diff --git a/notebooks/eda.ipynb b/notebooks/eda.ipynb
index e7f6df3..cd049c8 100644
--- a/notebooks/eda.ipynb
+++ b/notebooks/eda.ipynb
@@ -2,6 +2,7 @@
"cells": [
{
"cell_type": "code",
+ "execution_count": 21,
"id": "c93a55c8",
"metadata": {
"ExecuteTime": {
@@ -9,6 +10,7 @@
"start_time": "2025-09-21T12:36:58.109448Z"
}
},
+ "outputs": [],
"source": [
"import pandas as pd\n",
"import geopandas as gpd\n",
@@ -20,12 +22,11 @@
"sys.path.append(os.path.abspath(\"..\"))\n",
"from core.utils.data_loader import DataLoader\n",
"from core.config.pipeline_config import PipelineConfig"
- ],
- "outputs": [],
- "execution_count": 21
+ ]
},
{
"cell_type": "code",
+ "execution_count": 22,
"id": "c0b00261",
"metadata": {
"ExecuteTime": {
@@ -33,6 +34,7 @@
"start_time": "2025-09-21T12:37:00.288721Z"
}
},
+ "outputs": [],
"source": [
"config = PipelineConfig(\n",
" paths={\n",
@@ -47,37 +49,38 @@
")\n",
"\n",
"loader = DataLoader(config)"
- ],
- "outputs": [],
- "execution_count": 22
+ ]
},
{
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "b38394ce38864379",
"metadata": {
"ExecuteTime": {
"end_time": "2025-09-21T13:08:32.554903Z",
"start_time": "2025-09-21T13:07:48.219021Z"
}
},
- "cell_type": "code",
+ "outputs": [],
"source": [
"gdf = gpd.read_file(\"../osm/provinces.shp\")\n",
"gdf_proj = gdf.to_crs(epsg=32732)\n",
"gdf['centroid'] = gdf_proj.geometry.centroid.to_crs(gdf.crs)\n",
"\n",
"df = loader.load_csv_complete(config.paths.data_dir / \"names_featured.csv\")"
- ],
- "id": "b38394ce38864379",
- "outputs": [],
- "execution_count": 36
+ ]
},
{
"cell_type": "markdown",
"id": "c5aee565",
"metadata": {},
- "source": "## Breakdown by Province"
+ "source": [
+ "## Breakdown by Province"
+ ]
},
{
"cell_type": "code",
+ "execution_count": 37,
"id": "dac785a7-1a32-4a1b-b35d-d8b85df60589",
"metadata": {
"ExecuteTime": {
@@ -85,6 +88,7 @@
"start_time": "2025-09-21T13:09:11.722493Z"
}
},
+ "outputs": [],
"source": [
"import unicodedata\n",
"\n",
@@ -107,12 +111,11 @@
"counts.columns = ['province', 'count']\n",
"\n",
"gdf = gdf.merge(counts, left_on='province', right_on='province', how='left')\n"
- ],
- "outputs": [],
- "execution_count": 37
+ ]
},
{
"cell_type": "code",
+ "execution_count": 39,
"id": "c3f83d00-0606-44ea-a704-fc54d69263b4",
"metadata": {
"ExecuteTime": {
@@ -120,50 +123,9 @@
"start_time": "2025-09-21T13:09:31.853694Z"
}
},
- "source": "gdf.head(12)",
"outputs": [
{
"data": {
- "text/plain": [
- " full_id name \\\n",
- "0 r5646651 Kinshasa \n",
- "1 r193090 Bas-Congo \n",
- "2 r5642699 Sud-Kivu \n",
- "3 r5642698 Nord-Kivu \n",
- "4 r5642697 Maniema \n",
- "5 r193087 Kasaï-Occidental \n",
- "6 r193088 Kasaï-Oriental \n",
- "7 r3652052 Équateur \n",
- "8 r3652285 Orientale \n",
- "9 r3652335 Bandundu \n",
- "10 r3652419 Katanga \n",
- "\n",
- " geometry \\\n",
- "0 POLYGON ((16.03321 -4.98926, 16.02639 -4.98868... \n",
- "1 POLYGON ((15.64766 -4.91155, 15.6477 -4.91174,... \n",
- "2 POLYGON ((29.24225 -1.69991, 29.24228 -1.69991... \n",
- "3 POLYGON ((29.39797 0.81304, 29.39806 0.81285, ... \n",
- "4 POLYGON ((28.19331 -4.15135, 28.19369 -4.15152... \n",
- "5 POLYGON ((23.28516 -6.91736, 23.286 -6.91859, ... \n",
- "6 POLYGON ((23.28042 -7.98622, 23.28018 -7.98616... \n",
- "7 POLYGON ((23.31621 -0.24045, 23.31099 -0.24055... \n",
- "8 POLYGON ((23.08876 -0.40575, 23.08401 -0.40572... \n",
- "9 POLYGON ((20.35736 -2.19989, 20.36211 -2.20019... \n",
- "10 POLYGON ((23.1898 -7.76858, 23.18972 -7.77019,... \n",
- "\n",
- " centroid province count \n",
- "0 POINT (15.89714 -4.45276) KINSHASA 1122773 \n",
- "1 POINT (14.28812 -5.2961) BAS-CONGO 268119 \n",
- "2 POINT (28.26546 -3.22633) SUD-KIVU 346152 \n",
- "3 POINT (28.70741 -0.60502) NORD-KIVU 394999 \n",
- "4 POINT (26.39038 -3.06735) MANIEMA 127813 \n",
- "5 POINT (21.62853 -5.4419) KASAI-OCCIDENTAL 362896 \n",
- "6 POINT (23.94445 -4.54154) KASAI-ORIENTAL 358138 \n",
- "7 POINT (20.63056 1.02935) EQUATEUR 356404 \n",
