTemperaturePressureMalfunction
04/12/81666500
111/12/81670501
23/22/82669500
311/11/82668500
44/04/83667500
56/18/82672500
68/30/836731000
711/28/836701000
82/03/846572001
94/06/846632001
108/30/846702001
1110/05/846782000
1211/08/846672000
131/24/856532002
144/12/856672000
154/29/856752000
166/17/856702000
177/2903/856812000
188/27/856762000
1910/03/856792000
2010/30/856752002
2111/26/856762000
221/12/866582001
\n", + "
" + ], + "text/plain": [ + " 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" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv(\"https://app-learninglab.inria.fr/gitlab/moocrr-session1/moocrr-reproducibility-study/raw/master/data/shuttle.csv\")\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We know from our previous experience on this data set that filtering data is a really bad idea. We will therefore process it as such." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "pd.set_option('mode.chained_assignment',None) # this removes a useless warning from pandas\n", + "import matplotlib.pyplot as plt\n", + "\n", + "data[\"Frequency\"]=data.Malfunction/data.Count\n", + "data.plot(x=\"Temperature\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n", + "plt.grid(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Logistic regression\n", + "\n", + "Let's assume O-rings independently fail with the same probability which solely depends on temperature. A logistic regression should allow us to estimate the influence of temperature." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "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: Frequency No. Observations: 23
Model: GLM Df Residuals: 21
Model Family: Binomial Df Model: 1
Link Function: logit Scale: 1.0000
Method: IRLS Log-Likelihood: -3.9210
Date: Wed, 24 Oct 2018 Deviance: 3.0144
Time: 11:05:55 Pearson chi2: 5.00
No. Iterations: 6 Covariance Type: nonrobust
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coef std err z P>|z| [0.025 0.975]
Intercept 5.0850 7.477 0.680 0.496 -9.570 19.740
Temperature -0.1156 0.115 -1.004 0.316 -0.341 0.110
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Generalized Linear Model Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Frequency 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: -3.9210\n", + "Date: Wed, 24 Oct 2018 Deviance: 3.0144\n", + "Time: 11:05:55 Pearson chi2: 5.00\n", + "No. Iterations: 6 Covariance Type: nonrobust\n", + "===============================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "-------------------------------------------------------------------------------\n", + "Intercept 5.0850 7.477 0.680 0.496 -9.570 19.740\n", + "Temperature -0.1156 0.115 -1.004 0.316 -0.341 0.110\n", + "===============================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import statsmodels.api as sm\n", + "\n", + "data[\"Success\"]=data.Count-data.Malfunction\n", + "data[\"Intercept\"]=1\n", + "\n", + "logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], \n", + " family=sm.families.Binomial(sm.families.links.logit)).fit()\n", + "\n", + "logmodel.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The maximum likelyhood estimator of the intercept and of Temperature are thus $\\hat{\\alpha}=5.0849$ and $\\hat{\\beta}=-0.1156$. This **corresponds** to the values from the article of Dalal *et al.* The standard errors are $s_{\\hat{\\alpha}} = 7.477$ and $s_{\\hat{\\beta}} = 0.115$, which is **different** from the $3.052$ and $0.04702$ reported by Dallal *et al.* The deviance is $3.01444$ with 21 degrees of freedom. I cannot find any value similar to the Goodness of fit ($G^2=18.086$) reported by Dalal *et al.* There seems to be something wrong. Oh I know, I haven't indicated that my observations are actually the result of 6 observations for each rocket launch. Let's indicate these weights (since the weights are always the same throughout all experiments, it does not change the estimates of the fit but it does influence the variance estimates)." + ] + }, + { + "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: Frequency 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: Wed, 24 Oct 2018 Deviance: 18.086
Time: 11:05:55 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: Frequency 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: Wed, 24 Oct 2018 Deviance: 18.086\n", + "Time: 11:05:55 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": [ + "logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], \n", + " family=sm.families.Binomial(sm.families.links.logit),\n", + " var_weights=data['Count']).fit()\n", + "\n", + "logmodel.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Good, now I have recovered the asymptotic standard errors $s_{\\hat{\\alpha}}=3.052$ and $s_{\\hat{\\beta}}=0.047$.\n", + "The Goodness of fit (Deviance) indicated for this model is $G^2=18.086$ with 21 degrees of freedom (Df Residuals).\n", + "\n", + "**I have therefore managed to fully replicate the results of the Dalal *et al.* article**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Predicting failure probability\n", + "The temperature when launching the shuttle was 31°F. Let's try to estimate the failure probability for such temperature using our model.:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "data_pred = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=121), 'Intercept': 1})\n", + "data_pred['Frequency'] = logmodel.predict(data_pred)\n", + "data_pred.plot(x=\"Temperature\",y=\"Frequency\",kind=\"line\",ylim=[0,1])\n", + "plt.scatter(x=data[\"Temperature\"],y=data[\"Frequency\"])\n", + "plt.grid(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false, + "scrolled": true + }, + "source": [ + "This figure is very similar to the Figure 4 of Dalal *et al.