reports/report_7h_H3.ipynb

   1 {
   2  "cells": [
   3   {
   4    "cell_type": "markdown",
   5    "metadata": {},
   6    "source": [
   7     "\n",
   8     "\n",
   9     "<h2>Introduction</h2>\n",
  10     "\n",
  11     "J'ai fait quelques essais dans différentes configurations pour la méthode \"Neighbors\"\n",
  12     "(la seule dont on a parlé).<br>Il semble que le mieux soit\n",
  13     "\n",
  14     " * simtype=\"exo\" ou \"mix\" : similarités exogènes avec/sans endogènes (fenêtre optimisée par VC)\n",
  15     " * same_season=FALSE : les indices pour la validation croisée ne tiennent pas compte des saisons\n",
  16     " * mix_strategy=\"mult\" : on multiplie les poids (au lieu d'en éteindre)\n",
  17     "\n",
  18     "J'ai systématiquement comparé à une approche naïve : la moyennes des lendemains des jours\n",
  19     "\"similaires\" dans tout le passé ; à chaque fois sans prédiction du saut (sauf pour Neighbors :\n",
  20     "prédiction basée sur les poids calculés).\n",
  21     "\n",
  22     "Ensuite j'affiche les erreurs, quelques courbes prévues/mesurées, quelques filaments puis les\n",
  23     "histogrammes de quelques poids. Concernant les graphes de filaments, la moitié gauche du graphe\n",
  24     "correspond aux jours similaires au jour courant, tandis que la moitié droite affiche les\n",
  25     "lendemains : ce sont donc les voisinages tels qu'utilisés dans l'algorithme.\n",
  26     "\n"
  27    ]
  28   },
  29   {
  30    "cell_type": "code",
  31    "execution_count": null,
  32    "metadata": {
  33     "collapsed": false
  34    },
  35    "outputs": [],
  36    "source": [
  37     "library(talweg)\n",
  38     "\n",
  39     "ts_data = read.csv(system.file(\"extdata\",\"pm10_mesures_H_loc_report.csv\",package=\"talweg\"))\n",
  40     "exo_data = read.csv(system.file(\"extdata\",\"meteo_extra_noNAs.csv\",package=\"talweg\"))\n",
  41     "data = getData(ts_data, exo_data, input_tz = \"Europe/Paris\", working_tz=\"Europe/Paris\",\n",
  42     "\tpredict_at=7) #predict from P+1 to P+H included\n",
  43     "\n",
  44     "indices_ch = seq(as.Date(\"2015-01-18\"),as.Date(\"2015-01-24\"),\"days\")\n",
  45     "indices_ep = seq(as.Date(\"2015-03-15\"),as.Date(\"2015-03-21\"),\"days\")\n",
  46     "indices_np = seq(as.Date(\"2015-04-26\"),as.Date(\"2015-05-02\"),\"days\")"
  47    ]
  48   },
  49   {
  50    "cell_type": "markdown",
  51    "metadata": {},
  52    "source": [
  53     "\n",
  54     "\n",
  55     "<h2 style=\"color:blue;font-size:2em\">Pollution par chauffage</h2>"
  56    ]
  57   },
  58   {
  59    "cell_type": "code",
  60    "execution_count": null,
  61    "metadata": {
  62     "collapsed": false
  63    },
  64    "outputs": [],
  65    "source": [
  66     "p_nn_exo = computeForecast(data, indices_ch, \"Neighbors\", \"Neighbors\",\n",
  67     "\thorizon=3, simtype=\"exo\")\n",
  68     "p_nn_mix = computeForecast(data, indices_ch, \"Neighbors\", \"Neighbors\",\n",
  69     "\thorizon=3, simtype=\"mix\")\n",
  70     "p_az = computeForecast(data, indices_ch, \"Average\", \"Zero\",\n",
  71     "\thorizon=3)\n",
  72     "p_pz = computeForecast(data, indices_ch, \"Persistence\", \"Zero\",\n",
  73     "\thorizon=3, same_day=TRUE)"
  74    ]
  75   },
  76   {
  77    "cell_type": "code",
  78    "execution_count": null,
  79    "metadata": {
  80     "collapsed": false
