| 1 | { |
| 2 | "cells": [ |
| 3 | { |
| 4 | "cell_type": "code", |
| 5 | "execution_count": null, |
| 6 | "metadata": { |
| 7 | "collapsed": false |
| 8 | }, |
| 9 | "outputs": [], |
| 10 | "source": [ |
| 11 | "library(talweg)" |
| 12 | ] |
| 13 | }, |
| 14 | { |
| 15 | "cell_type": "code", |
| 16 | "execution_count": null, |
| 17 | "metadata": { |
| 18 | "collapsed": false |
| 19 | }, |
| 20 | "outputs": [], |
| 21 | "source": [ |
| 22 | "ts_data = read.csv(system.file(\"extdata\",\"pm10_mesures_H_loc_report.csv\",package=\"talweg\"))\n", |
| 23 | "exo_data = read.csv(system.file(\"extdata\",\"meteo_extra_noNAs.csv\",package=\"talweg\"))\n", |
| 24 | "data = getData(ts_data, exo_data, input_tz = \"Europe/Paris\", working_tz=\"Europe/Paris\", predict_at=13)" |
| 25 | ] |
| 26 | }, |
| 27 | { |
| 28 | "cell_type": "markdown", |
| 29 | "metadata": {}, |
| 30 | "source": [ |
| 31 | "## Introduction\n", |
| 32 | "\n", |
| 33 | "J'ai fait quelques essais dans différentes configurations pour la méthode \"Neighbors\" (la seule dont on a parlé).<br>Il semble que le mieux soit\n", |
| 34 | "\n", |
| 35 | " * simtype=\"mix\" : on utilise les similarités endogènes et exogènes (fenêtre optimisée par VC)\n", |
| 36 | " * same_season=FALSE : les indices pour la validation croisée ne tiennent pas compte des saisons\n", |
| 37 | " * mix_strategy=\"mult\" : on multiplie les poids (au lieu d'en éteindre)\n", |
| 38 | "\n", |
| 39 | "(valeurs par défaut).\n", |
| 40 | "\n", |
| 41 | "J'ai systématiquement comparé à deux autres approches : la persistence et la moyennes des lendemains des jours \"similaires\" dans tout le passé ; à chaque fois sans prédiction du saut (sauf pour Neighbors : prédiction basée sur les poids calculés).\n", |
| 42 | "\n", |
| 43 | "Ensuite j'affiche les erreurs, quelques courbes prévues/mesurées, quelques filaments puis les histogrammes de quelques poids. Concernant les graphes de filaments, la moitié gauche du graphe correspond aux jours similaires au jour courant, tandis que la moitié droite affiche les lendemains : ce sont donc les voisinages tels qu'utilisés dans l'algorithme.\n", |
| 44 | "\n", |
| 45 | "<h2 style=\"color:blue;font-size:2em\">Pollution par chauffage</h2>" |
| 46 | ] |
| 47 | }, |
| 48 | { |
| 49 | "cell_type": "code", |
| 50 | "execution_count": null, |
| 51 | "metadata": { |
| 52 | "collapsed": false |
| 53 | }, |
| 54 | "outputs": [], |
| 55 | "source": [ |
| 56 | "indices_ch = seq(as.Date(\"2015-01-18\"),as.Date(\"2015-01-24\"),\"days\")\n", |
| 57 | "p_ch_nn = computeForecast(data, indices_ch, \"Neighbors\", \"Neighbors\", simtype=\"mix\")\n", |
| 58 | "p_ch_pz = computeForecast(data, indices_ch, \"Persistence\", \"Zero\", same_day=TRUE)\n", |
| 59 | "p_ch_az = computeForecast(data, indices_ch, \"Average\", \"Zero\") #, memory=183)\n", |
| 60 | "#p_ch_zz = computeForecast(data, indices_ch, \"Zero\", \"Zero\")" |
| 61 | ] |
| 62 | }, |
| 63 | { |
| 64 | "cell_type": "code", |
| 65 | "execution_count": null, |
| 66 | "metadata": { |
| 67 | "collapsed": false |
| 68 | }, |
| 69 | "outputs": [], |
| 70 | "source": [ |
| 71 | "e_ch_nn = computeError(data, p_ch_nn)\n", |
| 72 | "e_ch_pz = computeError(data, p_ch_pz)\n", |
