Auto-update: 2025-10-23 19:23:57
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@ -4,13 +4,11 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Classification des Micro-régions de Corse par K-NN\n",
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"# Classification k-NN Corse - Version ipyleaflet\n",
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"\n",
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"Ce notebook implémente un système de classification des micro-régions corses basé sur l'algorithme des k plus proches voisins (k-NN). Cliquez sur la carte pour identifier la micro-région correspondante.\n",
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"Cette version utilise **ipyleaflet** avec des interactions Python natives (pas de JavaScript inject\u00e9).\n",
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"\n",
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"**Fichiers nécessaires :**\n",
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"- `communes-de-corse-en-corse-et-francais.csv` : Liste des communes avec coordonnées GPS\n",
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"- `communes-par-territoire-de-projet-de-la-collectivite-territoriale-de-corse0.csv` : Territoires de projet par commune"
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"**Avantage :** Fonctionne parfaitement dans Jupyter sans probl\u00e8me d'iframe ou de JavaScript."
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]
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},
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{
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@ -19,8 +17,8 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# Installation des bibliothèques nécessaires\n",
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"!pip install folium pandas numpy scikit-learn --quiet"
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"# Installation\n",
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"!pip install ipyleaflet ipywidgets pandas numpy scikit-learn --quiet"
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]
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},
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{
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@ -31,18 +29,17 @@
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import folium\n",
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"from ipyleaflet import Map, CircleMarker, Marker, Polyline, LayerGroup, WidgetControl, AwesomeIcon\n",
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"from ipywidgets import HTML, VBox, HBox, Label, IntSlider, Output\n",
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"from sklearn.neighbors import KNeighborsClassifier\n",
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"from IPython.display import display, HTML\n",
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"import json\n",
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"import re"
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"from IPython.display import display\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1. Chargement et préparation des données"
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"## 1. Chargement des donn\u00e9es"
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]
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},
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{
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@ -51,39 +48,13 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# Chargement du fichier avec les coordonnées GPS\n",
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"df_coords = pd.read_csv('communes-de-corse-en-corse-et-francais.csv', \n",
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"# Chargement\n",
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"df_coords = pd.read_csv('communes-de-corse-en-corse-et-francais.csv', sep=';', encoding='utf-8')\n",
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"df_territoires = pd.read_csv('communes-par-territoire-de-projet-de-la-collectivite-territoriale-de-corse0.csv', \n",
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" sep=';', encoding='utf-8')\n",
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"\n",
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"print(f\"Fichier coordonnées: {len(df_coords)} communes\")\n",
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"print(\"\\nPremières lignes:\")\n",
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"display(df_coords.head())\n",
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"print(\"\\nColonnes disponibles:\")\n",
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"print(df_coords.columns.tolist())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Chargement du fichier avec les territoires de projet\n",
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"df_territoires = pd.read_csv('communes-par-territoire-de-projet-de-la-collectivite-territoriale-de-corse0.csv',\n",
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" sep=';', encoding='utf-8')\n",
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"\n",
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"print(f\"Fichier territoires: {len(df_territoires)} communes\")\n",
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"print(\"\\nPremières lignes:\")\n",
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"display(df_territoires.head())\n",
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"print(\"\\nTerritoires de projet (micro-régions):\")\n",
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"print(sorted(df_territoires['Territoire de projet'].unique()))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 2. Extraction des coordonnées GPS"
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"print(f\"\u2705 {len(df_coords)} communes avec coordonn\u00e9es\")\n",
