name instead of year; ipynb generator debugged, with logging

[talweg.git] / reports / report_2017-03-01.13h.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "library(talweg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ts_data = read.csv(system.file(\"extdata\",\"pm10_mesures_H_loc_report.csv\",package=\"talweg\"))\n",
    "exo_data = read.csv(system.file(\"extdata\",\"meteo_extra_noNAs.csv\",package=\"talweg\"))\n",
    "data = getData(ts_data, exo_data, input_tz = \"Europe/Paris\", working_tz=\"Europe/Paris\", predict_at=13)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "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",
    "\n",
    " * simtype=\"mix\" : on utilise les similarités endogènes et exogènes (fenêtre optimisée par VC)\n",
    " * same_season=FALSE : les indices pour la validation croisée ne tiennent pas compte des saisons\n",
    " * mix_strategy=\"mult\" : on multiplie les poids (au lieu d'en éteindre)\n",
    "\n",
    "(valeurs par défaut).\n",
    "\n",
    "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",
    "\n",
    "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",
    "\n",
    "<h2 style=\"color:blue;font-size:2em\">Pollution par chauffage</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "indices_ch = seq(as.Date(\"2015-01-18\"),as.Date(\"2015-01-24\"),\"days\")\n",
    "p_ch_nn = computeForecast(data, indices_ch, \"Neighbors\", \"Neighbors\", simtype=\"mix\")\n",
    "p_ch_pz = computeForecast(data, indices_ch, \"Persistence\", \"Zero\", same_day=TRUE)\n",
    "p_ch_az = computeForecast(data, indices_ch, \"Average\", \"Zero\") #, memory=183)\n",
    "#p_ch_zz = computeForecast(data, indices_ch, \"Zero\", \"Zero\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "e_ch_nn = computeError(data, p_ch_nn)\n",
    "e_ch_pz = computeError(data, p_ch_pz)\n",
    "e_ch_az = computeError(data, p_ch_az)\n",
    "#e_ch_zz = computeError(data, p_ch_zz)\n",
    "options(repr.plot.width=9, repr.plot.height=7)\n",
    "plotError(list(e_ch_nn, e_ch_pz, e_ch_az), cols=c(1,2,colors()[258]))\n",
    "\n",
    "#Noir: neighbors, rouge: persistence, vert: moyenne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "par(mfrow=c(1,2))\n",
    "options(repr.plot.width=9, repr.plot.height=4)\n",
    "plotPredReal(data, p_ch_nn, 3)\n",
    "plotPredReal(data, p_ch_nn, 4)\n",
    "\n",
    "#Bleu: prévue, noir: réalisée"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "par(mfrow=c(1,2))\n",
    "plotPredReal(data, p_ch_az, 3)\n",
    "plotPredReal(data, p_ch_az, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "par(mfrow=c(1,2))\n",
    "f3_ch = computeFilaments(data, p_ch_nn$getIndexInData(3), plot=TRUE)\n",
    "f4_ch = computeFilaments(data, p_ch_nn$getIndexInData(4), plot=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "par(mfrow=c(1,2))\n",
    "plotFilamentsBox(data, f3_ch)\n",
    "plotFilamentsBox(data, f4_ch)\n",
    "\n",
    "#À gauche : jour 3 + lendemain (4) ; à droite : jour 4 + lendemain (5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "par(mfrow=c(1,2))\n",
    "plotRelVar(data, f3_ch)\n",
    "plotRelVar(data, f4_ch)\n",
    "\n",
    "#Variabilité globale en rouge ; sur nos 60 voisins (+ lendemains) en noir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "par(mfrow=c(1,2))\n",
    "plotSimils(p_ch_nn, 3)\n",
    "plotSimils(p_ch_nn, 4)\n",
    "\n",
    "#Non pollué à gauche, pollué à droite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n",
    "p_ch_nn$getParams(3)$window\n",
    "p_ch_nn$getParams(4)$window"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style=\"color:blue;font-size:2em\">Pollution par épandage</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "indices_ep = seq(as.Date(\"2015-03-15\"),as.Date(\"2015-03-21\"),\"days\")\n",
    "p_ep_nn = computeForecast(data,indices_ep, \"Neighbors\", \"Neighbors\", simtype=\"mix\")\n",
    "p_ep_pz = computeForecast(data, indices_ep, \"Persistence\", \"Zero\", same_day=TRUE)\n",
    "p_ep_az = computeForecast(data, indices_ep, \"Average\", \"Zero\") #, memory=183)\n",
    "#p_ep_zz = computeForecast(data, indices_ep, \"Zero\", \"Zero\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "e_ep_nn = computeError(data, p_ep_nn)\n",
    "e_ep_pz = computeError(data, p_ep_pz)\n",