| 73 | "e_ch_az = computeError(data, p_ch_az)\n", |
| 74 | "#e_ch_zz = computeError(data, p_ch_zz)\n", |
| 75 | "options(repr.plot.width=9, repr.plot.height=7)\n", |
| 76 | "plotError(list(e_ch_nn, e_ch_pz, e_ch_az), cols=c(1,2,colors()[258]))\n", |
| 77 | "\n", |
| 78 | "#Noir: neighbors, rouge: persistence, vert: moyenne" |
| 79 | ] |
| 80 | }, |
| 81 | { |
| 82 | "cell_type": "code", |
| 83 | "execution_count": null, |
| 84 | "metadata": { |
| 85 | "collapsed": false |
| 86 | }, |
| 87 | "outputs": [], |
| 88 | "source": [ |
| 89 | "par(mfrow=c(1,2))\n", |
| 90 | "options(repr.plot.width=9, repr.plot.height=4)\n", |
| 91 | "plotPredReal(data, p_ch_nn, 3)\n", |
| 92 | "plotPredReal(data, p_ch_nn, 4)\n", |
| 93 | "\n", |
| 94 | "#Bleu: prévue, noir: réalisée" |
| 95 | ] |
| 96 | }, |
| 97 | { |
| 98 | "cell_type": "code", |
| 99 | "execution_count": null, |
| 100 | "metadata": { |
| 101 | "collapsed": false |
| 102 | }, |
| 103 | "outputs": [], |
| 104 | "source": [ |
| 105 | "par(mfrow=c(1,2))\n", |
| 106 | "plotPredReal(data, p_ch_az, 3)\n", |
| 107 | "plotPredReal(data, p_ch_az, 4)" |
| 108 | ] |
| 109 | }, |
| 110 | { |
| 111 | "cell_type": "code", |
| 112 | "execution_count": null, |
| 113 | "metadata": { |
| 114 | "collapsed": false |
| 115 | }, |
| 116 | "outputs": [], |
| 117 | "source": [ |
| 118 | "par(mfrow=c(1,2))\n", |
| 119 | "f3_ch = computeFilaments(data, p_ch_nn$getIndexInData(3), plot=TRUE)\n", |
| 120 | "f4_ch = computeFilaments(data, p_ch_nn$getIndexInData(4), plot=TRUE)" |
| 121 | ] |
| 122 | }, |
| 123 | { |
| 124 | "cell_type": "code", |
| 125 | "execution_count": null, |
| 126 | "metadata": { |
| 127 | "collapsed": false |
| 128 | }, |
| 129 | "outputs": [], |
| 130 | "source": [ |
| 131 | "par(mfrow=c(1,2))\n", |
| 132 | "plotFilamentsBox(data, f3_ch)\n", |
| 133 | "plotFilamentsBox(data, f4_ch)\n", |
| 134 | "\n", |
| 135 | "#À gauche : jour 3 + lendemain (4) ; à droite : jour 4 + lendemain (5)" |
| 136 | ] |
| 137 | }, |
| 138 | { |
| 139 | "cell_type": "code", |
| 140 | "execution_count": null, |
| 141 | "metadata": { |
| 142 | "collapsed": false |
| 143 | }, |
| 144 | "outputs": [], |
| 145 | "source": [ |
| 146 | "par(mfrow=c(1,2))\n", |
| 147 | "plotRelVar(data, f3_ch)\n", |
| 148 | "plotRelVar(data, f4_ch)\n", |
| 149 | "\n", |
| 150 | "#Variabilité globale en rouge ; sur nos 60 voisins (+ lendemains) en noir" |
| 151 | ] |
| 152 | }, |
| 153 | { |
| 154 | "cell_type": "code", |
| 155 | "execution_count": null, |
| 156 | "metadata": { |
| 157 | "collapsed": false |
| 158 | }, |
| 159 | "outputs": [], |
| 160 | "source": [ |
| 161 | "par(mfrow=c(1,2))\n", |
| 162 | "plotSimils(p_ch_nn, 3)\n", |
| 163 | "plotSimils(p_ch_nn, 4)\n", |
| 164 | "\n", |
| 165 | "#Non pollué à gauche, pollué à droite" |
| 166 | ] |
| 167 | }, |
| 168 | { |
| 169 | "cell_type": "code", |
| 170 | "execution_count": null, |
| 171 | "metadata": { |
| 172 | "collapsed": false |
| 173 | }, |
| 174 | "outputs": [], |
| 175 | "source": [ |
| 176 | "#Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n", |
| 177 | "p_ch_nn$getParams(3)$window\n", |
| 178 | "p_ch_nn$getParams(4)$window" |
| 179 | ] |
| 180 | }, |
| 181 | { |
| 182 | "cell_type": "markdown", |
