diff --git a/wikipediageocoding.ipynb b/wikipediageocoding.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/jacquesfize/opt/anaconda3/envs/my_env/lib/python3.7/site-packages/tqdm/std.py:699: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n",
+ " from pandas import Panel\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "from tqdm.notebook import tqdm\n",
+ "tqdm.pandas()\n",
+ "\n",
+ "import warnings\n",
+ "from pandas.core.common import SettingWithCopyWarning\n",
+ "warnings.simplefilter(action=\"ignore\", category=SettingWithCopyWarning)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cooccurrence_FN = \"data/cooccurrence_FR.txt\"\n",
+ "k_cooc_used = 4\n",
+ "model_path = \"data/FR20_cooc_4_30_P.h5\"\n",
+ "model_ngram_index_path = \"data/FR20_cooc_4_30_P_index.txt\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv(cooccurrence_FN,sep=\"\\t\")\n",
+ "df = df.head(100) #Â For testing\n",
+ "df[\"interlinks\"] = df.interlinks.apply(lambda x: x.split(\"|\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>title</th>\n",
+ " <th>interlinks</th>\n",
+ " <th>longitude</th>\n",
+ " <th>latitude</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Auvergne</td>\n",
+ " <td>[Le Puy-en-Velay, Clermont-Ferrand, Réserve na...</td>\n",
+ " <td>3.300000</td>\n",
+ " <td>45.700000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>Alpes-de-Haute-Provence</td>\n",
+ " <td>[Annot, Uvernet-Fours, Canton de Manosque-Nord...</td>\n",
+ " <td>6.240000</td>\n",
+ " <td>44.095278</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>Alpes-Maritimes</td>\n",
+ " <td>[Gréolières, Valdeblore, Sophia Antipolis, Vil...</td>\n",
+ " <td>7.166667</td>\n",
+ " <td>43.833333</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>Bas-Rhin</td>\n",
+ " <td>[Bœrsch, Basse-Alsace, Pyrénées-Atlantiques, I...</td>\n",
+ " <td>7.783333</td>\n",
+ " <td>48.816667</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>Bouches-du-Rhône</td>\n",
+ " <td>[Saint-Rémy-de-Provence, Échangeur de Frais-Va...</td>\n",
+ " <td>5.083333</td>\n",
+ " <td>43.500000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " title interlinks \\\n",
+ "0 Auvergne [Le Puy-en-Velay, Clermont-Ferrand, Réserve na... \n",
+ "1 Alpes-de-Haute-Provence [Annot, Uvernet-Fours, Canton de Manosque-Nord... \n",
+ "2 Alpes-Maritimes [Gréolières, Valdeblore, Sophia Antipolis, Vil... \n",
+ "3 Bas-Rhin [Bœrsch, Basse-Alsace, Pyrénées-Atlantiques, I... \n",
+ "4 Bouches-du-Rhône [Saint-Rémy-de-Provence, Échangeur de Frais-Va... \n",
+ "\n",
+ " longitude latitude \n",
+ "0 3.300000 45.700000 \n",
+ "1 6.240000 44.095278 \n",
+ "2 7.166667 43.833333 \n",
+ "3 7.783333 48.816667 \n",
+ "4 5.083333 43.500000 "
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from lib.geocoder.our_geocoder import Geocoder\n",
+ "geo = Geocoder(model_path,model_ngram_index_path)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from lib.utils_geo import haversine_pd\n",
+ "\n",
+ "def heuristic_mean(geocoder,toponym, context_toponyms):\n",
+ " input_ = np.asarray([[toponym,t1] for t1 in context_toponyms if toponym != t1])\n",
+ " res_geocode = pd.DataFrame(input_,columns=\"t tc\".split())\n",
+ " lons,lats = geocoder.get_coords(input_[:,0],input_[:,1])\n",
+ " res_geocode[\"lon\"] = lons\n",
+ " res_geocode[\"lat\"] = lats\n",
+ " return [res_geocode[\"lon\"].mean(),res_geocode[\"lat\"].mean()]\n",
+ "\n",
+ "def accuracy_at_k(geocoding_df,k=100):\n",
+ " geocoding_df[\"distanceKM\"] = haversine_pd(geocoding_df.longitude,geocoding_df.latitude,geocoding_df.pred_longitude,geocoding_df.pred_latitude)\n",
+ " return (geocoding_df.distanceKM <k).sum()/len(geocoding_df)\n",
+ "\n",
+ "def geocode_wikipages(df,k=None):\n",
+ " import random\n",
+ " random.seed(42)\n",
+ " if not k:\n",
+ " found_coords = df.progress_apply(lambda x: heuristic_mean(geo,x.title,x.interlinks),axis=1).values\n",
+ " else:\n",
+ " found_coords = df.progress_apply(lambda x: heuristic_mean(geo,x.title,random.choices(x.interlinks,k=k)),axis=1).values\n",
+ " \n",
+ " found_coords = np.asarray(found_coords.tolist())\n",
+ " return found_coords"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "5374160c45964b9c99bef2f2fb51d920",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(HTML(value=''), FloatProgress(value=0.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "found_coords = geocode_wikipages(df,k_cooc_used)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.loc[:,\"pred_longitude\"] = found_coords[:,0]\n",
+ "df.loc[:,\"pred_latitude\"] = found_coords[:,1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(0.96, 0.81, 0.26)"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "accuracy_at_k(df,100),accuracy_at_k(df,50),accuracy_at_k(df,20)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python (my_env)",
+ "language": "python",
+ "name": "my_env"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}