  81    },
  82    "outputs": [],
  83    "source": [
  84     "e_nn_exo = computeError(data, p_nn_exo, 3)\n",
  85     "e_nn_mix = computeError(data, p_nn_mix, 3)\n",
  86     "e_az = computeError(data, p_az, 3)\n",
  87     "e_pz = computeError(data, p_pz, 3)\n",
  88     "options(repr.plot.width=9, repr.plot.height=7)\n",
  89     "plotError(list(e_nn_mix, e_pz, e_az, e_nn_exo), cols=c(1,2,colors()[258], 4))\n",
  90     "\n",
  91     "# Noir: neighbors_mix, bleu: neighbors_exo, vert: moyenne, rouge: persistence\n",
  92     "\n",
  93     "i_np = which.min(e_nn_exo$abs$indices)\n",
  94     "i_p = which.max(e_nn_exo$abs$indices)"
  95    ]
  96   },
  97   {
  98    "cell_type": "code",
  99    "execution_count": null,
 100    "metadata": {
 101     "collapsed": false
 102    },
 103    "outputs": [],
 104    "source": [
 105     "options(repr.plot.width=9, repr.plot.height=4)\n",
 106     "par(mfrow=c(1,2))\n",
 107     "\n",
 108     "plotPredReal(data, p_nn_exo, i_np); title(paste(\"PredReal nn exo day\",i_np))\n",
 109     "plotPredReal(data, p_nn_exo, i_p); title(paste(\"PredReal nn exo day\",i_p))\n",
 110     "\n",
 111     "plotPredReal(data, p_nn_mix, i_np); title(paste(\"PredReal nn mix day\",i_np))\n",
 112     "plotPredReal(data, p_nn_mix, i_p); title(paste(\"PredReal nn mix day\",i_p))\n",
 113     "\n",
 114     "plotPredReal(data, p_az, i_np); title(paste(\"PredReal az day\",i_np))\n",
 115     "plotPredReal(data, p_az, i_p); title(paste(\"PredReal az day\",i_p))\n",
 116     "\n",
 117     "# Bleu: prévue, noir: réalisée"
 118    ]
 119   },
 120   {
 121    "cell_type": "code",
 122    "execution_count": null,
 123    "metadata": {
 124     "collapsed": false
 125    },
 126    "outputs": [],
 127    "source": [
 128     "par(mfrow=c(1,2))\n",
 129     "f_np_exo = computeFilaments(data, p_nn_exo, i_np, plot=TRUE); title(paste(\"Filaments nn exo day\",i_np))\n",
 130     "f_p_exo = computeFilaments(data, p_nn_exo, i_p, plot=TRUE); title(paste(\"Filaments nn exo day\",i_p))\n",
 131     "\n",
 132     "f_np_mix = computeFilaments(data, p_nn_mix, i_np, plot=TRUE); title(paste(\"Filaments nn mix day\",i_np))\n",
 133     "f_p_mix = computeFilaments(data, p_nn_mix, i_p, plot=TRUE); title(paste(\"Filaments nn mix day\",i_p))"
 134    ]
 135   },
 136   {
 137    "cell_type": "code",
 138    "execution_count": null,
 139    "metadata": {
 140     "collapsed": false
 141    },
 142    "outputs": [],
 143    "source": [
 144     "par(mfrow=c(1,2))\n",
 145     "plotFilamentsBox(data, f_np_exo); title(paste(\"FilBox nn exo day\",i_np))\n",
 146     "plotFilamentsBox(data, f_p_exo); title(paste(\"FilBox nn exo day\",i_p))\n",
 147     "\n",
 148     "plotFilamentsBox(data, f_np_mix); title(paste(\"FilBox nn mix day\",i_np))\n",
 149     "plotFilamentsBox(data, f_p_mix); title(paste(\"FilBox nn mix day\",i_p))"
 150    ]
 151   },
 152   {
 153    "cell_type": "code",
 154    "execution_count": null,
 155    "metadata": {
 156     "collapsed": false
 157    },
 158    "outputs": [],
 159    "source": [
 160     "par(mfrow=c(1,2))\n",
 161     "plotRelVar(data, f_np_exo); title(paste(\"StdDev nn exo day\",i_np))\n",
 162     "plotRelVar(data, f_p_exo); title(paste(\"StdDev nn exo day\",i_p))\n",
 163     "\n",
 164     "plotRelVar(data, f_np_mix); title(paste(\"StdDev nn mix day\",i_np))\n",