* **I have managed to replicate the Figure 4 of the Dalal *et al.* article.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Computing and plotting uncertainty" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Following the documentation of [Seaborn](https://seaborn.pydata.org/generated/seaborn.regplot.html), I use regplot." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.set(color_codes=True)\n", + "plt.xlim(30,90)\n", + "plt.ylim(0,1)\n", + "sns.regplot(x='Temperature', y='Frequency', data=data, logistic=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**I think I have managed to correctly compute and plot the uncertainty of my prediction.** Although the shaded area seems very similar to [the one obtained by with R](https://app-learninglab.inria.fr/gitlab/moocrr-session1/moocrr-reproducibility-study/raw/5c9dbef11b4d7638b7ddf2ea71026e7bf00fcfb0/challenger.pdf), I can spot a few differences (e.g., the blue point for temperature 63 is outside)... Could this be a numerical error ? Or a difference in the statistical method ? It is not clear which one is \"right\"." + ] + } + ], + "metadata": { + "celltoolbar": "Hide code", + "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": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/module2/exo1/prueba2.ipynb b/module2/exo1/prueba2.ipynb new file mode 100644 index 0000000..ce45f84 --- /dev/null +++ b/module2/exo1/prueba2.ipynb @@ -0,0 +1,91 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Nuevo documento de prueba" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Se muestra una fórmula matemática" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "hideCode": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "2*1" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "hideOutput": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "11" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "3+7+9-8" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Hide code", + "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 +} diff --git a/module2/exo1/prueba_aprendido.ipynb b/module2/exo1/prueba_aprendido.ipynb new file mode 100644 index 0000000..51d94f5 --- /dev/null +++ b/module2/exo1/prueba_aprendido.ipynb @@ -0,0 +1,2101 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# INCIDENCE DU SYNDROME GRIPPAL" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import isoweek" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "data_url=\"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "raw_data = pd.read_csv(data_url, encoding='iso-8859-1', skiprows=1) " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020214233331127048.039574.05041.059.0FRFrance
120214132544220925.029959.03932.046.0FRFrance
220214032628621842.030730.04033.047.0FRFrance
320213932215518014.026296.03428.040.0FRFrance
420213831561412310.018918.02419.029.0FRFrance
520213731367310404.016942.02116.026.0FRFrance
62021363102897505.013073.01612.020.0FRFrance
72021353126099282.015936.01914.024.0FRFrance
82021343130159485.016545.02015.025.0FRFrance
92021333103927042.013742.01611.021.0FRFrance
1020213231558611009.020163.02417.031.0FRFrance
1120213131885513664.024046.02921.037.0FRFrance
122021303139919695.018287.02114.028.0FRFrance
132021293136269618.017634.02115.027.0FRFrance
14202128386365430.011842.0138.018.0FRFrance
152021273106936838.014548.01610.022.0FRFrance
16202126370864109.010063.0116.016.0FRFrance
17202125379425540.010344.0128.016.0FRFrance
18202124348553011.06699.074.010.0FRFrance
19202123367104455.08965.0107.013.0FRFrance
20202122378795495.010263.0128.016.0FRFrance
21202121378275403.010251.0128.016.0FRFrance
222021203102787540.013016.01612.020.0FRFrance
23202119395396860.012218.01410.018.0FRFrance
242021183121359165.015105.01814.022.0FRFrance
252021173120588891.015225.01813.023.0FRFrance
2620211631650512735.020275.02519.031.0FRFrance
2720211531930615398.023214.02923.035.0FRFrance
2820211432107317099.025047.03226.038.0FRFrance
2920211332641322094.030732.04033.047.0FRFrance
.................................
190019852132609619621.032571.04735.059.0FRFrance
190119852032789620885.034907.05138.064.0FRFrance
190219851934315432821.053487.07859.097.0FRFrance
190319851834055529935.051175.07455.093.0FRFrance
190419851733405324366.043740.06244.080.0FRFrance
190519851635036236451.064273.09166.0116.0FRFrance
190619851536388145538.082224.011683.0149.0FRFrance
19071985143134545114400.0154690.0244207.0281.0FRFrance
19081985133197206176080.0218332.0357319.0395.0FRFrance
19091985123245240223304.0267176.0445405.0485.0FRFrance
19101985113276205252399.0300011.0501458.0544.0FRFrance
19111985103353231326279.0380183.0640591.0689.0FRFrance
19121985093369895341109.0398681.0670618.0722.0FRFrance
19131985083389886359529.0420243.0707652.0762.0FRFrance
19141985073471852432599.0511105.0855784.0926.0FRFrance
19151985063565825518011.0613639.01026939.01113.0FRFrance
19161985053637302592795.0681809.011551074.01236.0FRFrance
19171985043424937390794.0459080.0770708.0832.0FRFrance
19181985033213901174689.0253113.0388317.0459.0FRFrance
191919850239758680949.0114223.0177147.0207.0FRFrance