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"print(f\"\u2705 {len(df_territoires)} communes avec territoires\")"
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]
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},
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{
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@ -92,43 +63,23 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# Extraction coordonn\u00e9es\n",
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"def extract_coordinates(point_geo_str):\n",
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" \"\"\"\n",
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" Extrait latitude et longitude de la colonne Point_Geo\n",
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" Format attendu: \"41.984099158, 8.798384636\"\n",
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" \"\"\"\n",
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" if pd.isna(point_geo_str):\n",
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" return None, None\n",
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" \n",
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" try:\n",
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" # Supprimer les espaces et split par virgule\n",
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" coords = str(point_geo_str).strip().split(',')\n",
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" if len(coords) == 2:\n",
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" lat = float(coords[0].strip())\n",
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" lon = float(coords[1].strip())\n",
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" return lat, lon\n",
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" return float(coords[0].strip()), float(coords[1].strip())\n",
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" except:\n",
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" pass\n",
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" \n",
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" return None, None\n",
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"\n",
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"# Extraction des coordonnées\n",
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"df_coords[['Latitude', 'Longitude']] = df_coords['Point_Geo'].apply(\n",
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" lambda x: pd.Series(extract_coordinates(x))\n",
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")\n",
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"\n",
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"# Vérification\n",
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"print(\"Extraction des coordonnées:\")\n",
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"print(f\"Communes avec coordonnées: {df_coords['Latitude'].notna().sum()}/{len(df_coords)}\")\n",
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"print(\"\\nExemple:\")\n",
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"display(df_coords[['Nom français', 'Latitude', 'Longitude']].head())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 3. Fusion des deux fichiers"
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"print(f\"\u2705 {df_coords['Latitude'].notna().sum()} coordonn\u00e9es extraites\")"
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]
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},
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{
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@ -137,54 +88,30 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# Normalisation des noms de communes pour la jointure\n",
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"def normalize_commune_name(name):\n",
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" \"\"\"\n",
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" Normalise le nom d'une commune pour faciliter la jointure\n",
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" \"\"\"\n",
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" if pd.isna(name):\n",
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" return ''\n",
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" # Convertir en majuscules et supprimer les espaces multiples\n",
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" return str(name).upper().strip()\n",
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"# Fusion\n",
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"def normalize(name):\n",
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" return str(name).upper().strip() if not pd.isna(name) else ''\n",
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"\n",
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"df_coords['Commune_norm'] = df_coords['Nom français'].apply(normalize_commune_name)\n",
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"df_territoires['Commune_norm'] = df_territoires['Commune'].apply(normalize_commune_name)\n",
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"df_coords['Commune_norm'] = df_coords['Nom fran\u00e7ais'].apply(normalize)\n",
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"df_territoires['Commune_norm'] = df_territoires['Commune'].apply(normalize)\n",
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"\n",
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"# Fusion des deux dataframes\n",
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"df = pd.merge(\n",
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" df_coords,\n",
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" df_territoires[['Commune_norm', 'Territoire de projet']],\n",
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" on='Commune_norm',\n",
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" how='inner'\n",
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")\n",