 165     "plotRelVar(data, f_p_mix); title(paste(\"StdDev nn mix day\",i_p))\n",
 166     "\n",
 167     "# Variabilité globale en rouge ; sur les 60 voisins (+ lendemains) en noir"
 168    ]
 169   },
 170   {
 171    "cell_type": "code",
 172    "execution_count": null,
 173    "metadata": {
 174     "collapsed": false
 175    },
 176    "outputs": [],
 177    "source": [
 178     "par(mfrow=c(1,2))\n",
 179     "plotSimils(p_nn_exo, i_np); title(paste(\"Weights nn exo day\",i_np))\n",
 180     "plotSimils(p_nn_exo, i_p); title(paste(\"Weights nn exo day\",i_p))\n",
 181     "\n",
 182     "plotSimils(p_nn_mix, i_np); title(paste(\"Weights nn mix day\",i_np))\n",
 183     "plotSimils(p_nn_mix, i_p); title(paste(\"Weights nn mix day\",i_p))\n",
 184     "\n",
 185     "# - pollué à gauche, + pollué à droite"
 186    ]
 187   },
 188   {
 189    "cell_type": "code",
 190    "execution_count": null,
 191    "metadata": {
 192     "collapsed": false
 193    },
 194    "outputs": [],
 195    "source": [
 196     "# Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n",
 197     "p_nn_exo$getParams(i_np)$window\n",
 198     "p_nn_exo$getParams(i_p)$window\n",
 199     "\n",
 200     "p_nn_mix$getParams(i_np)$window\n",
 201     "p_nn_mix$getParams(i_p)$window"
 202    ]
 203   },
 204   {
 205    "cell_type": "markdown",
 206    "metadata": {},
 207    "source": [
 208     "\n",
 209     "\n",
 210     "<h2 style=\"color:blue;font-size:2em\">Pollution par épandage</h2>"
 211    ]
 212   },
 213   {
 214    "cell_type": "code",
 215    "execution_count": null,
 216    "metadata": {
 217     "collapsed": false
 218    },
 219    "outputs": [],
 220    "source": [
 221     "p_nn_exo = computeForecast(data, indices_ep, \"Neighbors\", \"Neighbors\",\n",
 222     "\thorizon=3, simtype=\"exo\")\n",
 223     "p_nn_mix = computeForecast(data, indices_ep, \"Neighbors\", \"Neighbors\",\n",
 224     "\thorizon=3, simtype=\"mix\")\n",
 225     "p_az = computeForecast(data, indices_ep, \"Average\", \"Zero\",\n",
 226     "\thorizon=3)\n",
 227     "p_pz = computeForecast(data, indices_ep, \"Persistence\", \"Zero\",\n",
 228     "\thorizon=3, same_day=TRUE)"
 229    ]
 230   },
 231   {
 232    "cell_type": "code",
 233    "execution_count": null,
 234    "metadata": {
 235     "collapsed": false
 236    },
 237    "outputs": [],
 238    "source": [
 239     "e_nn_exo = computeError(data, p_nn_exo, 3)\n",
 240     "e_nn_mix = computeError(data, p_nn_mix, 3)\n",
 241     "e_az = computeError(data, p_az, 3)\n",
 242     "e_pz = computeError(data, p_pz, 3)\n",
 243     "options(repr.plot.width=9, repr.plot.height=7)\n",
 244     "plotError(list(e_nn_mix, e_pz, e_az, e_nn_exo), cols=c(1,2,colors()[258], 4))\n",
 245     "\n",
 246     "# Noir: neighbors_mix, bleu: neighbors_exo, vert: moyenne, rouge: persistence\n",
 247     "\n",
 248     "i_np = which.min(e_nn_exo$abs$indices)\n",
 249     "i_p = which.max(e_nn_exo$abs$indices)"
 250    ]
 251   },
 252   {
 253    "cell_type": "code",
 254    "execution_count": null,
 255    "metadata": {
 256     "collapsed": false
 257    },
 258    "outputs": [],
 259    "source": [
 260     "options(repr.plot.width=9, repr.plot.height=4)\n",
 261     "par(mfrow=c(1,2))\n",
 262     "\n",
 263     "plotPredReal(data, p_nn_exo, i_np); title(paste(\"PredReal nn exo day\",i_np))\n",
