From 88f49a32e9454c342dcaa5b1bdc17b553f222fbd Mon Sep 17 00:00:00 2001 From: 4c0611008db969a4dbfc8fda2f0d9d72 <4c0611008db969a4dbfc8fda2f0d9d72@app-learninglab.inria.fr> Date: Fri, 18 Aug 2023 12:55:05 +0000 Subject: [PATCH] exo2 module3 --- module3/exo2/exercice.ipynb | 1385 ++++++++++++++++++++++++++++++++++- 1 file changed, 1382 insertions(+), 3 deletions(-) diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe37..562f54b 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,5 +1,1385 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analyse incidence Varicelle" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np\n", + "import isoweek" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les données de l'incidence de la varicelle sont disponibles sur le [site Web du Réseau Sentinelles](https://sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv?v=3m0ly\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n", + "\n", + "| Nom de colonne | Libellé de colonne |\n", + "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n", + "| week | Semaine calendaire (ISO 8601) |\n", + "| indicator | Code de l'indicateur de surveillance |\n", + "| inc | Estimation de l'incidence de consultations en nombre de cas |\n", + "| inc_low | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation |\n", + "| inc_up | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation |\n", + "| inc100 | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", + "| inc100_low | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", + "| inc100_up | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", + "| geo_insee | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/ |\n", + "| geo_name | Libellé de la zone géographique (ce libellé peut être modifié sans préavis) |\n", + "\n", + "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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132023197934460911259714919FRFrance
14202318710671729114051161121FRFrance
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17202315714040761320467211131FRFrance
182023147152471103219462231729FRFrance
19202313713322970016944201525FRFrance
20202312710374721813530161121FRFrance
2120231174919288069587410FRFrance
2220231074854273169777410FRFrance
23202309770044548946011715FRFrance
242023087817553161103412816FRFrance
25202307765953782940810614FRFrance
262023067959560171317314919FRFrance
2720230576237390785679513FRFrance
2820230476299397386259612FRFrance
2920230376063379883289612FRFrance
.................................
16761991267176081130423912312042FRFrance
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16781991247161711007122271281739FRFrance
1679199123711947767116223211329FRFrance
1680199122715452995320951271737FRFrance
1681199121714903897520831261636FRFrance
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16841991187213851388228888382551FRFrance
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199112 7 10864 7331 14397 19 13 \n", + "1691 199111 7 15574 11184 19964 27 19 \n", + "1692 199110 7 16643 11372 21914 29 20 \n", + "1693 199109 7 13741 8780 18702 24 15 \n", + "1694 199108 7 13289 8813 17765 23 15 \n", + "1695 199107 7 12337 8077 16597 22 15 \n", + "1696 199106 7 10877 7013 14741 19 12 \n", + "1697 199105 7 10442 6544 14340 18 11 \n", + "1698 199104 7 7913 4563 11263 14 8 \n", + "1699 199103 7 15387 10484 20290 27 18 \n", + "1700 199102 7 16277 11046 21508 29 20 \n", + "1701 199101 7 15565 10271 20859 27 18 \n", + "1702 199052 7 19375 13295 25455 34 23 \n", + "1703 199051 7 19080 13807 24353 34 25 \n", + "1704 199050 7 11079 6660 15498 20 12 \n", + "1705 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 25 FR France \n", + "1 8 FR France \n", + "2 13 FR France \n", + "3 28 FR France \n", + "4 14 FR France \n", + "5 15 FR France \n", + "6 18 FR France \n", + "7 22 FR France \n", + "8 22 FR France \n", + "9 29 FR France \n", + "10 24 FR France \n", + "11 23 FR France \n", + "12 21 FR France \n", + "13 19 FR France \n", + "14 21 FR France \n", + "15 19 FR France \n", + "16 22 FR France \n", + "17 31 FR France \n", + "18 29 FR France \n", + "19 25 FR France \n", + "20 21 FR France \n", + "21 10 FR France \n", + "22 10 FR France \n", + "23 15 FR France \n", + "24 16 FR France \n", + "25 14 FR France \n", + "26 19 FR France \n", + "27 13 FR France \n", + "28 12 FR France \n", + "29 12 FR France \n", + "... ... ... ... \n", + "1676 42 FR France \n", + "1677 38 FR France \n", + "1678 39 FR France \n", + "1679 29 FR France \n", + "1680 37 FR France \n", + "1681 36 FR France \n", + "1682 45 FR France \n", + "1683 39 FR France \n", + "1684 51 FR France \n", + "1685 32 FR France \n", + "1686 34 FR France \n", + "1687 32 FR France \n", + "1688 30 FR France \n", + "1689 23 FR France \n", + "1690 25 FR France \n", + "1691 35 FR France \n", + "1692 38 FR France \n", + "1693 33 FR France \n", + "1694 31 FR France \n", + "1695 29 FR France \n", + "1696 26 FR France \n", + "1697 25 FR France \n", + "1698 20 FR France \n", + "1699 36 FR France \n", + "1700 38 FR France \n", + "1701 36 FR France \n", + "1702 45 FR France \n", + "1703 43 FR France \n", + "1704 28 FR France \n", + "1705 5 FR France \n", + "\n", + "[1706 rows x 10 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv(data_url, skiprows=1)\n", + "raw_data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([], dtype=int64), array([], dtype=int64))" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.where( raw_data.isnull() )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pas de valeur nulle!" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "data = raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "os données utilisent une convention inhabituelle: le numéro de semaine est collé à l'année, donnant l'impression qu'il s'agit de nombre entier. C'est comme ça que Pandas les interprète.\n", + "\n", + "Un deuxième problème est que Pandas ne comprend pas les numéros de semaine. Il faut lui fournir les dates de début et de fin de semaine. Nous utilisons pour cela la bibliothèque isoweek.\n", + "\n", + "Comme la conversion des semaines est devenu assez complexe, nous écrivons une petite fonction Python pour cela. Ensuite, nous l'appliquons à tous les points de nos donnés. Les résultats vont dans une nouvelle colonne 'period'.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def convert_week(year_and_week_int):\n", + "\n", + " year_and_week_str = str(year_and_week_int)\n", + "\n", + " year = int(year_and_week_str[:4])\n", + "\n", + " week = int(year_and_week_str[4:])\n", + "\n", + " w = isoweek.Week(year, week)\n", + "\n", + " return pd.Period(w.day(0), 'W')\n", + "\n", + "\n", + "data['period'] = [convert_week(yw) for yw in data['week']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il restent deux petites modifications à faire.\n", + "\n", + "Premièrement, nous définissons les périodes d'observation comme nouvel index de notre jeux de données. Ceci en fait une suite chronologique, ce qui sera pratique par la suite.\n", + "\n", + "Deuxièmement, nous trions les points par période, dans le sens chronologique.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data = data.set_index('period').sort_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous vérifions la cohérence des données. Entre la fin d'une période et le début de la période qui suit, la différence temporelle doit être zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\" d'une seconde.\n", + "\n", + "Ceci s'avère tout à fait juste." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "periods = sorted_data.index\n", + "for p1, p2 in zip(periods[:-1], periods[1:]):\n", + " delta = p2.to_timestamp() - p1.end_time\n", + " if delta > pd.Timedelta('1s'):\n", + " print(p1, p2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Un premier regard sur les données !" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc'].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en septembre." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc'][-100:].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Etude de l'incidence annuelle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Etant donné que le pic de l'épidémie se situe en hiver, à cheval entre deux années civiles, nous définissons la période de référence entre deux minima de l'incidence, du 1er septembre de l'année 𝑁 au 1er septembre de l'année 𝑁+1.\n", + "\n", + "Notre tâche est un peu compliquée par le fait que l'année ne comporte pas un nombre entier de semaines. Nous modifions donc un peu nos périodes de référence: à la place du 1er septembre de chaque année, nous utilisons le premier jour de la semaine qui contient le 1er septembre.\n", + "\n", + "Comme l'incidence de la varicelle est très faible en été, cette modification ne risque pas de fausser nos conclusions." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", + " for y in range(1991,\n", + " sorted_data.index[-1].year)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n", + "\n", + "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "year = []\n", + "yearly_incidence = []\n", + "pbs = []\n", + "for week1, week2 in zip(first_september_week[:-1],\n", + " first_september_week[1:]):\n", + " one_year = sorted_data['inc'][week1:week2-1]\n", + " if abs(len(one_year)-52) > 1:\n", + " pbs.append((one_year, abs(len(one_year)-52)))\n", + " yearly_incidence.append(one_year.sum())\n", + " year.append(week2.year)\n", + "assert len(pbs) == 0\n", + "yearly_incidence = pd.Series(data=yearly_incidence, index=year)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "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.plot(style='*')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2020 221186\n", + "2021 376290\n", + "2002 516689\n", + "2018 542312\n", + "2017 551041\n", + "1996 564901\n", + "2019 584066\n", + "2015 604382\n", + "2000 617597\n", + "2001 619041\n", + "2012 624573\n", + "2005 628464\n", + "2006 632833\n", + "2022 641397\n", + "2011 642368\n", + "1993 643387\n", + "1995 652478\n", + "1994 661409\n", + "1998 677775\n", + "1997 683434\n", + "2014 685769\n", + "2013 698332\n", + "2007 717352\n", + "2008 749478\n", + "1999 756456\n", + "2003 758363\n", + "2004 777388\n", + "2016 782114\n", + "2010 829911\n", + "1992 832939\n", + "2009 842373\n", + "dtype: int64" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence.sort_values()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +1396,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } - -- 2.18.1