192019850138548965918.0105060.0155120.0190.0FRFrance
192119845238483060602.0109058.0154110.0198.0FRFrance
1922198451310172680242.0123210.0185146.0224.0FRFrance
19231984503123680101401.0145959.0225184.0266.0FRFrance
1924198449310107381684.0120462.0184149.0219.0FRFrance
192519844837862060634.096606.0143110.0176.0FRFrance
192619844737202954274.089784.013199.0163.0FRFrance
192719844638733067686.0106974.0159123.0195.0FRFrance
19281984453135223101414.0169032.0246184.0308.0FRFrance
192919844436842220056.0116788.012537.0213.0FRFrance
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1930 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", + "1693 198919 3 0 NaN NaN 0 NaN NaN \n", + "\n", + " geo_insee geo_name \n", + "1693 FR France " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "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 +} diff --git a/module2/exo1/src_Python3_challenger.ipynb b/module2/exo1/src_Python3_challenger.ipynb new file mode 100644 index 0000000..da173e6 --- /dev/null +++ b/module2/exo1/src_Python3_challenger.ipynb @@ -0,0 +1,780 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Risk Analysis of the Space Shuttle: Pre-Challenger Prediction of Failure" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this document we reperform some of the analysis provided in \n", + "*Risk Analysis of the Space Shuttle: Pre-Challenger Prediction of Failure* by *Siddhartha R. Dalal, Edward B. Fowlkes, Bruce Hoadley* published in *Journal of the American Statistical Association*, Vol. 84, No. 408 (Dec., 1989), pp. 945-957 and available at http://www.jstor.org/stable/2290069. \n", + "\n", + "On the fourth page of this article, they indicate that the maximum likelihood estimates of the logistic regression using only temperature are: $\\hat{\\alpha}=5.085$ and $\\hat{\\beta}=-0.1156$ and their asymptotic standard errors are $s_{\\hat{\\alpha}}=3.052$ and $s_{\\hat{\\beta}}=0.047$. The Goodness of fit indicated for this model was $G^2=18.086$ with 21 degrees of freedom. Our goal is to reproduce the computation behind these values and the Figure 4 of this article, possibly in a nicer looking way." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Technical information on the computer on which the analysis is run" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will be using the python3 language using the pandas, statsmodels, numpy, matplotlib and seaborn libraries." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.6.4 |Anaconda, Inc.| (default, Jan 16 2018, 18:10:19) \n", + "[GCC 7.2.0]\n", + "uname_result(system='Linux', node='3a716011d2b6', release='4.4.0-116-generic', version='#140-Ubuntu SMP Mon Feb 12 21:23:04 UTC 2018', machine='x86_64', processor='x86_64')\n", + "IPython 6.4.0\n", + "IPython.core.release 6.4.0\n", + "PIL 5.2.0\n", + "PIL.Image 5.2.0\n", + "PIL._version 5.2.0\n", + "_csv 1.0\n", + "_ctypes 1.1.0\n", + "_curses b'2.2'\n", + "decimal 1.70\n", + "argparse 1.1\n", + "backcall 0.1.0\n", + "cffi 1.11.5\n", + "csv 1.0\n", + "ctypes 1.1.0\n", + "cycler 0.10.0\n", + "dateutil 2.7.3\n", + "decimal 1.70\n", + "decorator 4.3.0\n", + "distutils 3.6.4\n", + "ipaddress 1.0\n", + "ipykernel 4.8.2\n", + "ipykernel._version 4.8.2\n", + "ipython_genutils 0.2.0\n", + "ipython_genutils._version 0.2.0\n", + "ipywidgets 7.2.1\n", + "ipywidgets._version 7.2.1\n", + "jedi 0.12.1\n", + "json 2.0.9\n", + "jupyter_client 5.2.3\n", + "jupyter_client._version 5.2.3\n", + "jupyter_core 4.4.0\n", + "jupyter_core.version 4.4.0\n", + "kiwisolver 1.0.1\n", + "logging 0.5.1.2\n", + "matplotlib 2.2.2\n", + "matplotlib.backends.backend_agg 2.2.2\n", + "numpy 1.13.3\n", + "numpy.core 1.13.3\n", + "numpy.core.multiarray 3.1\n", + "numpy.core.umath b'0.4.0'\n", + "numpy.lib 1.13.3\n", + "numpy.linalg._umath_linalg b'0.1.5'\n", + "numpy.matlib 1.13.3\n", + "optparse 1.5.3\n", + "pandas 0.22.0\n", + "_libjson 1.33\n", + "parso 0.3.0\n", + "patsy 0.5.0\n", + "patsy.version 0.5.0\n", + "pexpect 4.6.0\n", + "pickleshare 0.7.4\n", + "platform 1.0.8\n", + "prompt_toolkit 1.0.15\n", + "ptyprocess 0.6.0\n", + "pygments 2.2.0\n", + "pyparsing 2.2.0\n", + "pytz 2018.5\n", + "re 2.2.1\n", + "scipy 1.1.0\n", + "scipy._lib.decorator 4.0.5\n", + "scipy._lib.six 1.2.0\n", + "scipy.fftpack._fftpack b'$Revision: $'\n", + "scipy.fftpack.convolve b'$Revision: $'\n", + "scipy.integrate._dop b'$Revision: $'\n", + "scipy.integrate._ode $Id$\n", + "scipy.integrate._odepack 1.9 \n", + "scipy.integrate._quadpack 1.13 \n", + "scipy.integrate.lsoda b'$Revision: $'\n", + "scipy.integrate.vode b'$Revision: $'\n", + "scipy.interpolate._fitpack 1.7 \n", + "scipy.interpolate.dfitpack b'$Revision: $'\n", + "scipy.linalg 0.4.9\n", + "scipy.linalg._fblas b'$Revision: $'\n", + "scipy.linalg._flapack b'$Revision: $'\n", + "scipy.linalg._flinalg b'$Revision: $'\n", + "scipy.ndimage 2.0\n", + "scipy.optimize._cobyla b'$Revision: $'\n", + "scipy.optimize._lbfgsb b'$Revision: $'\n", + "scipy.optimize._minpack 1.10 \n", + "scipy.optimize._nnls b'$Revision: $'\n", + "scipy.optimize._slsqp b'$Revision: $'\n", + "scipy.optimize.minpack2 b'$Revision: $'\n", + "scipy.signal.spline 0.2\n", + "scipy.sparse.linalg.eigen.arpack._arpack b'$Revision: $'\n", + "scipy.sparse.linalg.isolve._iterative b'$Revision: $'\n", + "scipy.special.specfun b'$Revision: $'\n", + "scipy.stats.mvn b'$Revision: $'\n", + "scipy.stats.statlib b'$Revision: $'\n", + "seaborn 0.8.1\n", + "seaborn.external.husl 2.1.0\n", + "seaborn.external.six 1.10.0\n", + "six 1.11.0\n", + "statsmodels 0.9.0\n", + "statsmodels.