| 183 | "metadata": {}, |
| 184 | "source": [ |
| 185 | "<h2 style=\"color:blue;font-size:2em\">Pollution par épandage</h2>" |
| 186 | ] |
| 187 | }, |
| 188 | { |
| 189 | "cell_type": "code", |
| 190 | "execution_count": null, |
| 191 | "metadata": { |
| 192 | "collapsed": false |
| 193 | }, |
| 194 | "outputs": [], |
| 195 | "source": [ |
| 196 | "indices_ep = seq(as.Date(\"2015-03-15\"),as.Date(\"2015-03-21\"),\"days\")\n", |
| 197 | "p_ep_nn = computeForecast(data,indices_ep, \"Neighbors\", \"Neighbors\", simtype=\"mix\")\n", |
| 198 | "p_ep_pz = computeForecast(data, indices_ep, \"Persistence\", \"Zero\", same_day=TRUE)\n", |
| 199 | "p_ep_az = computeForecast(data, indices_ep, \"Average\", \"Zero\") #, memory=183)\n", |
| 200 | "#p_ep_zz = computeForecast(data, indices_ep, \"Zero\", \"Zero\")" |
| 201 | ] |
| 202 | }, |
| 203 | { |
| 204 | "cell_type": "code", |
| 205 | "execution_count": null, |
| 206 | "metadata": { |
| 207 | "collapsed": false |
| 208 | }, |
| 209 | "outputs": [], |
| 210 | "source": [ |
| 211 | "e_ep_nn = computeError(data, p_ep_nn)\n", |
| 212 | "e_ep_pz = computeError(data, p_ep_pz)\n", |
| 213 | "e_ep_az = computeError(data, p_ep_az)\n", |
| 214 | "#e_ep_zz = computeError(data, p_ep_zz)\n", |
| 215 | "options(repr.plot.width=9, repr.plot.height=7)\n", |
| 216 | "plotError(list(e_ep_nn, e_ep_pz, e_ep_az), cols=c(1,2,colors()[258]))\n", |
| 217 | "\n", |
| 218 | "#Noir: neighbors, rouge: persistence, vert: moyenne" |
| 219 | ] |
| 220 | }, |
| 221 | { |
| 222 | "cell_type": "code", |
| 223 | "execution_count": null, |
| 224 | "metadata": { |
| 225 | "collapsed": false |
| 226 | }, |
| 227 | "outputs": [], |
| 228 | "source": [ |
| 229 | "par(mfrow=c(1,2))\n", |
| 230 | "options(repr.plot.width=9, repr.plot.height=4)\n", |
| 231 | "plotPredReal(data, p_ep_nn, 6)\n", |
| 232 | "plotPredReal(data, p_ep_nn, 3)\n", |
| 233 | "\n", |
| 234 | "#Bleu: prévue, noir: réalisée" |
| 235 | ] |
| 236 | }, |
| 237 | { |
| 238 | "cell_type": "code", |
| 239 | "execution_count": null, |
| 240 | "metadata": { |
| 241 | "collapsed": false |
| 242 | }, |
| 243 | "outputs": [], |
| 244 | "source": [ |
| 245 | "par(mfrow=c(1,2))\n", |
| 246 | "plotPredReal(data, p_ep_az, 6)\n", |
| 247 | "plotPredReal(data, p_ep_az, 3)" |
| 248 | ] |
| 249 | }, |
| 250 | { |
| 251 | "cell_type": "code", |
| 252 | "execution_count": null, |
| 253 | "metadata": { |
| 254 | "collapsed": false |
| 255 | }, |
| 256 | "outputs": [], |
| 257 | "source": [ |
| 258 | "par(mfrow=c(1,2))\n", |
| 259 | "f6_ep = computeFilaments(data, p_ep_nn$getIndexInData(6), plot=TRUE)\n", |
| 260 | "f3_ep = computeFilaments(data, p_ep_nn$getIndexInData(3), plot=TRUE)" |
| 261 | ] |
| 262 | }, |
| 263 | { |
| 264 | "cell_type": "code", |
| 265 | "execution_count": null, |
| 266 | "metadata": { |
| 267 | "collapsed": false |
| 268 | }, |
| 269 | "outputs": [], |
| 270 | "source": [ |
| 271 | "par(mfrow=c(1,2))\n", |
| 272 | "plotFilamentsBox(data, f6_ep)\n", |
| 273 | "plotFilamentsBox(data, f3_ep)\n", |
| 274 | "\n", |
| 275 | "#À gauche : jour 6 + lendemain (7) ; à droite : jour 3 + lendemain (4)" |
| 276 | ] |
| 277 | }, |
| 278 | { |
| 279 | "cell_type": "code", |
