688 lines
25 KiB
Text
688 lines
25 KiB
Text
{
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"cells": [
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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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"# Classification des Micro-régions de Corse par K-NN\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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"\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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]
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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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"# Installation des bibliothèques nécessaires\n",
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"!pip install folium pandas numpy scikit-learn --quiet"
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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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"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 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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]
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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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]
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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 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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" 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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]
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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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"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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" 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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]
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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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"# 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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"\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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"\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_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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]
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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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]
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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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"# Préparation des données pour 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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"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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]
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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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]
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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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"# Couleurs pour chaque micro-région\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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"\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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"for region, color in sorted(color_map.items()):\n",
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" print(f\" • {region}: {color}\")"
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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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"# Coordonnées du centre de la Corse\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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")\n",
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"\n",
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"# Ajout des marqueurs pour chaque commune\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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" 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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"\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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"\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",
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" commune: commune.Commune\n",
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" }};\n",
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" }});\n",
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" \n",
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" // Trier par distance\n",
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" distances.sort((a, b) => a.distance - b.distance);\n",
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" \n",
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" // Prendre les k plus proches\n",
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" var kNearest = distances.slice(0, k);\n",
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" \n",
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" // Vote pondéré par l'inverse de la distance\n",
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" var votes = {{}};\n",
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" kNearest.forEach(function(neighbor) {{\n",
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" var weight = 1 / (neighbor.distance + 0.001); // +0.001 pour éviter division par 0\n",
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" if (votes[neighbor.region]) {{\n",
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" votes[neighbor.region] += weight;\n",
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" }} else {{\n",
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" votes[neighbor.region] = weight;\n",
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" }}\n",
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" }});\n",
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" \n",
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" // Trouver la région gagnante\n",
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" var maxVote = 0;\n",
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" var predictedRegion = '';\n",
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" for (var region in votes) {{\n",
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" if (votes[region] > maxVote) {{\n",
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" maxVote = votes[region];\n",
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" predictedRegion = region;\n",
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" }}\n",
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" }}\n",
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" \n",
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" return {{\n",
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" region: predictedRegion,\n",
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" neighbors: kNearest\n",
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" }};\n",
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"}}\n",
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"\n",
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"// Variable pour stocker le marqueur de prédiction\n",
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"var predictionMarker = null;\n",
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"var neighborLines = [];\n",
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"\n",
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"// Attendre que la carte soit chargée\n",
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"setTimeout(function() {{\n",
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" var maps = document.querySelectorAll('.folium-map');\n",
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" if (maps.length > 0) {{\n",
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" var mapElement = maps[maps.length - 1];\n",
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" var leafletMap = mapElement._leaflet_map;\n",
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" \n",
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" if (leafletMap) {{\n",
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" leafletMap.on('click', function(e) {{\n",
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" var lat = e.latlng.lat;\n",
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" var lon = e.latlng.lng;\n",
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" \n",
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" // Prédiction avec k={k}\n",
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" var result = predictRegion(lat, lon, {k});\n",
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" \n",
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" // Supprimer l'ancien marqueur et lignes\n",
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" if (predictionMarker) {{\n",
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" leafletMap.removeLayer(predictionMarker);\n",
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" }}\n",
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" neighborLines.forEach(function(line) {{\n",
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" leafletMap.removeLayer(line);\n",
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" }});\n",
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" neighborLines = [];\n",
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" \n",
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" // Créer le popup avec informations détaillées\n",
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" var popupContent = '<div style=\"min-width: 200px;\">' +\n",
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" '<h4 style=\"margin: 5px 0; color: ' + colorMap[result.region] + ';\">🎯 ' + result.region + '</h4>' +\n",
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" '<p style=\"margin: 5px 0; font-size: 11px;\"><b>Coordonnées cliquées:</b><br>' + \n",
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" 'Lat: ' + lat.toFixed(5) + '°<br>Lon: ' + lon.toFixed(5) + '°</p>' +\n",
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" '<hr style=\"margin: 5px 0;\">' +\n",
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" '<p style=\"margin: 5px 0; font-size: 11px;\"><b>{k} plus proches communes:</b></p>' +\n",
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" '<ul style=\"margin: 5px 0; padding-left: 20px; font-size: 10px;\">';\n",
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" \n",
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" result.neighbors.forEach(function(neighbor, i) {{\n",
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" popupContent += '<li><b>' + neighbor.commune + '</b> (' + neighbor.distance.toFixed(2) + ' km)</li>';\n",
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" }});\n",
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" \n",
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" popupContent += '</ul></div>';\n",
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" \n",
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" // Ajouter le nouveau marqueur\n",
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" predictionMarker = L.marker([lat, lon], {{\n",
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" icon: L.divIcon({{\n",
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" className: 'prediction-marker',\n",
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" html: '<div style=\"background-color: ' + colorMap[result.region] + \n",
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" '; width: 20px; height: 20px; border-radius: 50%; ' +\n",
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" 'border: 3px solid white; box-shadow: 0 0 10px rgba(0,0,0,0.5);\"></div>',\n",
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" iconSize: [20, 20]\n",
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" }})\n",
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" }}).addTo(leafletMap);\n",
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" \n",
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" predictionMarker.bindPopup(popupContent, {{maxWidth: 300}}).openPopup();\n",
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" \n",
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" // Ajouter des lignes vers les k plus proches voisins\n",
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" result.neighbors.forEach(function(neighbor) {{\n",
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" var commune = communesData.find(c => c.Commune === neighbor.commune);\n",
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" if (commune) {{\n",
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" var line = L.polyline(\n",
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" [[lat, lon], [commune.Latitude, commune.Longitude]],\n",
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" {{\n",
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" color: 'gray',\n",
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" weight: 1,\n",
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" opacity: 0.5,\n",
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" dashArray: '5, 5'\n",
|
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" }}\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"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 7. Sauvegarde de la carte"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"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)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"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",
|
||
" coords_rad = np.radians([[lat, lon]])\n",
|
||
" \n",
|
||
" # Prédiction\n",
|
||
" prediction = knn.predict(coords_rad)[0]\n",
|
||
" \n",
|
||
" # Trouver les k plus proches voisins\n",
|
||
" distances, indices = knn.kneighbors(coords_rad)\n",
|
||
" \n",
|
||
" # Convertir les distances de radians en km\n",
|
||
" distances_km = distances[0] * 6371 # Rayon de la Terre en km\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",
|
||
" \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",
|
||
" \n",
|
||
" return prediction\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)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Évaluation de la cohérence du modèle (cross-validation)\n",
|
||
"from sklearn.model_selection import cross_val_score\n",
|
||
"\n",
|
||
"# Test avec différentes valeurs de k\n",
|
||
"k_values = [3, 5, 7, 9, 11, 15]\n",
|
||
"scores = []\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",
|
||
"\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)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 10. Statistiques par micro-région"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"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",
|
||
"\n",
|
||
"total_communes = len(df_clean)\n",
|
||
"stats = df_clean['Territoire de projet'].value_counts().sort_index()\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",
|
||
"\n",
|
||
"print(\"=\" * 65)\n",
|
||
"print(f\"{'TOTAL':<35} {total_communes:<15} 100.0%\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Conclusion\n",
|
||
"\n",
|
||
"✅ **Notebook k-NN Corse - Résumé**\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",
|
||
"\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",
|
||
"\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!"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.8.0"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 4
|
||
}
|