__init__ 0.9.0\n", + "traitlets 4.3.2\n", + "traitlets._version 4.3.2\n", + "urllib.request 3.6\n", + "zlib 1.0\n", + "zmq 17.1.0\n", + "zmq.sugar 17.1.0\n", + "zmq.sugar.version 17.1.0\n" + ] + } + ], + "source": [ + "def print_imported_modules():\n", + " import sys\n", + " for name, val in sorted(sys.modules.items()):\n", + " if(hasattr(val, '__version__')): \n", + " print(val.__name__, val.__version__)\n", + "# else:\n", + "# print(val.__name__, \"(unknown version)\")\n", + "def print_sys_info():\n", + " import sys\n", + " import platform\n", + " print(sys.version)\n", + " print(platform.uname())\n", + "\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 sns\n", + "\n", + "print_sys_info()\n", + "print_imported_modules()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading and inspecting data\n", + "Let's start by reading data." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DateCountTemperaturePressureMalfunction
04/12/81666500
111/12/81670501
23/22/82669500
311/11/82668500
44/04/83667500
56/18/82672500
68/30/836731000
711/28/836701000
82/03/846572001
94/06/846632001
108/30/846702001
1110/05/846782000
1211/08/846672000
131/24/856532002
144/12/856672000
154/29/856752000
166/17/856702000
177/2903/856812000
188/27/856762000
1910/03/856792000
2010/30/856752002
2111/26/856762000
221/12/866582001
\n", + "
" + ], + "text/plain": [ + " 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" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv(\"https://app-learninglab.inria.fr/gitlab/moocrr-session1/moocrr-reproducibility-study/raw/master/data/shuttle.csv\")\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We know from our previous experience on this data set that filtering data is a really bad idea. We will therefore process it as such." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "pd.set_option('mode.chained_assignment',None) # this removes a useless warning from pandas\n", + "import matplotlib.pyplot as plt\n", + "\n", + "data[\"Frequency\"]=data.Malfunction/data.Count\n", + "data.plot(x=\"Temperature\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n", + "plt.grid(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Logistic regression\n", + "\n", + "Let's assume O-rings independently fail with the same probability which solely depends on temperature. A logistic regression should allow us to estimate the influence of temperature." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "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: Frequency No. Observations: 23
Model: GLM Df Residuals: 21
Model Family: Binomial Df Model: 1
Link Function: logit Scale: 1.0000
Method: IRLS Log-Likelihood: -3.9210
Date: Wed, 24 Oct 2018 Deviance: 3.0144
Time: 11:05:55 Pearson chi2: 5.00
No. Iterations: 6 Covariance Type: nonrobust
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coef std err z P>|z| [0.025 0.975]
Intercept 5.0850 7.477 0.680 0.496 -9.570 19.740
Temperature -0.1156 0.115 -1.004 0.316 -0.341 0.110
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Generalized Linear Model Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Frequency 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: -3.9210\n", + "Date: Wed, 24 Oct 2018 Deviance: 3.0144\n", + "Time: 11:05:55 Pearson chi2: 5.00\n", + "No. Iterations: 6 Covariance Type: nonrobust\n", + "===============================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "-------------------------------------------------------------------------------\n", + "Intercept 5.0850 7.477 0.680 0.496 -9.570 19.740\n", + "Temperature -0.1156 0.115 -1.004 0.316 -0.341 0.110\n", + "===============================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import statsmodels.api as sm\n", + "\n", + "data[\"Success\"]=data.Count-data.Malfunction\n", + "data[\"Intercept\"]=1\n", + "\n", + "logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], \n", + " family=sm.families.Binomial(sm.families.links.logit)).fit()\n", + "\n", + "logmodel.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The maximum likelyhood estimator of the intercept and of Temperature are thus $\\hat{\\alpha}=5.0849$ and $\\hat{\\beta}=-0.1156$. This **corresponds** to the values from the article of Dalal *et al.* The standard errors are $s_{\\hat{\\alpha}} = 7.477$ and $s_{\\hat{\\beta}} = 0.115$, which is **different** from the $3.052$ and $0.04702$ reported by Dallal *et al.* The deviance is $3.01444$ with 21 degrees of freedom. I cannot find any value similar to the Goodness of fit ($G^2=18.086$) reported by Dalal *et al.* There seems to be something wrong. Oh I know, I haven't indicated that my observations are actually the result of 6 observations for each rocket launch. Let's indicate these weights (since the weights are always the same throughout all experiments, it does not change the estimates of the fit but it does influence the variance estimates)." + ] + }, + { + "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: Frequency 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: Wed, 24 Oct 2018 Deviance: 18.086