| 280 | "execution_count": null, |
| 281 | "metadata": { |
| 282 | "collapsed": false |
| 283 | }, |
| 284 | "outputs": [], |
| 285 | "source": [ |
| 286 | "par(mfrow=c(1,2))\n", |
| 287 | "plotRelVar(data, f6_ep)\n", |
| 288 | "plotRelVar(data, f3_ep)\n", |
| 289 | "\n", |
| 290 | "#Variabilité globale en rouge ; sur nos 60 voisins (+ lendemains) en noir" |
| 291 | ] |
| 292 | }, |
| 293 | { |
| 294 | "cell_type": "code", |
| 295 | "execution_count": null, |
| 296 | "metadata": { |
| 297 | "collapsed": false |
| 298 | }, |
| 299 | "outputs": [], |
| 300 | "source": [ |
| 301 | "par(mfrow=c(1,2))\n", |
| 302 | "plotSimils(p_ep_nn, 6)\n", |
| 303 | "plotSimils(p_ep_nn, 3)" |
| 304 | ] |
| 305 | }, |
| 306 | { |
| 307 | "cell_type": "code", |
| 308 | "execution_count": null, |
| 309 | "metadata": { |
| 310 | "collapsed": false |
| 311 | }, |
| 312 | "outputs": [], |
| 313 | "source": [ |
| 314 | "#Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n", |
| 315 | "p_ep_nn$getParams(6)$window\n", |
| 316 | "p_ep_nn$getParams(3)$window" |
| 317 | ] |
| 318 | }, |
| 319 | { |
| 320 | "cell_type": "markdown", |
| 321 | "metadata": {}, |
| 322 | "source": [ |
| 323 | "<h2 style=\"color:blue;font-size:2em\">Semaine non polluée</h2>" |
| 324 | ] |
| 325 | }, |
| 326 | { |
| 327 | "cell_type": "code", |
| 328 | "execution_count": null, |
| 329 | "metadata": { |
| 330 | "collapsed": false |
| 331 | }, |
| 332 | "outputs": [], |
| 333 | "source": [ |
| 334 | "indices_np = seq(as.Date(\"2015-04-26\"),as.Date(\"2015-05-02\"),\"days\")\n", |
| 335 | "p_np_nn = computeForecast(data,indices_np, \"Neighbors\", \"Neighbors\", simtype=\"mix\")\n", |
| 336 | "p_np_pz = computeForecast(data, indices_np, \"Persistence\", \"Zero\", same_day=FALSE)\n", |
| 337 | "p_np_az = computeForecast(data, indices_np, \"Average\", \"Zero\") #, memory=183)\n", |
| 338 | "#p_np_zz = computeForecast(data, indices_np, \"Zero\", \"Zero\")" |
| 339 | ] |
| 340 | }, |
| 341 | { |
| 342 | "cell_type": "code", |
| 343 | "execution_count": null, |
| 344 | "metadata": { |
| 345 | "collapsed": false |
| 346 | }, |
| 347 | "outputs": [], |
| 348 | "source": [ |
| 349 | "e_np_nn = computeError(data, p_np_nn)\n", |
| 350 | "e_np_pz = computeError(data, p_np_pz)\n", |
| 351 | "e_np_az = computeError(data, p_np_az)\n", |
| 352 | "#e_np_zz = computeError(data, p_np_zz)\n", |
| 353 | "options(repr.plot.width=9, repr.plot.height=7)\n", |
| 354 | "plotError(list(e_np_nn, e_np_pz, e_np_az), cols=c(1,2,colors()[258]))\n", |
| 355 | "\n", |
| 356 | "#Noir: neighbors, rouge: persistence, vert: moyenne" |
| 357 | ] |
| 358 | }, |
| 359 | { |
| 360 | "cell_type": "code", |
| 361 | "execution_count": null, |
| 362 | "metadata": { |
| 363 | "collapsed": false |
| 364 | }, |
| 365 | "outputs": [], |
| 366 | "source": [ |
| 367 | "par(mfrow=c(1,2))\n", |
| 368 | "options(repr.plot.width=9, repr.plot.height=4)\n", |
| 369 | "plotPredReal(data, p_np_nn, 5)\n", |
| 370 | "plotPredReal(data, p_np_nn, 3)\n", |
| 371 | "\n", |
| 372 | "#Bleu: prévue, noir: réalisée" |
| 373 | ] |
| 374 | }, |
| 375 | { |
| 376 | "cell_type": "code", |
| 377 | "execution_count": null, |
| 378 | "metadata": { |
| 379 | "collapsed": false |
| 380 | }, |