 264     "plotPredReal(data, p_nn_exo, i_p); title(paste(\"PredReal nn exo day\",i_p))\n",
 265     "\n",
 266     "plotPredReal(data, p_nn_mix, i_np); title(paste(\"PredReal nn mix day\",i_np))\n",
 267     "plotPredReal(data, p_nn_mix, i_p); title(paste(\"PredReal nn mix day\",i_p))\n",
 268     "\n",
 269     "plotPredReal(data, p_az, i_np); title(paste(\"PredReal az day\",i_np))\n",
 270     "plotPredReal(data, p_az, i_p); title(paste(\"PredReal az day\",i_p))\n",
 271     "\n",
 272     "# Bleu: prévue, noir: réalisée"
 273    ]
 274   },
 275   {
 276    "cell_type": "code",
 277    "execution_count": null,
 278    "metadata": {
 279     "collapsed": false
 280    },
 281    "outputs": [],
 282    "source": [
 283     "par(mfrow=c(1,2))\n",
 284     "f_np_exo = computeFilaments(data, p_nn_exo, i_np, plot=TRUE); title(paste(\"Filaments nn exo day\",i_np))\n",
 285     "f_p_exo = computeFilaments(data, p_nn_exo, i_p, plot=TRUE); title(paste(\"Filaments nn exo day\",i_p))\n",
 286     "\n",
 287     "f_np_mix = computeFilaments(data, p_nn_mix, i_np, plot=TRUE); title(paste(\"Filaments nn mix day\",i_np))\n",
 288     "f_p_mix = computeFilaments(data, p_nn_mix, i_p, plot=TRUE); title(paste(\"Filaments nn mix day\",i_p))"
 289    ]
 290   },
 291   {
 292    "cell_type": "code",
 293    "execution_count": null,
 294    "metadata": {
 295     "collapsed": false
 296    },
 297    "outputs": [],
 298    "source": [
 299     "par(mfrow=c(1,2))\n",
 300     "plotFilamentsBox(data, f_np_exo); title(paste(\"FilBox nn exo day\",i_np))\n",
 301     "plotFilamentsBox(data, f_p_exo); title(paste(\"FilBox nn exo day\",i_p))\n",
 302     "\n",
 303     "plotFilamentsBox(data, f_np_mix); title(paste(\"FilBox nn mix day\",i_np))\n",
 304     "plotFilamentsBox(data, f_p_mix); title(paste(\"FilBox nn mix day\",i_p))"
 305    ]
 306   },
 307   {
 308    "cell_type": "code",
 309    "execution_count": null,
 310    "metadata": {
 311     "collapsed": false
 312    },
 313    "outputs": [],
 314    "source": [
 315     "par(mfrow=c(1,2))\n",
 316     "plotRelVar(data, f_np_exo); title(paste(\"StdDev nn exo day\",i_np))\n",
 317     "plotRelVar(data, f_p_exo); title(paste(\"StdDev nn exo day\",i_p))\n",
 318     "\n",
 319     "plotRelVar(data, f_np_mix); title(paste(\"StdDev nn mix day\",i_np))\n",
 320     "plotRelVar(data, f_p_mix); title(paste(\"StdDev nn mix day\",i_p))\n",
 321     "\n",
 322     "# Variabilité globale en rouge ; sur les 60 voisins (+ lendemains) en noir"
 323    ]
 324   },
 325   {
 326    "cell_type": "code",
 327    "execution_count": null,
 328    "metadata": {
 329     "collapsed": false
 330    },
 331    "outputs": [],
 332    "source": [
 333     "par(mfrow=c(1,2))\n",
 334     "plotSimils(p_nn_exo, i_np); title(paste(\"Weights nn exo day\",i_np))\n",
 335     "plotSimils(p_nn_exo, i_p); title(paste(\"Weights nn exo day\",i_p))\n",
 336     "\n",
 337     "plotSimils(p_nn_mix, i_np); title(paste(\"Weights nn mix day\",i_np))\n",
 338     "plotSimils(p_nn_mix, i_p); title(paste(\"Weights nn mix day\",i_p))\n",
 339     "\n",
 340     "# - pollué à gauche, + pollué à droite"
 341    ]
 342   },
 343   {
 344    "cell_type": "code",
 345    "execution_count": null,
 346    "metadata": {
 347     "collapsed": false
 348    },
 349    "outputs": [],
 350    "source": [
 351     "# Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n",
 352     "p_nn_exo$getParams(i_np)$window\n",