Time: 11:05:55 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: Frequency 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: Wed, 24 Oct 2018 Deviance: 18.086\n", + "Time: 11:05:55 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": [ + "logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], \n", + " family=sm.families.Binomial(sm.families.links.logit),\n", + " var_weights=data['Count']).fit()\n", + "\n", + "logmodel.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Good, now I have recovered the asymptotic standard errors $s_{\\hat{\\alpha}}=3.052$ and $s_{\\hat{\\beta}}=0.047$.\n", + "The Goodness of fit (Deviance) indicated for this model is $G^2=18.086$ with 21 degrees of freedom (Df Residuals).\n", + "\n", + "**I have therefore managed to fully replicate the results of the Dalal *et al.* article**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Predicting failure probability\n", + "The temperature when launching the shuttle was 31°F. Let's try to estimate the failure probability for such temperature using our model.:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "data_pred = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=121), 'Intercept': 1})\n", + "data_pred['Frequency'] = logmodel.predict(data_pred)\n", + "data_pred.plot(x=\"Temperature\",y=\"Frequency\",kind=\"line\",ylim=[0,1])\n", + "plt.scatter(x=data[\"Temperature\"],y=data[\"Frequency\"])\n", + "plt.grid(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false, + "scrolled": true + }, + "source": [ + "This figure is very similar to the Figure 4 of Dalal *et al.* **I have managed to replicate the Figure 4 of the Dalal *et al.* article.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Computing and plotting uncertainty" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Following the documentation of [Seaborn](https://seaborn.pydata.org/generated/seaborn.regplot.html), I use regplot." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.set(color_codes=True)\n", + "plt.xlim(30,90)\n", + "plt.ylim(0,1)\n", + "sns.regplot(x='Temperature', y='Frequency', data=data, logistic=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**I think I have managed to correctly compute and plot the uncertainty of my prediction.** Although the shaded area seems very similar to [the one obtained by with R](https://app-learninglab.inria.fr/gitlab/moocrr-session1/moocrr-reproducibility-study/raw/5c9dbef11b4d7638b7ddf2ea71026e7bf00fcfb0/challenger.pdf), I can spot a few differences (e.g., the blue point for temperature 63 is outside)... Could this be a numerical error ? Or a difference in the statistical method ? It is not clear which one is \"right\"." + ] + } + ], + "metadata": { + "celltoolbar": "Hide code", + "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": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/module2/exo1/toy_notebook_fr.ipynb b/module2/exo1/toy_notebook_fr.ipynb index 4bec6e7..2230d3b 100644 --- a/module2/exo1/toy_notebook_fr.ipynb +++ b/module2/exo1/toy_notebook_fr.ipynb @@ -2,7 +2,9 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": true + }, "source": [ "# EXEMPLE D'UN DOCUMENT MARKDOWN" ] @@ -16,7 +18,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideOutput": true + }, "source": [ "1. **Texte en gras**\n", "2. _Texte en italique_\n", @@ -42,7 +46,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hideOutput": false, + "hidePrompt": false + }, "source": [ "## Un tableau\n", "\n", @@ -68,6 +76,7 @@ } ], "metadata": { + "celltoolbar": "Hide code", "kernelspec": { "display_name": "Python 3", "language": "python", diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index 59d72b5..5161553 100644 --- a/module3/exo1/analyse-syndrome-grippal.ipynb +++ b/module3/exo1/analyse-syndrome-grippal.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -28,13 +28,12 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ - "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" + "# Pour télécharger le fichier depuis le Réseau Sentinelles enlever le # du code suivante\n", + "# data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" ] }, { @@ -61,11 +60,978 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020223233184623266.040426.04835.061.0FRFrance
120223132191416321.027507.03325.041.0FRFrance
220223031966314779.024547.03023.037.0FRFrance
320222932426818906.029630.03729.045.0FRFrance
420222832484519214.030476.03729.045.0FRFrance
520222734074533994.047496.06151.071.0FRFrance
620222633401028521.039499.05143.059.0FRFrance
720222532337719042.027712.03528.042.0FRFrance
820222432632821829.030827.04033.047.0FRFrance
920222332343018950.027910.03528.042.0FRFrance
1020222231895115099.022803.02923.035.0FRFrance
1120222131363210251.017013.02116.026.0FRFrance
1220222031978715756.023818.03024.036.0FRFrance
1320221931788414079.021689.02721.033.0FRFrance
1420221833035325089.035617.04638.054.0FRFrance
1520221733600630373.041639.05446.062.0FRFrance
1620221634994942836.057062.07564.086.0FRFrance
17202215310080690824.0110788.0152137.0167.0FRFrance
182022143155441143891.0166991.0234217.0251.0FRFrance
192022133191914179558.0204270.0289270.0308.0FRFrance
202022123166224155035.0177413.0251234.0268.0FRFrance
212022113122849113306.0132392.0185171.0199.0FRFrance
2220221038790479741.096067.0133121.0145.0FRFrance
2320220935018243958.056406.07667.085.0FRFrance
2420220833096325942.035984.04739.055.0FRFrance
2520220733488229446.040318.05345.061.0FRFrance
2620220634662340398.052848.07061.079.0FRFrance
2720220536297056043.069897.09585.0105.0FRFrance
2820220437220964804.079614.010998.0120.0FRFrance
2920220337461367144.082082.0113102.0124.0FRFrance
.................................