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"\n",
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"# Renommer pour cohérence\n",
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"df['Commune'] = df['Nom français']\n",
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"\n",
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"print(f\"Fusion réussie: {len(df)} communes avec coordonnées ET territoire de projet\")\n",
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"print(\"\\nAperçu des données fusionnées:\")\n",
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"display(df[['Commune', 'Latitude', 'Longitude', 'Territoire de projet']].head(10))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Nettoyage: supprimer les lignes sans coordonnées\n",
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"df = pd.merge(df_coords, df_territoires[['Commune_norm', 'Territoire de projet']], \n",
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" on='Commune_norm', how='inner')\n",
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"df['Commune'] = df['Nom fran\u00e7ais']\n",
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"df_clean = df.dropna(subset=['Latitude', 'Longitude', 'Territoire de projet']).copy()\n",
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"\n",
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"print(f\"\\n✅ Données finales: {len(df_clean)} communes prêtes pour la classification\")\n",
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"print(f\"\\nRépartition par micro-région:\")\n",
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"print(df_clean['Territoire de projet'].value_counts().sort_index())"
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"print(f\"\u2705 {len(df_clean)} communes fusionn\u00e9es\")\n",
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"print(f\"\\nMicro-r\u00e9gions: {len(df_clean['Territoire de projet'].unique())}\")\n",
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"for region in sorted(df_clean['Territoire de projet'].unique()):\n",
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" count = (df_clean['Territoire de projet'] == region).sum()\n",
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" print(f\" \u2022 {region}: {count} communes\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 4. Entraînement du modèle k-NN"
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"## 2. Entra\u00eenement k-NN"
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"# Préparation des données pour k-NN\n",
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"# Mod\u00e8le k-NN\n",
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"X = df_clean[['Latitude', 'Longitude']].values\n",
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"y = df_clean['Territoire de projet'].values\n",
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"\n",
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"# Création du modèle k-NN avec k=5 (ajustable)\n",
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"k = 5\n",
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"knn = KNeighborsClassifier(n_neighbors=k, weights='distance', metric='haversine')\n",
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"\n",
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"# Conversion des coordonnées en radians pour la distance haversine\n",
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"knn = KNeighborsClassifier(n_neighbors=5, weights='distance', metric='haversine')\n",
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"X_rad = np.radians(X)\n",
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"\n",
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"# Entraînement du modèle\n",
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"knn.fit(X_rad, y)\n",
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"\n",
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"print(f\"✅ Modèle k-NN entraîné avec k={k} voisins\")\n",
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"print(f\"📊 Nombre de micro-régions: {len(np.unique(y))}\")\n",
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"print(f\"\\n🗺️ Micro-régions identifiées:\")\n",
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"for i, region in enumerate(sorted(np.unique(y)), 1):\n",
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" count = (y == region).sum()\n",
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" print(f\" {i:2d}. {region} ({count} communes)\")"
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"print(f\"\u2705 Mod\u00e8le k-NN entra\u00een\u00e9\")\n",
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"print(f\"\u2705 {len(df_clean)} communes\")\n",
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"print(f\"\u2705 {len(np.unique(y))} micro-r\u00e9gions\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 5. Création de la carte interactive avec Folium"
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"## 3. Configuration des couleurs"
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]
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},
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{
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@ -228,17 +146,24 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# Couleurs pour chaque micro-région\n",
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"# Couleurs par micro-r\u00e9gion\n",
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"microregions = sorted(df_clean['Territoire de projet'].unique())\n",
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"colors = ['red', 'blue', 'green', 'purple', 'orange', 'darkred', \n",
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" 'lightred', 'beige', 'darkblue', 'darkgreen', 'cadetblue', \n",
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" 'darkpurple', 'pink', 'lightblue', 'lightgreen', 'gray', 'black', 'lightgray']\n",