 353     "p_nn_exo$getParams(i_p)$window\n",
 354     "\n",
 355     "p_nn_mix$getParams(i_np)$window\n",
 356     "p_nn_mix$getParams(i_p)$window"
 357    ]
 358   },
 359   {
 360    "cell_type": "markdown",
 361    "metadata": {},
 362    "source": [
 363     "\n",
 364     "\n",
 365     "<h2 style=\"color:blue;font-size:2em\">Semaine non polluée</h2>"
 366    ]
 367   },
 368   {
 369    "cell_type": "code",
 370    "execution_count": null,
 371    "metadata": {
 372     "collapsed": false
 373    },
 374    "outputs": [],
 375    "source": [
 376     "p_nn_exo = computeForecast(data, indices_np, \"Neighbors\", \"Neighbors\",\n",
 377     "\thorizon=3, simtype=\"exo\")\n",
 378     "p_nn_mix = computeForecast(data, indices_np, \"Neighbors\", \"Neighbors\",\n",
 379     "\thorizon=3, simtype=\"mix\")\n",
 380     "p_az = computeForecast(data, indices_np, \"Average\", \"Zero\",\n",
 381     "\thorizon=3)\n",
 382     "p_pz = computeForecast(data, indices_np, \"Persistence\", \"Zero\",\n",
 383     "\thorizon=3, same_day=TRUE)"
 384    ]
 385   },
 386   {
 387    "cell_type": "code",
 388    "execution_count": null,
 389    "metadata": {
 390     "collapsed": false
 391    },
 392    "outputs": [],
 393    "source": [
 394     "e_nn_exo = computeError(data, p_nn_exo, 3)\n",
 395     "e_nn_mix = computeError(data, p_nn_mix, 3)\n",
 396     "e_az = computeError(data, p_az, 3)\n",
 397     "e_pz = computeError(data, p_pz, 3)\n",
 398     "options(repr.plot.width=9, repr.plot.height=7)\n",
 399     "plotError(list(e_nn_mix, e_pz, e_az, e_nn_exo), cols=c(1,2,colors()[258], 4))\n",
 400     "\n",
 401     "# Noir: neighbors_mix, bleu: neighbors_exo, vert: moyenne, rouge: persistence\n",
 402     "\n",
 403     "i_np = which.min(e_nn_exo$abs$indices)\n",
 404     "i_p = which.max(e_nn_exo$abs$indices)"
 405    ]
 406   },
 407   {
 408    "cell_type": "code",
 409    "execution_count": null,
 410    "metadata": {
 411     "collapsed": false
 412    },
 413    "outputs": [],
 414    "source": [
 415     "options(repr.plot.width=9, repr.plot.height=4)\n",
 416     "par(mfrow=c(1,2))\n",
 417     "\n",
 418     "plotPredReal(data, p_nn_exo, i_np); title(paste(\"PredReal nn exo day\",i_np))\n",
 419     "plotPredReal(data, p_nn_exo, i_p); title(paste(\"PredReal nn exo day\",i_p))\n",
 420     "\n",
 421     "plotPredReal(data, p_nn_mix, i_np); title(paste(\"PredReal nn mix day\",i_np))\n",
 422     "plotPredReal(data, p_nn_mix, i_p); title(paste(\"PredReal nn mix day\",i_p))\n",
 423     "\n",
 424     "plotPredReal(data, p_az, i_np); title(paste(\"PredReal az day\",i_np))\n",
 425     "plotPredReal(data, p_az, i_p); title(paste(\"PredReal az day\",i_p))\n",
 426     "\n",
 427     "# Bleu: prévue, noir: réalisée"
 428    ]
 429   },
 430   {
 431    "cell_type": "code",
 432    "execution_count": null,
 433    "metadata": {
 434     "collapsed": false
 435    },
 436    "outputs": [],
 437    "source": [
 438     "par(mfrow=c(1,2))\n",
 439     "f_np_exo = computeFilaments(data, p_nn_exo, i_np, plot=TRUE); title(paste(\"Filaments nn exo day\",i_np))\n",
 440     "f_p_exo = computeFilaments(data, p_nn_exo, i_p, plot=TRUE); title(paste(\"Filaments nn exo day\",i_p))\n",
 441     "\n",
 442     "f_np_mix = computeFilaments(data, p_nn_mix, i_np, plot=TRUE); title(paste(\"Filaments nn mix day\",i_np))\n",
 443     "f_p_mix = computeFilaments(data, p_nn_mix, i_p, plot=TRUE); title(paste(\"Filaments nn mix day\",i_p))"
 444    ]
 445   },
 446   {
 447    "cell_type": "code",
 448    "execution_count": null,
 449    "metadata": {
 450     "collapsed": false
 451    },
 452    "outputs": [],
 453    "source": [
 454     "par(mfrow=c(1,2))\n",
 455     "plotFilamentsBox(data, f_np_exo); title(paste(\"FilBox nn exo day\",i_np))\n",
 456     "plotFilamentsBox(data, f_p_exo); title(paste(\"FilBox nn exo day\",i_p))\n",
 457     "\n",
 458     "plotFilamentsBox(data, f_np_mix); title(paste(\"FilBox nn mix day\",i_np))\n",
 459     "plotFilamentsBox(data, f_p_mix); title(paste(\"FilBox nn mix day\",i_p))"
 460    ]
 461   },
 462   {
 463    "cell_type": "code",
 464    "execution_count": null,
 465    "metadata": {
 466     "collapsed": false
 467    },
 468    "outputs": [],
 469    "source": [
 470     "par(mfrow=c(1,2))\n",
 471     "plotRelVar(data, f_np_exo); title(paste(\"StdDev nn exo day\",i_np))\n",
 472     "plotRelVar(data, f_p_exo); title(paste(\"StdDev nn exo day\",i_p))\n",
 473     "\n",
 474     "plotRelVar(data, f_np_mix); title(paste(\"StdDev nn mix day\",i_np))\n",
 475     "plotRelVar(data, f_p_mix); title(paste(\"StdDev nn mix day\",i_p))\n",
 476     "\n",
 477     "# Variabilité globale en rouge ; sur les 60 voisins (+ lendemains) en noir"
 478    ]
 479   },
 480   {
 481    "cell_type": "code",
 482    "execution_count": null,
 483    "metadata": {
 484     "collapsed": false
 485    },
 486    "outputs": [],
 487    "source": [
 488     "par(mfrow=c(1,2))\n",
 489     "plotSimils(p_nn_exo, i_np); title(paste(\"Weights nn exo day\",i_np))\n",
 490     "plotSimils(p_nn_exo, i_p); title(paste(\"Weights nn exo day\",i_p))\n",
 491     "\n",
 492     "plotSimils(p_nn_mix, i_np); title(paste(\"Weights nn mix day\",i_np))\n",
 493     "plotSimils(p_nn_mix, i_p); title(paste(\"Weights nn mix day\",i_p))\n",
 494     "\n",
 495     "# - pollué à gauche, + pollué à droite"
 496    ]
 497   },
 498   {
 499    "cell_type": "code",
 500    "execution_count": null,
 501    "metadata": {
 502     "collapsed": false
 503    },
 504    "outputs": [],
 505    "source": [
 506     "# Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n",
 507     "p_nn_exo$getParams(i_np)$window\n",
 508     "p_nn_exo$getParams(i_p)$window\n",
 509     "\n",
 510     "p_nn_mix$getParams(i_np)$window\n",
 511     "p_nn_mix$getParams(i_p)$window"
 512    ]
 513   },
 514   {
 515    "cell_type": "markdown",
 516    "metadata": {},
 517    "source": [
 518     "\n",
 519     "\n",
 520     "<h2>Bilan</h2>\n",
 521     "\n",
 522     "Problème difficile : on ne fait guère mieux qu'une naïve moyenne des lendemains des jours\n",
 523     "similaires dans le passé, ce qui n'est pas loin de prédire une série constante égale à la\n",
 524     "dernière valeur observée (méthode \"zéro\"). La persistence donne parfois de bons résultats\n",
 525     "mais est trop instable (sensibilité à l'argument <code>same_day</code>).\n",
 526     "\n",
 527     "Comment améliorer la méthode ?"
 528    ]
 529   }
 530  ],
 531  "metadata": {
 532   "kernelspec": {
 533    "display_name": "R",
 534    "language": "R",
 535    "name": "ir"
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 539    "file_extension": ".r",
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 548 }