194219852132609619621.032571.04735.059.0FRFrance
194319852032789620885.034907.05138.064.0FRFrance
194419851934315432821.053487.07859.097.0FRFrance
194519851834055529935.051175.07455.093.0FRFrance
194619851733405324366.043740.06244.080.0FRFrance
194719851635036236451.064273.09166.0116.0FRFrance
194819851536388145538.082224.011683.0149.0FRFrance
19491985143134545114400.0154690.0244207.0281.0FRFrance
19501985133197206176080.0218332.0357319.0395.0FRFrance
19511985123245240223304.0267176.0445405.0485.0FRFrance
19521985113276205252399.0300011.0501458.0544.0FRFrance
19531985103353231326279.0380183.0640591.0689.0FRFrance
19541985093369895341109.0398681.0670618.0722.0FRFrance
19551985083389886359529.0420243.0707652.0762.0FRFrance
19561985073471852432599.0511105.0855784.0926.0FRFrance
19571985063565825518011.0613639.01026939.01113.0FRFrance
19581985053637302592795.0681809.011551074.01236.0FRFrance
19591985043424937390794.0459080.0770708.0832.0FRFrance
19601985033213901174689.0253113.0388317.0459.0FRFrance
196119850239758680949.0114223.0177147.0207.0FRFrance
196219850138548965918.0105060.0155120.0190.0FRFrance
196319845238483060602.0109058.0154110.0198.0FRFrance
1964198451310172680242.0123210.0185146.0224.0FRFrance
19651984503123680101401.0145959.0225184.0266.0FRFrance
1966198449310107381684.0120462.0184149.0219.0FRFrance
196719844837862060634.096606.0143110.0176.0FRFrance
196819844737202954274.089784.013199.0163.0FRFrance
196919844638733067686.0106974.0159123.0195.0FRFrance
19701984453135223101414.0169032.0246184.0308.0FRFrance
197119844436842220056.0116788.012537.0213.0FRFrance
\n", + "

1972 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202232 3 31846 23266.0 40426.0 48 35.0 \n", + "1 202231 3 21914 16321.0 27507.0 33 25.0 \n", + "2 202230 3 19663 14779.0 24547.0 30 23.0 \n", + "3 202229 3 24268 18906.0 29630.0 37 29.0 \n", + "4 202228 3 24845 19214.0 30476.0 37 29.0 \n", + "5 202227 3 40745 33994.0 47496.0 61 51.0 \n", + "6 202226 3 34010 28521.0 39499.0 51 43.0 \n", + "7 202225 3 23377 19042.0 27712.0 35 28.0 \n", + "8 202224 3 26328 21829.0 30827.0 40 33.0 \n", + "9 202223 3 23430 18950.0 27910.0 35 28.0 \n", + "10 202222 3 18951 15099.0 22803.0 29 23.0 \n", + "11 202221 3 13632 10251.0 17013.0 21 16.0 \n", + "12 202220 3 19787 15756.0 23818.0 30 24.0 \n", + "13 202219 3 17884 14079.0 21689.0 27 21.0 \n", + "14 202218 3 30353 25089.0 35617.0 46 38.0 \n", + "15 202217 3 36006 30373.0 41639.0 54 46.0 \n", + "16 202216 3 49949 42836.0 57062.0 75 64.0 \n", + "17 202215 3 100806 90824.0 110788.0 152 137.0 \n", + "18 202214 3 155441 143891.0 166991.0 234 217.0 \n", + "19 202213 3 191914 179558.0 204270.0 289 270.0 \n", + "20 202212 3 166224 155035.0 177413.0 251 234.0 \n", + "21 202211 3 122849 113306.0 132392.0 185 171.0 \n", + "22 202210 3 87904 79741.0 96067.0 133 121.0 \n", + "23 202209 3 50182 43958.0 56406.0 76 67.0 \n", + "24 202208 3 30963 25942.0 35984.0 47 39.0 \n", + "25 202207 3 34882 29446.0 40318.0 53 45.0 \n", + "26 202206 3 46623 40398.0 52848.0 70 61.0 \n", + "27 202205 3 62970 56043.0 69897.0 95 85.0 \n", + "28 202204 3 72209 64804.0 79614.0 109 98.0 \n", + "29 202203 3 74613 67144.0 82082.0 113 102.0 \n", + "... ... ... ... ... ... ... ... \n", + "1942 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1943 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1944 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1945 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1946 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1947 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1948 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1949 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1950 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1951 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1952 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1953 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1954 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1955 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1956 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1957 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1958 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1959 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1960 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1961 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1962 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1963 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1964 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1965 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "1966 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "1967 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "1968 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1969 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1970 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1971 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 61.0 FR France \n", + "1 41.0 FR France \n", + "2 37.0 FR France \n", + "3 45.0 FR France \n", + "4 45.0 FR France \n", + "5 71.0 FR France \n", + "6 59.0 FR France \n", + "7 42.0 FR France \n", + "8 47.0 FR France \n", + "9 42.0 FR France \n", + "10 35.0 FR France \n", + "11 26.0 FR France \n", + "12 36.0 FR France \n", + "13 33.0 FR France \n", + "14 54.0 FR France \n", + "15 62.0 FR France \n", + "16 86.0 FR France \n", + "17 167.0 FR France \n", + "18 251.0 FR France \n", + "19 308.0 FR France \n", + "20 268.0 FR France \n", + "21 199.0 FR France \n", + "22 145.0 FR France \n", + "23 85.0 FR France \n", + "24 55.0 FR France \n", + "25 61.0 FR France \n", + "26 79.0 FR France \n", + "27 105.0 FR France \n", + "28 120.0 