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" 'lightcoral', 'beige', 'darkblue', 'darkgreen', 'cadetblue', \n",
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" 'darkviolet', 'pink', 'lightblue', 'lightgreen', 'gray']\n",
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"\n",
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"color_map = {region: colors[i % len(colors)] for i, region in enumerate(microregions)}\n",
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"\n",
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"print(\"🎨 Carte des couleurs par micro-région:\")\n",
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"print(\"\u2705 Couleurs configur\u00e9es:\")\n",
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"for region, color in sorted(color_map.items()):\n",
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" print(f\" • {region}: {color}\")"
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" print(f\" \u2022 {region}: {color}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 4. Cr\u00e9ation de la carte interactive"
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"# Coordonnées du centre de la Corse\n",
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"# Carte ipyleaflet\n",
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"center_lat = df_clean['Latitude'].mean()\n",
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"center_lon = df_clean['Longitude'].mean()\n",
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"\n",
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"print(f\"Centre de la carte: {center_lat:.4f}°N, {center_lon:.4f}°E\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 6. Carte interactive avec prédiction au clic"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Création de la carte interactive\n",
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"m_interactive = folium.Map(\n",
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" location=[center_lat, center_lon],\n",
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" zoom_start=9,\n",
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" tiles='OpenStreetMap'\n",
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"m = Map(\n",
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" center=(center_lat, center_lon),\n",
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" zoom=9,\n",
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" scroll_wheel_zoom=True\n",
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")\n",
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"\n",
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"# Ajout des marqueurs pour chaque commune\n",
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"# Ajouter les communes\n",
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"commune_layer = LayerGroup(name='Communes')\n",
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"\n",
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"for idx, row in df_clean.iterrows():\n",
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" folium.CircleMarker(\n",
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" location=[row['Latitude'], row['Longitude']],\n",
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" marker = CircleMarker(\n",
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" location=(row['Latitude'], row['Longitude']),\n",
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" radius=3,\n",
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" popup=f\"<b>{row['Commune']}</b><br>{row['Territoire de projet']}<br><small>({row['Latitude']:.4f}, {row['Longitude']:.4f})</small>\",\n",
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" tooltip=row['Commune'],\n",
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" color=color_map[row['Territoire de projet']],\n",
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" fill=True,\n",
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" fillColor=color_map[row['Territoire de projet']],\n",
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" fillOpacity=0.7\n",
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" ).add_to(m_interactive)\n",
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" fill_color=color_map[row['Territoire de projet']],\n",
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" fill_opacity=0.7,\n",
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" weight=1\n",
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" )\n",
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" # Popup avec info\n",
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" marker.popup = HTML(f\"<b>{row['Commune']}</b><br>{row['Territoire de projet']}\")\n",
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" commune_layer.add_layer(marker)\n",
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"\n",
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"# Préparer les données des communes pour JavaScript\n",
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"communes_data = df_clean[['Latitude', 'Longitude', 'Commune', 'Territoire de projet']].to_dict('records')\n",
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"m.add_layer(commune_layer)\n",
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"\n",
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"# JavaScript pour la prédiction k-NN au clic\n",
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"click_js = f\"\"\"\n",
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"<script>\n",
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"// Données des communes\n",
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"var communesData = {json.dumps(communes_data)};\n",
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"\n",
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"// Carte des couleurs\n",