| 381 | "outputs": [], |
| 382 | "source": [ |
| 383 | "par(mfrow=c(1,2))\n", |
| 384 | "plotPredReal(data, p_np_az, 5)\n", |
| 385 | "plotPredReal(data, p_np_az, 3)" |
| 386 | ] |
| 387 | }, |
| 388 | { |
| 389 | "cell_type": "code", |
| 390 | "execution_count": null, |
| 391 | "metadata": { |
| 392 | "collapsed": false |
| 393 | }, |
| 394 | "outputs": [], |
| 395 | "source": [ |
| 396 | "par(mfrow=c(1,2))\n", |
| 397 | "f5_np = computeFilaments(data, p_np_nn$getIndexInData(5), plot=TRUE)\n", |
| 398 | "f3_np = computeFilaments(data, p_np_nn$getIndexInData(3), plot=TRUE)" |
| 399 | ] |
| 400 | }, |
| 401 | { |
| 402 | "cell_type": "code", |
| 403 | "execution_count": null, |
| 404 | "metadata": { |
| 405 | "collapsed": false |
| 406 | }, |
| 407 | "outputs": [], |
| 408 | "source": [ |
| 409 | "par(mfrow=c(1,2))\n", |
| 410 | "plotFilamentsBox(data, f5_np)\n", |
| 411 | "plotFilamentsBox(data, f3_np)\n", |
| 412 | "\n", |
| 413 | "#À gauche : jour 5 + lendemain (6) ; à droite : jour 3 + lendemain (4)" |
| 414 | ] |
| 415 | }, |
| 416 | { |
| 417 | "cell_type": "code", |
| 418 | "execution_count": null, |
| 419 | "metadata": { |
| 420 | "collapsed": false |
| 421 | }, |
| 422 | "outputs": [], |
| 423 | "source": [ |
| 424 | "par(mfrow=c(1,2))\n", |
| 425 | "plotRelVar(data, f5_np)\n", |
| 426 | "plotRelVar(data, f3_np)\n", |
| 427 | "\n", |
| 428 | "#Variabilité globale en rouge ; sur nos 60 voisins (+ lendemains) en noir" |
| 429 | ] |
| 430 | }, |
| 431 | { |
| 432 | "cell_type": "code", |
| 433 | "execution_count": null, |
| 434 | "metadata": { |
| 435 | "collapsed": false |
| 436 | }, |
| 437 | "outputs": [], |
| 438 | "source": [ |
| 439 | "par(mfrow=c(1,2))\n", |
| 440 | "plotSimils(p_np_nn, 5)\n", |
| 441 | "plotSimils(p_np_nn, 3)" |
| 442 | ] |
| 443 | }, |
| 444 | { |
| 445 | "cell_type": "code", |
| 446 | "execution_count": null, |
| 447 | "metadata": { |
| 448 | "collapsed": false |
| 449 | }, |
| 450 | "outputs": [], |
| 451 | "source": [ |
| 452 | "#Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n", |
| 453 | "p_np_nn$getParams(5)$window\n", |
| 454 | "p_np_nn$getParams(3)$window" |
| 455 | ] |
| 456 | }, |
| 457 | { |
| 458 | "cell_type": "markdown", |
| 459 | "metadata": {}, |
| 460 | "source": [ |
| 461 | "## Bilan\n", |
| 462 | "\n", |
| 463 | "Problème difficile : on ne fait guère mieux qu'une naïve moyenne des lendemains des jours similaires dans le passé, ce qui n'est pas loin de prédire une série constante égale à la dernière valeur observée (méthode \"zéro\"). La persistence donne parfois de bons résultats mais est trop instable (sensibilité à l'argument <code>same_day</code>).\n", |
| 464 | "\n", |
| 465 | "Comment améliorer la méthode ?" |
| 466 | ] |
| 467 | } |
| 468 | ], |
| 469 | "metadata": { |
| 470 | "kernelspec": { |
| 471 | "display_name": "R", |
| 472 | "language": "R", |
| 473 | "name": "ir" |
| 474 | }, |
| 475 | "language_info": { |
| 476 | "codemirror_mode": "r", |
| 477 | "file_extension": ".r", |
| 478 | "mimetype": "text/x-r-source", |
| 479 | "name": "R", |
| 480 | "pygments_lexer": "r", |
| 481 | "version": "3.3.3" |
| 482 | } |
| 483 | }, |
| 484 | "nbformat": 4, |
| 485 | "nbformat_minor": 2 |
| 486 | } |