FR France \n", + "29 124.0 FR France \n", + "... ... ... ... \n", + "1942 59.0 FR France \n", + "1943 64.0 FR France \n", + "1944 97.0 FR France \n", + "1945 93.0 FR France \n", + "1946 80.0 FR France \n", + "1947 116.0 FR France \n", + "1948 149.0 FR France \n", + "1949 281.0 FR France \n", + "1950 395.0 FR France \n", + "1951 485.0 FR France \n", + "1952 544.0 FR France \n", + "1953 689.0 FR France \n", + "1954 722.0 FR France \n", + "1955 762.0 FR France \n", + "1956 926.0 FR France \n", + "1957 1113.0 FR France \n", + "1958 1236.0 FR France \n", + "1959 832.0 FR France \n", + "1960 459.0 FR France \n", + "1961 207.0 FR France \n", + "1962 190.0 FR France \n", + "1963 198.0 FR France \n", + "1964 224.0 FR France \n", + "1965 266.0 FR France \n", + "1966 219.0 FR France \n", + "1967 176.0 FR France \n", + "1968 163.0 FR France \n", + "1969 195.0 FR France \n", + "1970 308.0 FR France \n", + "1971 213.0 FR France \n", + "\n", + "[1972 rows x 10 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "raw_data = pd.read_csv(data_url, skiprows=1)\n", + "raw_data = pd.read_csv(\"../exo1/incidence-PAY-3.csv\", encoding = 'iso-8859-1', skiprows=1)\n", "raw_data" ] }, @@ -78,9 +1044,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
173519891930NaNNaN0NaNNaNFRFrance
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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", + "1735 198919 3 0 NaN NaN 0 NaN NaN \n", + "\n", + " geo_insee geo_name \n", + "1735 FR France " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] @@ -94,9 +1124,976 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020223233184623266.040426.04835.061.0FRFrance
120223132191416321.027507.03325.041.0FRFrance
220223031966314779.024547.03023.037.0FRFrance
320222932426818906.029630.03729.045.0FRFrance
420222832484519214.030476.03729.045.0FRFrance
520222734074533994.047496.06151.071.0FRFrance
620222633401028521.039499.05143.059.0FRFrance
720222532337719042.027712.03528.042.0FRFrance
820222432632821829.030827.04033.047.0FRFrance
920222332343018950.027910.03528.042.0FRFrance
1020222231895115099.022803.02923.035.0FRFrance
1120222131363210251.017013.02116.026.0FRFrance
1220222031978715756.023818.03024.036.0FRFrance
1320221931788414079.021689.02721.033.0FRFrance
1420221833035325089.035617.04638.054.0FRFrance
1520221733600630373.041639.05446.062.0FRFrance
1620221634994942836.057062.07564.086.0FRFrance
17202215310080690824.0110788.0152137.0167.0FRFrance
182022143155441143891.0166991.0234217.0251.0FRFrance
192022133191914179558.0204270.0289270.0308.0FRFrance
202022123166224155035.0177413.0251234.0268.0FRFrance
212022113122849113306.0132392.0185171.0199.0FRFrance
2220221038790479741.096067.0133121.0145.0FRFrance
2320220935018243958.056406.07667.085.0FRFrance
2420220833096325942.035984.04739.055.0FRFrance
2520220733488229446.040318.05345.061.0FRFrance
2620220634662340398.052848.07061.079.0FRFrance
2720220536297056043.069897.09585.0105.0FRFrance
2820220437220964804.079614.010998.0120.0FRFrance
2920220337461367144.082082.0113102.0124.0FRFrance
.................................
194219852132609619621.032571.04735.059.0FRFrance
194319852032789620885.034907.05138.064.0FRFrance
194419851934315432821.053487.07859.097.0FRFrance
194519851834055529935.051175.07455.093.0FRFrance
194619851733405324366.043740.06244.080.0FRFrance
194719851635036236451.064273.09166.0116.0FRFrance
194819851536388145538.082224.011683.0149.0FRFrance
19491985143134545114400.0154690.0244207.0281.0FRFrance
19501985133197206176080.0218332.0357319.0395.0FRFrance
19511985123245240223304.0267176.0445405.0485.0FRFrance
19521985113276205252399.0300011.0501458.0544.0FRFrance
19531985103353231326279.0380183.0640591.0689.0FRFrance
19541985093369895341109.0398681.0670618.0722.0FRFrance
19551985083389886359529.0420243.0707652.0762.0FRFrance
19561985073471852432599.0511105.0855784.0926.0FRFrance
19571985063565825518011.0613639.01026939.01113.0FRFrance
19581985053637302592795.0681809.011551074.01236.0FRFrance
19591985043424937390794.0459080.0770708.0832.0FRFrance
19601985033213901174689.0253113.0388317.0459.0FRFrance
196119850239758680949.0114223.0177147.0207.0FRFrance
196219850138548965918.0105060.0155120.0190.0FRFrance
196319845238483060602.0109058.0154110.0198.0FRFrance
1964198451310172680242.0123210.0185146.0224.0FRFrance
19651984503123680101401.0145959.0225184.0266.0FRFrance
1966198449310107381684.0120462.0184149.0219.0FRFrance
196719844837862060634.096606.0143110.0176.0FRFrance
196819844737202954274.089784.013199.0163.0FRFrance
196919844638733067686.0106974.0159123.0195.0FRFrance
19701984453135223101414.0169032.0246184.0308.0FRFrance
197119844436842220056.0116788.012537.0213.0FRFrance
\n", + "

1971 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202232 3 31846 23266.0 40426.0 48 35.0 \n", + "1 202231 3 21914 16321.0 27507.0 33 25.0 \n", + "2 202230 3 19663 14779.0 24547.0 30 23.0 \n", + "3 202229 3 24268 18906.0 29630.0 37 29.0 \n", + "4 202228 3 24845 19214.0 30476.0 37 29.0 \n", + "5 202227 3 40745 33994.0 47496.0 61 51.0 \n", + "6 202226 3 34010 28521.0 39499.0 51 43.0 \n", + "7 202225 3 23377 19042.0 27712.0 35 28.0 \n", + "8 202224 3 26328 21829.0 30827.0 40 33.0 \n", + "9 202223 3 23430 18950.0 27910.0 35 28.0 \n", + "10 202222 3 18951 15099.0 22803.0 29 23.0 \n", + "11 202221 3 13632 10251.0 17013.0 21 16.0 \n", + "12 202220 3 19787 15756.0 23818.0 30 24.0 \n", + "13 202219 3 17884 14079.0 21689.0 27 21.0 \n", + "14 202218 3 30353 25089.0 35617.0 46 38.0 \n", + "15 202217 3 36006 30373.0 41639.0 54 46.0 \n", + "16 202216 3 49949 42836.0 57062.0 75 64.0 \n", + "17 202215 3 100806 90824.0 110788.0 152 137.0 \n", + "18 202214 3 155441 143891.0 166991.0 234 217.0 \n", + "19 