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"var colorMap = {json.dumps(color_map)};\n",
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"\n",
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"// Fonction pour calculer la distance haversine\n",
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"function haversineDistance(lat1, lon1, lat2, lon2) {{\n",
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" const R = 6371; // Rayon de la Terre en km\n",
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" const dLat = (lat2 - lat1) * Math.PI / 180;\n",
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" const dLon = (lon2 - lon1) * Math.PI / 180;\n",
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" const a = Math.sin(dLat/2) * Math.sin(dLat/2) +\n",
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" Math.cos(lat1 * Math.PI / 180) * Math.cos(lat2 * Math.PI / 180) *\n",
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" Math.sin(dLon/2) * Math.sin(dLon/2);\n",
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" const c = 2 * Math.atan2(Math.sqrt(a), Math.sqrt(1-a));\n",
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" return R * c;\n",
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"}}\n",
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"\n",
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"// Fonction k-NN\n",
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"function predictRegion(lat, lon, k) {{\n",
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" // Calculer les distances\n",
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" var distances = communesData.map(function(commune) {{\n",
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" return {{\n",
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" distance: haversineDistance(lat, lon, commune.Latitude, commune.Longitude),\n",
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" region: commune['Territoire de projet'],\n",
|
||||
" commune: commune.Commune\n",
|
||||
" }};\n",
|
||||
" }});\n",
|
||||
" \n",
|
||||
" // Trier par distance\n",
|
||||
" distances.sort((a, b) => a.distance - b.distance);\n",
|
||||
" \n",
|
||||
" // Prendre les k plus proches\n",
|
||||
" var kNearest = distances.slice(0, k);\n",
|
||||
" \n",
|
||||
" // Vote pondéré par l'inverse de la distance\n",
|
||||
" var votes = {{}};\n",
|
||||
" kNearest.forEach(function(neighbor) {{\n",
|
||||
" var weight = 1 / (neighbor.distance + 0.001); // +0.001 pour éviter division par 0\n",
|
||||
" if (votes[neighbor.region]) {{\n",
|
||||
" votes[neighbor.region] += weight;\n",
|
||||
" }} else {{\n",
|
||||
" votes[neighbor.region] = weight;\n",
|
||||
" }}\n",
|
||||
" }});\n",
|
||||
" \n",
|
||||
" // Trouver la région gagnante\n",
|
||||
" var maxVote = 0;\n",
|
||||
" var predictedRegion = '';\n",
|
||||
" for (var region in votes) {{\n",
|
||||
" if (votes[region] > maxVote) {{\n",
|
||||
" maxVote = votes[region];\n",
|
||||
" predictedRegion = region;\n",
|
||||
" }}\n",
|
||||
" }}\n",
|
||||
" \n",
|
||||
" return {{\n",
|
||||
" region: predictedRegion,\n",
|
||||
" neighbors: kNearest\n",
|
||||
" }};\n",
|
||||
"}}\n",
|
||||
"\n",
|
||||
"// Variable pour stocker le marqueur de prédiction\n",
|
||||
"var predictionMarker = null;\n",
|
||||
"var neighborLines = [];\n",
|
||||
"\n",
|
||||
"// Attendre que la carte soit chargée\n",
|
||||
"setTimeout(function() {{\n",
|
||||
" var maps = document.querySelectorAll('.folium-map');\n",
|
||||
" if (maps.length > 0) {{\n",
|
||||
" var mapElement = maps[maps.length - 1];\n",
|
||||
" var leafletMap = mapElement._leaflet_map;\n",
|
||||
" \n",
|
||||
" if (leafletMap) {{\n",
|
||||
" leafletMap.on('click', function(e) {{\n",
|
||||
" var lat = e.latlng.lat;\n",
|
||||
" var lon = e.latlng.lng;\n",
|
||||
" \n",
|
||||
" // Prédiction avec k={k}\n",
|
||||
" var result = predictRegion(lat, lon, {k});\n",
|
||||
" \n",
|
||||
" // Supprimer l'ancien marqueur et lignes\n",
|
||||
" if (predictionMarker) {{\n",
|
||||
" leafletMap.removeLayer(predictionMarker);\n",
|
||||
" }}\n",
|
||||
" neighborLines.forEach(function(line) {{\n",
|
||||
" leafletMap.removeLayer(line);\n",
|
||||
" }});\n",
|
||||
" neighborLines = [];\n",
|
||||
" \n",
|
||||
" // Créer le popup avec informations détaillées\n",
|
||||
" var popupContent = '<div style=\"min-width: 200px;\">' +\n",
|
||||
" '<h4 style=\"margin: 5px 0; color: ' + colorMap[result.region] + ';\">🎯 ' + result.region + '</h4>' +\n",
|
||||
" '<p style=\"margin: 5px 0; font-size: 11px;\"><b>Coordonnées cliquées:</b><br>' + \n",
|
||||
" 'Lat: ' + lat.toFixed(5) + '°<br>Lon: ' + lon.toFixed(5) + '°</p>' +\n",
|
||||
" '<hr style=\"margin: 5px 0;\">' +\n",
|
||||
" '<p style=\"margin: 5px 0; font-size: 11px;\"><b>{k} plus proches communes:</b></p>' +\n",
|
||||
" '<ul style=\"margin: 5px 0; padding-left: 20px; font-size: 10px;\">';\n",
|
||||
" \n",
|
||||
" result.neighbors.forEach(function(neighbor, i) {{\n",
|
||||
" popupContent += '<li><b>' + neighbor.commune + '</b> (' + neighbor.distance.toFixed(2) + ' km)</li>';\n",
|
||||
" }});\n",
|
||||
" \n",
|
||||
" popupContent += '</ul></div>';\n",
|
||||
" \n",
|
||||
" // Ajouter le nouveau marqueur\n",
|
||||
" predictionMarker = L.marker([lat, lon], {{\n",
|
||||
" icon: L.divIcon({{\n",
|
||||
" className: 'prediction-marker',\n",
|
||||
" html: '<div style=\"background-color: ' + colorMap[result.region] + \n",
|
||||
" '; width: 20px; height: 20px; border-radius: 50%; ' +\n",
|
||||
" 'border: 3px solid white; box-shadow: 0 0 10px rgba(0,0,0,0.5);\"></div>',\n",
|
||||
" iconSize: [20, 20]\n",
|
||||
" }})\n",
|
||||
" }}).addTo(leafletMap);\n",
|
||||
" \n",
|
||||
" predictionMarker.bindPopup(popupContent, {{maxWidth: 300}}).openPopup();\n",
|
||||
" \n",
|
||||
" // Ajouter des lignes vers les k plus proches voisins\n",
|
||||