    "e_ep_az = computeError(data, p_ep_az)\n",
    "#e_ep_zz = computeError(data, p_ep_zz)\n",
    "options(repr.plot.width=9, repr.plot.height=7)\n",
    "plotError(list(e_ep_nn, e_ep_pz, e_ep_az), cols=c(1,2,colors()[258]))\n",
    "\n",
    "#Noir: neighbors, rouge: persistence, vert: moyenne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "par(mfrow=c(1,2))\n",
    "options(repr.plot.width=9, repr.plot.height=4)\n",
    "plotPredReal(data, p_ep_nn, 6)\n",
    "plotPredReal(data, p_ep_nn, 3)\n",
    "\n",
    "#Bleu: prévue, noir: réalisée"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "par(mfrow=c(1,2))\n",
    "plotPredReal(data, p_ep_az, 6)\n",
    "plotPredReal(data, p_ep_az, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "par(mfrow=c(1,2))\n",
    "f6_ep = computeFilaments(data, p_ep_nn$getIndexInData(6), plot=TRUE)\n",
    "f3_ep = computeFilaments(data, p_ep_nn$getIndexInData(3), plot=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "par(mfrow=c(1,2))\n",
    "plotFilamentsBox(data, f6_ep)\n",
    "plotFilamentsBox(data, f3_ep)\n",
    "\n",
    "#À gauche : jour 6 + lendemain (7) ; à droite : jour 3 + lendemain (4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "par(mfrow=c(1,2))\n",
    "plotRelVar(data, f6_ep)\n",
    "plotRelVar(data, f3_ep)\n",
    "\n",
    "#Variabilité globale en rouge ; sur nos 60 voisins (+ lendemains) en noir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "par(mfrow=c(1,2))\n",
    "plotSimils(p_ep_nn, 6)\n",
    "plotSimils(p_ep_nn, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n",
    "p_ep_nn$getParams(6)$window\n",
    "p_ep_nn$getParams(3)$window"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style=\"color:blue;font-size:2em\">Semaine non polluée</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "indices_np = seq(as.Date(\"2015-04-26\"),as.Date(\"2015-05-02\"),\"days\")\n",
    "p_np_nn = computeForecast(data,indices_np, \"Neighbors\", \"Neighbors\", simtype=\"mix\")\n",
    "p_np_pz = computeForecast(data, indices_np, \"Persistence\", \"Zero\", same_day=FALSE)\n",
    "p_np_az = computeForecast(data, indices_np, \"Average\", \"Zero\") #, memory=183)\n",
    "#p_np_zz = computeForecast(data, indices_np, \"Zero\", \"Zero\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "e_np_nn = computeError(data, p_np_nn)\n",
    "e_np_pz = computeError(data, p_np_pz)\n",
    "e_np_az = computeError(data, p_np_az)\n",
    "#e_np_zz = computeError(data, p_np_zz)\n",
    "options(repr.plot.width=9, repr.plot.height=7)\n",
    "plotError(list(e_np_nn, e_np_pz, e_np_az), cols=c(1,2,colors()[258]))\n",
    "\n",
    "#Noir: neighbors, rouge: persistence, vert: moyenne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "par(mfrow=c(1,2))\n",
    "options(repr.plot.width=9, repr.plot.height=4)\n",
    "plotPredReal(data, p_np_nn, 5)\n",
    "plotPredReal(data, p_np_nn, 3)\n",
    "\n",
    "#Bleu: prévue, noir: réalisée"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "par(mfrow=c(1,2))\n",
    "plotPredReal(data, p_np_az, 5)\n",
    "plotPredReal(data, p_np_az, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "par(mfrow=c(1,2))\n",
    "f5_np = computeFilaments(data, p_np_nn$getIndexInData(5), plot=TRUE)\n",
    "f3_np = computeFilaments(data, p_np_nn$getIndexInData(3), plot=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "par(mfrow=c(1,2))\n",
    "plotFilamentsBox(data, f5_np)\n",
    "plotFilamentsBox(data, f3_np)\n",
    "\n",
    "#À gauche : jour 5 + lendemain (6) ; à droite : jour 3 + lendemain (4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "par(mfrow=c(1,2))\n",
    "plotRelVar(data, f5_np)\n",
    "plotRelVar(data, f3_np)\n",
    "\n",
    "#Variabilité globale en rouge ; sur nos 60 voisins (+ lendemains) en noir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "par(mfrow=c(1,2))\n",
    "plotSimils(p_np_nn, 5)\n",
    "plotSimils(p_np_nn, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n",
    "p_np_nn$getParams(5)$window\n",
    "p_np_nn$getParams(3)$window"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bilan\n",
    "\n",
    "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",
    "\n",
    "Comment améliorer la méthode ?"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.3.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}