202213 3 191914 179558.0 204270.0 289 270.0 \n", + "20 202212 3 166224 155035.0 177413.0 251 234.0 \n", + "21 202211 3 122849 113306.0 132392.0 185 171.0 \n", + "22 202210 3 87904 79741.0 96067.0 133 121.0 \n", + "23 202209 3 50182 43958.0 56406.0 76 67.0 \n", + "24 202208 3 30963 25942.0 35984.0 47 39.0 \n", + "25 202207 3 34882 29446.0 40318.0 53 45.0 \n", + "26 202206 3 46623 40398.0 52848.0 70 61.0 \n", + "27 202205 3 62970 56043.0 69897.0 95 85.0 \n", + "28 202204 3 72209 64804.0 79614.0 109 98.0 \n", + "29 202203 3 74613 67144.0 82082.0 113 102.0 \n", + "... ... ... ... ... ... ... ... \n", + "1942 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1943 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1944 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1945 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1946 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1947 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1948 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1949 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1950 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1951 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1952 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1953 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1954 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1955 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1956 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1957 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1958 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1959 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1960 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1961 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1962 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1963 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1964 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1965 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "1966 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "1967 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "1968 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1969 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1970 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1971 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 61.0 FR France \n", + "1 41.0 FR France \n", + "2 37.0 FR France \n", + "3 45.0 FR France \n", + "4 45.0 FR France \n", + "5 71.0 FR France \n", + "6 59.0 FR France \n", + "7 42.0 FR France \n", + "8 47.0 FR France \n", + "9 42.0 FR France \n", + "10 35.0 FR France \n", + "11 26.0 FR France \n", + "12 36.0 FR France \n", + "13 33.0 FR France \n", + "14 54.0 FR France \n", + "15 62.0 FR France \n", + "16 86.0 FR France \n", + "17 167.0 FR France \n", + "18 251.0 FR France \n", + "19 308.0 FR France \n", + "20 268.0 FR France \n", + "21 199.0 FR France \n", + "22 145.0 FR France \n", + "23 85.0 FR France \n", + "24 55.0 FR France \n", + "25 61.0 FR France \n", + "26 79.0 FR France \n", + "27 105.0 FR France \n", + "28 120.0 FR France \n", + "29 124.0 FR France \n", + "... ... ... ... \n", + "1942 59.0 FR France \n", + "1943 64.0 FR France \n", + "1944 97.0 FR France \n", + "1945 93.0 FR France \n", + "1946 80.0 FR France \n", + "1947 116.0 FR France \n", + "1948 149.0 FR France \n", + "1949 281.0 FR France \n", + "1950 395.0 FR France \n", + "1951 485.0 FR France \n", + "1952 544.0 FR France \n", + "1953 689.0 FR France \n", + "1954 722.0 FR France \n", + "1955 762.0 FR France \n", + "1956 926.0 FR France \n", + "1957 1113.0 FR France \n", + "1958 1236.0 FR France \n", + "1959 832.0 FR France \n", + "1960 459.0 FR France \n", + "1961 207.0 FR France \n", + "1962 190.0 FR France \n", + "1963 198.0 FR France \n", + "1964 224.0 FR France \n", + "1965 266.0 FR France \n", + "1966 219.0 FR France \n", + "1967 176.0 FR France \n", + "1968 163.0 FR France \n", + "1969 195.0 FR France \n", + "1970 308.0 FR France \n", + "1971 213.0 FR France \n", + "\n", + "[1971 rows x 10 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = raw_data.dropna().copy()\n", "data" @@ -122,7 +2119,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -152,10 +2149,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 15, + "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" @@ -179,9 +2174,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n" + ] + } + ], "source": [ "periods = sorted_data.index\n", "for p1, p2 in zip(periods[:-1], periods[1:]):\n", @@ -199,9 +2202,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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5115251\n", + "1990 5235827\n", + "1989 5466192\n", + "dtype: int64" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "yearly_incidence.sort_values()" ] @@ -331,9 +2448,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "yearly_incidence.hist(xrot=20)" ] @@ -364,7 +2504,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.4" } }, "nbformat": 4, diff --git a/module3/exo1/incidence-PAY-3.csv b/module3/exo1/incidence-PAY-3.csv new file mode 100644 index 0000000..0ae9743 --- /dev/null +++ b/module3/exo1/incidence-PAY-3.csv @@ -0,0 +1,1974 @@ +# @source="réseau Sentinelles, INSERM, Sorbonne Université, http://www.sentiweb.fr", @meta={"period":[198444,202232],"geo":["PAY","1"],"geo_ref":"insee","indicator":"3","type":"all","conf_int":true,"compact":false}, @date=2022-08-19T01:49:09+02:00 +week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name +202232,3,31846,23266,40426,48,35,61,FR,France +202231,3,21914,16321,27507,33,25,41,FR,France +202230,3,19663,14779,24547,30,23,37,FR,France 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