" result.neighbors.forEach(function(neighbor) {{\n",
|
||||
" var commune = communesData.find(c => c.Commune === neighbor.commune);\n",
|
||||
" if (commune) {{\n",
|
||||
" var line = L.polyline(\n",
|
||||
" [[lat, lon], [commune.Latitude, commune.Longitude]],\n",
|
||||
" {{\n",
|
||||
" color: 'gray',\n",
|
||||
" weight: 1,\n",
|
||||
" opacity: 0.5,\n",
|
||||
" dashArray: '5, 5'\n",
|
||||
" }}\n",
|
||||
" ).addTo(leafletMap);\n",
|
||||
" neighborLines.push(line);\n",
|
||||
" }}\n",
|
||||
" }});\n",
|
||||
" }});\n",
|
||||
" \n",
|
||||
" console.log('✅ Gestionnaire de clic k-NN activé');\n",
|
||||
" console.log('📊 ' + communesData.length + ' communes chargées');\n",
|
||||
" }}\n",
|
||||
" }}\n",
|
||||
"}}, 1000);\n",
|
||||
"</script>\n",
|
||||
"\"\"\"\n",
|
||||
"\n",
|
||||
"m_interactive.get_root().html.add_child(folium.Element(click_js))\n",
|
||||
"\n",
|
||||
"# Ajout de la légende\n",
|
||||
"legend_html = '''\n",
|
||||
"<div style=\"position: fixed; \n",
|
||||
" top: 10px; right: 10px; width: 250px; max-height: 85vh; overflow-y: auto;\n",
|
||||
" background-color: white; border:2px solid grey; z-index:9999; border-radius: 5px;\n",
|
||||
" font-size:12px; padding: 10px; box-shadow: 0 0 15px rgba(0,0,0,0.2);\">\n",
|
||||
"<p style=\"margin-bottom: 8px; font-weight: bold; font-size: 14px;\">🗺️ Micro-régions de Corse</p>\n",
|
||||
"'''\n",
|
||||
"\n",
|
||||
"for region, color in sorted(color_map.items()):\n",
|
||||
" legend_html += f'<p style=\"margin: 3px 0;\"><i class=\"fa fa-circle\" style=\"color:{color}\"></i> {region}</p>'\n",
|
||||
"\n",
|
||||
"legend_html += f'<hr style=\"margin: 8px 0;\"><p style=\"margin: 3px 0; font-size: 11px; color: #666;\">k = {k} voisins<br>Distance: Haversine</p></div>'\n",
|
||||
"\n",
|
||||
"m_interactive.get_root().html.add_child(folium.Element(legend_html))\n",
|
||||
"\n",
|
||||
"# Ajout d'instructions\n",
|
||||
"instructions_html = '''\n",
|
||||
"<div style=\"position: fixed; \n",
|
||||
" bottom: 10px; left: 10px; width: 320px; \n",
|
||||
" background-color: white; border:2px solid grey; z-index:9999; border-radius: 5px;\n",
|
||||
" font-size:13px; padding: 12px; box-shadow: 0 0 15px rgba(0,0,0,0.2);\">\n",
|
||||
"<p style=\"margin: 0 0 8px 0; font-weight: bold;\">🖱️ Mode d'emploi</p>\n",
|
||||
"<p style=\"margin: 5px 0; line-height: 1.4;\"><b>Cliquez</b> n'importe où sur la carte pour prédire la micro-région.</p>\n",
|
||||
"<p style=\"margin: 5px 0; line-height: 1.4; font-size: 11px;\">• Un marqueur coloré apparaît au point cliqué<br>\n",
|
||||
"• Les lignes pointillées montrent les k communes les plus proches<br>\n",
|
||||
"• Le popup affiche la prédiction détaillée</p>\n",
|
||||
"</div>\n",
|
||||
"'''\n",
|
||||
"\n",
|
||||
"m_interactive.get_root().html.add_child(folium.Element(instructions_html))\n",
|
||||
"\n",
|
||||
"print(\"\\n✅ Carte interactive créée avec succès!\")\n",
|
||||
"print(f\"\\n🖱️ Cliquez sur n'importe quel point de la carte pour prédire sa micro-région avec k={k} voisins.\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Affichage de la carte interactive\n",
|
||||
"m_interactive"
|
||||
"print(f\"\u2705 {len(df_clean)} communes ajout\u00e9es \u00e0 la carte\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 7. Sauvegarde de la carte"
|
||||
"## 5. Interface interactive avec widgets"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -498,17 +216,33 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Sauvegarder la carte interactive\n",
|
||||
"m_interactive.save('carte_corse_knn_interactive.html')\n",
|
||||
"print(\"✅ Carte sauvegardée dans 'carte_corse_knn_interactive.html'\")\n",
|
||||
"print(\"📁 Vous pouvez ouvrir ce fichier dans un navigateur pour une utilisation autonome.\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 8. Test de la prédiction (optionnel)"
|
||||
"# Widgets\n",
|
||||
"k_slider = IntSlider(\n",
|
||||
" value=5,\n",
|
||||
" min=1,\n",
|
||||
" max=15,\n",
|
||||
" step=1,\n",
|
||||
" description='k voisins:',\n",
|
||||
" continuous_update=False\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"info_html = HTML(\n",
|
||||
" value=\"<div style='background:#e3f2fd;padding:10px;border-radius:5px;'>\"\n",
|
||||
" \"<b>\ud83d\uddb1\ufe0f Cliquez sur la carte</b> pour pr\u00e9dire la micro-r\u00e9gion.<br>\"\n",
|
||||
" \"Ajustez <b>k</b> pour changer le nombre de voisins.\"\n",
|
||||
" \"</div>\"\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"result_output = Output()\n",
|
||||
"\n",
|
||||
"# Stocker les coordonn\u00e9es courantes\n",
|
||||
"current_coords = {'lat': None, 'lon': None}\n",
|
||||
"\n",
|
||||
"# Layer pour la pr\u00e9diction\n",
|
||||
"prediction_layer = LayerGroup(name='Pr\u00e9diction')\n",
|
||||
"m.add_layer(prediction_layer)\n",
|
||||
"\n",
|
||||
"print(\"\u2705 Widgets cr\u00e9\u00e9s\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -517,60 +251,83 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Fonction pour tester la prédiction sur des coordonnées spécifiques\n",
|
||||
"def predict_region(lat, lon, k_value=5):\n",
|
||||
" \"\"\"\n",
|
||||
" Prédit la micro-région pour des coordonnées données\n",
|
||||
" \"\"\"\n",
|
||||
" # Conversion en radians\n",
|
||||
"# Fonction de pr\u00e9diction\n",
|
||||
"def update_classification(lat, lon, k_value):\n",
|
||||
" \"\"\"Met \u00e0 jour la classification pour un point donn\u00e9.\"\"\"\n",
|
||||
" # Pr\u00e9diction\n",
|
||||
" coords_rad = np.radians([[lat, lon]])\n",
|
||||
" \n",
|
||||
" # Prédiction\n",
|
||||
" prediction = knn.predict(coords_rad)[0]\n",
|
||||
" knn.n_neighbors = k_value\n",
|
||||
" knn.fit(X_rad, y) # R\u00e9entra\u00eener avec nouveau k\n",
|
||||
" predicted_region = knn.predict(coords_rad)[0]\n",
|
||||
" \n",
|
||||
" # Trouver les k plus proches voisins\n",
|
||||
" distances, indices = knn.kneighbors(coords_rad)\n",
|
||||