- "8 POINT (26.42917 2.12421) ORIENTALE 317201 \n",
- "9 POINT (18.35771 -4.37828) BANDUNDU 809949 \n",
- "10 POINT (26.4593 -8.73955) KATANGA 836220 "
- ],
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+ "metadata": {},
+ "output_type": "display_data"
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+ "source": [
+ "letters = list('abcdefghijklmnopqrstuvwxyz')\n",
+ "plt.figure(figsize=(12, 10))\n",
+ "sns.heatmap(df_probs.loc[letters, letters].values,\n",
+ " xticklabels=letters, yticklabels=letters,\n",
+ " cmap=\"Reds\")\n",
+ "plt.title(\"Transition probabilities (surnames) — a -> b\")\n",
+ "plt.xlabel(\"Next letter\")\n",
+ "plt.ylabel(\"Current letter\")\n",
+ "plt.show()"
+ ]
+ },
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+ "cell_type": "code",
+ "execution_count": 8,
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+ "source": [
+ "plt.figure(figsize=(12,6))\n",
+ "sns.barplot(x=\"letter\", y=\"freq\", data=df_letters, color=\"steelblue\")\n",
+ "plt.title(\"Letter frequency (alphabetical order)\")\n",
+ "plt.xlabel(\"Letter\")\n",
+ "plt.ylabel(\"Relative frequency\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "b90a37c2-a368-4df2-a2d4-4e5d0fdba63e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df_sorted = df_letters.sort_values(\"freq\", ascending=False)\n",
+ "\n",
+ "plt.figure(figsize=(12,6))\n",
+ "sns.barplot(x=\"letter\", y=\"freq\", data=df_sorted, color=\"darkorange\")\n",
+ "plt.title(\"Letter frequency (descending order)\")\n",
+ "plt.xlabel(\"Letter\")\n",
+ "plt.ylabel(\"Relative frequency\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "41a84964-69b1-4b3f-af5e-02118c26234d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " letter | \n",
+ " count | \n",
+ " freq | \n",
+ "
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+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
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+ " \n",
+ " | 10 | \n",
+ " g | \n",
+ " 1189924 | \n",
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+ "
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+ " \n",
+ " | 11 | \n",
+ " s | \n",
+ " 1048120 | \n",
+ " 0.032629 | \n",
+ "
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+ " \n",
+ " | 12 | \n",
+ " t | \n",
+ " 870620 | \n",
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+ " | 13 | \n",
+ " d | \n",
+ " 833422 | \n",
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+ "
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+ " y | \n",
+ " 654446 | \n",
+ " 0.020373 | \n",
+ "
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+ " \n",
+ " | 15 | \n",
+ " h | \n",
+ " 502652 | \n",
+ " 0.015648 | \n",
+ "
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+ " \n",
+ " | 16 | \n",
+ " w | \n",
+ " 417888 | \n",
+ " 0.013009 | \n",
+ "
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+ " \n",
+ " | 17 | \n",
+ " z | \n",
+ " 386517 | \n",
+ " 0.012032 | \n",
+ "
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+ " \n",
+ " | 18 | \n",
+ " p | \n",
+ " 311915 | \n",
+ " 0.009710 | \n",
+ "
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+ " \n",
+ " | 19 | \n",
+ " r | \n",
+ " 285818 | \n",
+ " 0.008898 | \n",
+ "
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+ " \n",
+ "
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+ "
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(14, 12))\n",
+ "sns.heatmap(df_bigrams_matrix, cmap=\"Reds\", cbar=True, xticklabels=letters, yticklabels=letters)\n",
+ "plt.title(\"Bigram frequencies in surnames\")\n",
+ "plt.xlabel(\"Next letter\")\n",
+ "plt.ylabel(\"Current letter\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8e948cdd-c77b-48f6-b643-0a24f513afa9",
+ "metadata": {},
+ "source": [
+ "### Tableau des bigrammes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "07a8bd99-761e-4492-a8ff-0c66185524d7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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