" distances_km = distances[0] * 6371 # Conversion en km\n",
|
||||
" \n",
|
||||
" # Convertir les distances de radians en km\n",
|
||||
" distances_km = distances[0] * 6371 # Rayon de la Terre en km\n",
|
||||
" # Nettoyer la couche de pr\u00e9diction\n",
|
||||
" prediction_layer.clear_layers()\n",
|
||||
" \n",
|
||||
" print(f\"\\n📍 Coordonnées: {lat:.5f}°N, {lon:.5f}°E\")\n",
|
||||
" print(f\"🎯 Micro-région prédite: {prediction}\")\n",
|
||||
" print(f\"\\n{k_value} plus proches communes:\")\n",
|
||||
" # Cr\u00e9er une ic\u00f4ne personnalis\u00e9e avec AwesomeIcon\n",
|
||||
" custom_icon = AwesomeIcon(\n",
|
||||
" name='star',\n",
|
||||
" marker_color=color_map[predicted_region],\n",
|
||||
" icon_color='white',\n",
|
||||
" spin=False\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" # Ajouter le marqueur de pr\u00e9diction\n",
|
||||
" prediction_marker = Marker(\n",
|
||||
" location=(lat, lon),\n",
|
||||
" draggable=False,\n",
|
||||
" icon=custom_icon\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" # Popup d\u00e9taill\u00e9\n",
|
||||
" popup_html = f\"\"\"<div style='min-width:220px;'>\n",
|
||||
" <h4 style='margin:5px 0;color:{color_map[predicted_region]};'>\ud83c\udfaf {predicted_region}</h4>\n",
|
||||
" <p style='margin:5px 0;font-size:11px;'>\n",
|
||||
" <b>Coordonn\u00e9es:</b><br>\n",
|
||||
" Lat: {lat:.5f}\u00b0<br>\n",
|
||||
" Lon: {lon:.5f}\u00b0\n",
|
||||
" </p>\n",
|
||||
" <hr style='margin:5px 0;'>\n",
|
||||
" <p style='margin:5px 0;font-size:11px;'><b>{k_value} plus proches communes:</b></p>\n",
|
||||
" <ul style='margin:5px 0;padding-left:20px;font-size:10px;'>\"\"\"\n",
|
||||
" \n",
|
||||
" for i, idx in enumerate(indices[0]):\n",
|
||||
" commune_info = df_clean.iloc[idx]\n",
|
||||
" print(f\" {i+1}. {commune_info['Commune']:30s} ({commune_info['Territoire de projet']:30s}) - {distances_km[i]:6.2f} km\")\n",
|
||||
" popup_html += f\"<li><b>{commune_info['Commune']}</b> ({distances_km[i]:.2f} km)</li>\"\n",
|
||||
" \n",
|
||||
" return prediction\n",
|
||||
" popup_html += \"</ul></div>\"\n",
|
||||
" prediction_marker.popup = HTML(popup_html)\n",
|
||||
" prediction_layer.add_layer(prediction_marker)\n",
|
||||
" \n",
|
||||
" # Ajouter les lignes vers les k plus proches voisins\n",
|
||||
" for i, idx in enumerate(indices[0]):\n",
|
||||
" commune_info = df_clean.iloc[idx]\n",
|
||||
" line = Polyline(\n",
|
||||
" locations=[\n",
|
||||
" (lat, lon),\n",
|
||||
" (commune_info['Latitude'], commune_info['Longitude'])\n",
|
||||
" ],\n",
|
||||
" color='gray',\n",
|
||||
" weight=2,\n",
|
||||
" opacity=0.6,\n",
|
||||
" dash_array='8, 8'\n",
|
||||
" )\n",
|
||||
" prediction_layer.add_layer(line)\n",
|
||||
" \n",
|
||||
" # Afficher le r\u00e9sultat\n",
|
||||
" with result_output:\n",
|
||||
" result_output.clear_output()\n",
|
||||
" print(f\"\\n\ud83c\udfaf Pr\u00e9diction: {predicted_region}\")\n",
|
||||
" print(f\"\ud83d\udccd Coordonn\u00e9es: ({lat:.5f}, {lon:.5f})\")\n",
|
||||
" print(f\"\\n{k_value} plus proches communes:\")\n",
|
||||
" for i, idx in enumerate(indices[0]):\n",
|
||||
" commune_info = df_clean.iloc[idx]\n",
|
||||
" print(f\" {i+1}. {commune_info['Commune']:30s} - {distances_km[i]:6.2f} km\")\n",
|
||||
"\n",
|
||||
"# Exemples de test\n",
|
||||
"print(\"=\" * 100)\n",
|
||||
"print(\"TESTS DE PRÉDICTION k-NN\")\n",
|
||||
"print(\"=\" * 100)\n",
|
||||
"\n",
|
||||
"# Test 1: Centre approximatif de la Corse (vers Corte)\n",
|
||||
"print(\"\\n🔍 Test 1: Centre de la Corse\")\n",
|
||||
"predict_region(42.15, 9.15, k)\n",
|
||||
"\n",
|
||||
"# Test 2: Nord de la Corse (Balagne/Bastia)\n",
|
||||
"print(\"\\n🔍 Test 2: Nord de la Corse\")\n",
|
||||
"predict_region(42.55, 8.85, k)\n",
|
||||
"\n",
|
||||
"# Test 3: Sud de la Corse (vers Porto-Vecchio)\n",
|
||||
"print(\"\\n🔍 Test 3: Sud de la Corse\")\n",
|
||||
"predict_region(41.65, 9.15, k)\n",
|
||||
"\n",
|
||||
"# Test 4: Ouest (vers Ajaccio)\n",
|
||||
"print(\"\\n🔍 Test 4: Ouest de la Corse (Ajaccio)\")\n",
|
||||
"predict_region(41.93, 8.74, k)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 9. Analyse de performance (optionnel)"
|
||||
"print(\"\u2705 Fonction de pr\u00e9diction d\u00e9finie\")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -579,37 +336,33 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Évaluation de la cohérence du modèle (cross-validation)\n",
|
||||
"from sklearn.model_selection import cross_val_score\n",
|
||||
"# Gestionnaires d'\u00e9v\u00e9nements\n",
|
||||
"def handle_click(**kwargs):\n",
|
||||
" \"\"\"Gestionnaire de clic sur la carte.\"\"\"\n",
|
||||
" if kwargs.get('type') == 'click':\n",
|
||||
" coords = kwargs.get('coordinates')\n",
|
||||
" lat, lon = coords\n",
|
||||
" current_coords['lat'] = lat\n",
|
||||
" current_coords['lon'] = lon\n",
|
||||
" update_classification(lat, lon, k_slider.value)\n",
|
||||
"\n",
|
||||
"# Test avec différentes valeurs de k\n",
|
||||
"k_values = [3, 5, 7, 9, 11, 15]\n",
|
||||
"scores = []\n",
|
||||
"def on_k_change(change):\n",
|
||||
" \"\"\"Gestionnaire de changement de k.\"\"\"\n",
|
||||
" if current_coords['lat'] is not None:\n",
|
||||
" update_classification(current_coords['lat'], current_coords['lon'], change['new'])\n",
|
||||
"\n",
|
||||
"print(\"📊 Évaluation de la précision pour différentes valeurs de k:\\n\")\n",
|
||||
"print(f\"{'k':<5} {'Précision moyenne':<20} {'Écart-type':<15}\")\n",
|
||||
"print(\"-\" * 50)\n",
|
||||
"# Connecter les \u00e9v\u00e9nements\n",
|
||||
"m.on_interaction(handle_click)\n",
|
||||
"k_slider.observe(on_k_change, names='value')\n",
|
||||
"\n",
|
||||
"for k_val in k_values:\n",
|
||||
" knn_temp = KNeighborsClassifier(n_neighbors=k_val, weights='distance', metric='haversine')\n",
|
||||
" cv_scores = cross_val_score(knn_temp, X_rad, y, cv=5)\n",
|
||||
" mean_score = cv_scores.mean()\n",
|
||||
" std_score = cv_scores.std()\n",
|
||||
" scores.append(mean_score)\n",
|
||||
" print(f\"{k_val:<5} {mean_score:.4f} ({mean_score*100:5.2f}%) ± {std_score:.4f}\")\n",
|
||||
"\n",
|
||||
"best_k = k_values[scores.index(max(scores))]\n",
|
||||
"best_score = max(scores)\n",
|
||||
"print(\"\\n\" + \"=\" * 50)\n",
|
||||
"print(f\"✨ Meilleure valeur de k: {best_k} (précision: {best_score:.4f} / {best_score*100:.2f}%)\")\n",
|
||||
"print(\"=\" * 50)"
|
||||
"print(\"\u2705 Gestionnaires d'\u00e9v\u00e9nements connect\u00e9s\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 10. Statistiques par micro-région"
|
||||
"## 6. Affichage de l'interface compl\u00e8te"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -618,20 +371,58 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Statistiques descriptives par micro-région\n",
|
||||
"print(\"📈 STATISTIQUES PAR MICRO-RÉGION\\n\")\n",
|
||||
"print(f\"{'Micro-région':<35} {'Nb communes':<15} {'% du total'}\")\n",
|
||||
"print(\"=\" * 65)\n",
|
||||
"# L\u00e9gende HTML\n",
|
||||
"legend_html = \"<div style='background:white;padding:10px;border-radius:5px;max-height:300px;overflow-y:auto;'>\"\n",
|
||||
"legend_html += \"<h4 style='margin-top:0;'>\ud83d\uddfa\ufe0f Micro-r\u00e9gions</h4>\"\n",
|
||||
"for region, color in sorted(color_map.items()):\n",
|
||||
" legend_html += f\"<div style='margin:3px 0;'><span style='display:inline-block;width:12px;height:12px;background:{color};border-radius:50%;margin-right:5px;'></span>{region}</div>\"\n",
|
||||
"legend_html += \"</div>\"\n",
|
||||
"\n",
|
||||
"total_communes = len(df_clean)\n",
|
||||
"stats = df_clean['Territoire de projet'].value_counts().sort_index()\n",
|
||||
"legend_widget = HTML(legend_html)\n",
|
||||
"legend_control = WidgetControl(widget=legend_widget, position='topright')\n",
|
||||
"m.add_control(legend_control)\n",
|
||||
"\n",
|
||||
"for region, count in stats.items():\n",
|
||||
" pct = (count / total_communes) * 100\n",
|
||||
" print(f\"{region:<35} {count:<15} {pct:>5.1f}%\")\n",
|
||||
"# Afficher l'interface compl\u00e8te\n",
|
||||
"display(VBox([\n",
|
||||
" info_html,\n",
|
||||
" HBox([Label(''), k_slider]),\n",
|
||||
" m,\n",
|
||||
" result_output\n",
|
||||
"]))\n",
|
||||
"\n",
|
||||
"print(\"=\" * 65)\n",
|
||||
"print(f\"{'TOTAL':<35} {total_communes:<15} 100.0%\")"
|
||||
"print(\"\\n\" + \"=\"*60)\n",
|
||||
"print(\"\u2705 CARTE INTERACTIVE PR\u00caTE!\")\n",
|
||||
"print(\"=\"*60)\n",
|
||||
"print(\"\\n\ud83d\uddb1\ufe0f Cliquez n'importe o\u00f9 sur la carte pour pr\u00e9dire la micro-r\u00e9gion.\")\n",
|
||||
"print(\"\ud83c\udf9a\ufe0f Utilisez le slider pour changer k (nombre de voisins).\")\n",
|
||||
"print(\"\\n\u2b50 Cette version utilise ipyleaflet avec interactions Python natives.\")\n",
|
||||
"print(\" Pas de JavaScript inject\u00e9 = Fonctionne parfaitement dans Jupyter!\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 7. Fonction de test (optionnel)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Test manuel\n",
|
||||
"def test_prediction(lat, lon, k_val=5):\n",
|
||||
" \"\"\"Tester une pr\u00e9diction avec des coordonn\u00e9es sp\u00e9cifiques.\"\"\"\n",
|
||||
" print(f\"\\nTest de pr\u00e9diction pour ({lat}, {lon}) avec k={k_val}\")\n",
|
||||
" print(\"=\"*60)\n",
|
||||
" update_classification(lat, lon, k_val)\n",
|
||||
"\n",
|
||||
"# Exemples\n",
|
||||
"# test_prediction(42.15, 9.15, 5) # Centre Corse\n",
|
||||
"# test_prediction(42.55, 8.85, 5) # Nord\n",
|
||||
"# test_prediction(41.65, 9.15, 5) # Sud"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -640,27 +431,21 @@
|
|||
"source": [
|
||||
"## Conclusion\n",
|
||||
"\n",
|
||||
"✅ **Notebook k-NN Corse - Résumé**\n",
|
||||
"\u2705 **Carte interactive fonctionnelle avec ipyleaflet**\n",
|
||||
"\n",
|
||||
"Ce notebook implémente un classificateur k-NN pour les micro-régions de Corse avec:\n",
|
||||
"1. ✅ Chargement des données depuis 2 fichiers CSV (coordonnées + territoires)\n",
|
||||
"2. ✅ Extraction automatique des coordonnées GPS depuis la colonne Point_Geo\n",
|
||||
"3. ✅ Fusion intelligente des deux sources de données\n",
|
||||
"4. ✅ Entraînement d'un modèle k-NN avec distance haversine\n",
|
||||
"5. ✅ Carte interactive Folium avec prédiction au clic\n",
|
||||
"6. ✅ Visualisation des k plus proches voisins\n",
|
||||
"7. ✅ Tests de performance et validation\n",
|
||||
"8. ✅ Export HTML pour utilisation autonome\n",
|
||||
"**Avantages par rapport \u00e0 Folium :**\n",
|
||||
"- \u2705 Interactions Python natives (pas de JavaScript inject\u00e9)\n",
|
||||
"- \u2705 Fonctionne parfaitement dans Jupyter\n",
|
||||
"- \u2705 Pas de probl\u00e8me d'iframe ou de s\u00e9curit\u00e9\n",
|
||||
"- \u2705 Slider interactif pour changer k en temps r\u00e9el\n",
|
||||
"- \u2705 R\u00e9sultats affich\u00e9s sous la carte\n",
|
||||
"\n",
|
||||
"**🖱️ Utilisation:**\n",
|
||||
"- Cliquez n'importe où sur la carte\n",
|
||||
"- Un marqueur coloré apparaît avec la micro-région prédite\n",
|
||||
"- Des lignes pointillées montrent les k communes les plus proches\n",
|
||||
"- Un popup détaille la prédiction et les voisins\n",
|
||||
"**Utilisation :**\n",
|
||||
"1. Cliquez sur la carte \u2192 Pr\u00e9diction s'affiche avec marqueur et lignes\n",
|
||||
"2. Changez k avec le slider \u2192 Pr\u00e9diction se met \u00e0 jour automatiquement\n",
|
||||
"3. Les r\u00e9sultats d\u00e9taill\u00e9s s'affichent sous la carte\n",
|
||||
"\n",
|
||||
"**📁 Fichier exporté:** `carte_corse_knn_interactive.html`\n",
|
||||
"\n",
|
||||
"La carte HTML peut être ouverte dans n'importe quel navigateur pour une utilisation autonome!"
|
||||
"**Note :** Cette version ne g\u00e9n\u00e8re pas de fichier HTML standalone car ipyleaflet n\u00e9cessite un serveur Jupyter pour les interactions Python. Pour partager, utilisez Jupyter nbviewer ou Binder."
|
||||
]
|
||||
}
|
||||
],
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue