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+names,0.3189041485139616,0.04320097944655348,0.0215380760498496,0.03236952774820154,0.979 +surnames,1.2770018925640299,0.2936188220992242,0.23989460296618093,0.26675671253270256,0.001 diff --git a/assets/transition_difference.png b/assets/transition_difference.png new file mode 100644 index 0000000..8c2fdc2 Binary files /dev/null and b/assets/transition_difference.png differ diff --git a/notebooks/names.ipynb b/notebooks/names.ipynb index a338089..9ee8f2f 100644 --- a/notebooks/names.ipynb +++ b/notebooks/names.ipynb @@ -1,62 +1,64 @@ { "cells": [ { - "cell_type": "markdown", - "id": "b6cc3c6b-85c7-4a04-9aef-8ffc055aa93c", "metadata": {}, - "source": "# Names Analysis & Modeling" + "cell_type": "markdown", + "source": "# Names Analysis & Modeling", + "id": "ae0a818eafd9bd24" }, { "cell_type": "code", "id": "initial_id", "metadata": { "ExecuteTime": { - "end_time": "2025-09-25T19:54:31.700090Z", - "start_time": "2025-09-25T19:54:31.689969Z" + "end_time": "2025-09-28T14:57:07.646210Z", + "start_time": "2025-09-28T14:57:07.643563Z" } }, "source": [ - "import pandas as pd \n", - "import unicodedata \n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", - "import numpy as np \n", - "import re \n", "import sys \n", "import os\n", - "from collections import Counter\n", - "\n", - "from rich.jupyter import display\n", - "\n", - "sys.path.append(os.path.abspath(\"..\")) \n", - "from collections import Counter \n", - "from core.utils.data_loader import DataLoader\n", - "from core.utils.region_mapper import RegionMapper\n", - "from core.config.pipeline_config import PipelineConfig" + "from collections import Counter" ], "outputs": [], - "execution_count": 116 + "execution_count": 1 }, { "metadata": { "ExecuteTime": { - "end_time": "2025-09-25T18:26:50.162866Z", - "start_time": "2025-09-25T18:26:50.159601Z" + "end_time": "2025-09-28T14:57:07.915456Z", + "start_time": "2025-09-28T14:57:07.719242Z" } }, "cell_type": "code", - "source": "LETTERS = 'abcdefghijklmnopqrstuvwxyz'", - "id": "16647cc71aea7594", + "source": [ + "sys.path.append(os.path.abspath(\"..\"))\n", + "\n", + "from core.utils.data_loader import DataLoader\n", + "from core.utils.region_mapper import RegionMapper\n", + "from core.config.pipeline_config import PipelineConfig\n", + "\n", + "from research.statistics.utils import LETTERS\n", + "from research.statistics.utils import identified_category_dist\n", + "from research.statistics.utils import explode_words_token\n", + "from research.statistics.utils import build_transition_probabilities\n", + "from research.statistics.utils import build_transition_comparisons\n", + "from research.statistics.utils import build_letter_frequencies\n", + "from research.statistics.plots import plot_transition_matrix" + ], + "id": "584a6fcfcbea71e4", "outputs": [], - "execution_count": 47 + "execution_count": 2 }, { "cell_type": "code", "id": "f1a69290-a9c0-40d0-9fe8-a06d8a466671", "metadata": { "ExecuteTime": { - "end_time": "2025-09-25T17:39:49.310278Z", - "start_time": "2025-09-25T17:39:49.304876Z" + "end_time": "2025-09-28T14:57:08.401248Z", + "start_time": "2025-09-28T14:57:08.396493Z" } }, "source": [ @@ -72,16 +74,7 @@ " }\n", ")\n", "\n", - "loader = DataLoader(config)\n", - "\n", - "def normalize_letters(s):\n", - " \"\"\"Normalize accents -> ascii, lowercase, keep only a-z.\"\"\"\n", - " s = str(s)\n", - " s = unicodedata.normalize(\"NFKD\", s)\n", - " s = s.encode(\"ascii\", errors=\"ignore\").decode(\"utf-8\")\n", - " s = s.lower()\n", - " s = re.sub(r\"[^a-z]\", \"\", s)\n", - " return s" + "loader = DataLoader(config)" ], "outputs": [], "execution_count": 3 @@ -89,29 +82,17 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-09-25T17:59:34.598256Z", - "start_time": "2025-09-25T17:58:54.210735Z" + "end_time": "2025-09-28T14:57:36.335481Z", + "start_time": "2025-09-28T14:57:08.553275Z" } }, "cell_type": "code", "source": [ "df = loader.load_csv_complete(config.paths.data_dir / \"names_featured.csv\")\n", - "df['province'] = RegionMapper.clean_province(df['province'])" + "df['province'] = RegionMapper.clean_province(df['province'])\n", + "df.columns" ], "id": "e48c6fd9a213bcd2", - "outputs": [], - "execution_count": 23 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2025-09-25T17:59:38.255948Z", - "start_time": "2025-09-25T17:59:38.249016Z" - } - }, - "cell_type": "code", - "source": "df.columns", - "id": "2715f291947f5158", "outputs": [ { "data": { @@ -123,250 +104,77 @@ " dtype='object')" ] }, - "execution_count": 24, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 24 + "execution_count": 4 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## Name category distribution", + "id": "46429fc98b67a89f" }, { "metadata": { "ExecuteTime": { - "end_time": "2025-09-25T18:01:22.242283Z", - "start_time": "2025-09-25T18:01:21.580597Z" - } - }, - "cell_type": "code", - "source": "df.describe().T", - "id": "93f8859e3e9c4350", - "outputs": [ - { - "data": { - "text/plain": [ - " count mean std min 25% 50% 75% max\n", - "words 6467942.0 2.869578 0.46841 1.0 3.0 3.0 3.0 11.0\n", - "length 6467942.0 20.141236 3.796574 0.0 18.0 21.0 23.0 60.0\n", - "ner_tagged 5018124.0 0.997939 0.045348 0.0 1.0 1.0 1.0 1.0\n", - "annotated 5018124.0 0.997939 0.045348 0.0 1.0 1.0 1.0 1.0" - ], - "text/html": [ - "
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" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 28 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2025-09-25T18:02:53.214472Z", - "start_time": "2025-09-25T18:02:52.427559Z" + "end_time": "2025-09-28T14:57:43.780783Z", + "start_time": "2025-09-28T14:57:43.246962Z" } }, "cell_type": "code", "source": [ - "# Group by province and compute counts\n", - "word_stats = (\n", - " df.groupby(\"province\")[\"identified_category\"]\n", - " .value_counts(normalize=True) # get proportions\n", - " .unstack(fill_value=0) # reshape into columns per word count\n", - ")\n", + "df_name_categories = identified_category_dist(df)\n", + "df_name_categories.head(12)\n", "\n", - "display(word_stats.head(12))" + "# save data\n", + "df_name_categories.to_csv(\"../assets/identified_category_distribution.csv\")" ], "id": "378147d2abc9ab24", + "outputs": [], + "execution_count": 5 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### Simple vs Compose (all provinces)", + "id": "3c99d846cb37c469" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-28T14:57:45.847127Z", + "start_time": "2025-09-28T14:57:43.788533Z" + } + }, + "cell_type": "code", + "source": [ + "labels = ['Simple', 'Compose']\n", + "values = [\n", + " len(df.query(\"identified_category == 'simple'\")),\n", + " len(df.query(\"identified_category == 'compose'\"))\n", + "]\n", + "\n", + "plt.figure(figsize=(6, 6))\n", + "plt.pie(values, labels=labels, autopct='%1.1f%%')\n", + "plt.axis(\"equal\")\n", + "\n", + "# save figures\n", + "plt.savefig(\"../assets/identified_category_distribution.png\")\n", + "plt.savefig(\"../assets/identified_category_distribution.svg\")\n", + "\n", + "plt.show()" + ], + "id": "ae30e79a975010d4", "outputs": [ { "data": { "text/plain": [ - "identified_category compose simple\n", - "province \n", - "AUTRES 0.206217 0.793783\n", - "BANDUNDU 0.626906 0.373094\n", - "BAS-CONGO 0.090813 0.909187\n", - "EQUATEUR 0.124238 0.875762\n", - "KASAI-OCCIDENTAL 0.261266 0.738734\n", - "KASAI-ORIENTAL 0.076224 0.923776\n", - "KATANGA 0.180624 0.819376\n", - "KINSHASA 0.076792 0.923208\n", - "MANIEMA 0.461150 0.538850\n", - "NORD-KIVU 0.119626 0.880374\n", - "ORIENTALE 0.160905 0.839095\n", - "SUD-KIVU 0.409647 0.590353" + "
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KINSHASA0.0767920.923208
MANIEMA0.4611500.538850
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" }, "metadata": {}, "output_type": "display_data", @@ -375,28 +183,43 @@ } } ], - "execution_count": 30 + "execution_count": 6 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### Simple vs Compose by Province", + "id": "d31d8ae890cdfece" }, { "metadata": { "ExecuteTime": { - "end_time": "2025-09-25T18:05:12.951347Z", - "start_time": "2025-09-25T18:05:12.736698Z" + "end_time": "2025-09-28T14:57:46.356962Z", + "start_time": "2025-09-28T14:57:45.874356Z" } }, "cell_type": "code", "source": [ - "ax = word_stats.plot(\n", - " kind=\"bar\",\n", + "df_name_categories_pct = df_name_categories.div(df_name_categories.sum(axis=1), axis=0) * 100\n", + "ax = df_name_categories_pct.plot.barh(\n", " stacked=True,\n", " figsize=(12, 6),\n", - " colormap=\"tab20c\"\n", + " colormap=\"tab20c\",\n", + " width=0.85\n", ")\n", "\n", - "plt.xlabel(\"Province\")\n", - "plt.ylabel(\"Proportion (%)\")\n", - "plt.legend(title=\"Name Category\", bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", + "ax.set_xlabel(\"Proportion (%)\")\n", + "ax.set_ylabel(\"Province\")\n", + "ax.set_xlim(0, 100)\n", + "ax.grid(axis=\"x\", linestyle=\"--\", alpha=0.6)\n", + "\n", + "plt.legend(title=\"Category\", bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", "plt.tight_layout()\n", + "\n", + "# save figures\n", + "plt.savefig(\"../assets/identified_category_distribution_by_province.png\")\n", + "plt.savefig(\"../assets/identified_category_distribution_by_province.svg\")\n", + "\n", "plt.show()" ], "id": "6d5c1abb55b7076a", @@ -406,7 +229,7 @@ "text/plain": [ "
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}, "metadata": {}, "output_type": "display_data", @@ -415,52 +238,41 @@ } } ], - "execution_count": 33 + "execution_count": 7 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## Native Names vs Surnames", + "id": "1973b08d18c2258e" }, { - "cell_type": "code", - "id": "24a4ad40319f441b", "metadata": { "ExecuteTime": { - "end_time": "2025-09-25T18:07:02.981141Z", - "start_time": "2025-09-25T18:06:14.115049Z" + "end_time": "2025-09-28T14:57:47.131714Z", + "start_time": "2025-09-28T14:57:46.364153Z" } }, - "source": [ - "# Explode identified_name into words\n", - "df_names = (\n", - " df.assign(\n", - " name=(\n", - " df['identified_name']\n", - " .fillna('')\n", - " .str.replace(r\"[^\\w'\\-]+\", \" \", regex=True) # keep letters, digits, _, -, '\n", - " .str.strip()\n", - " .str.split()\n", - " )\n", - " )\n", - " .explode('name', ignore_index=True)\n", - " .dropna(subset=['name'])\n", - ")\n", - "\n", - "# Clean up tokens\n", - "df_names['name'] = df_names['name'].str.strip()\n", - "df_names = df_names[df_names['name'].ne('')]\n", - "\n", - "df_names = df_names[['name', 'province', 'sex']].reset_index(drop=True)" - ], + "cell_type": "code", + "source": "df_base = df.query(\"identified_category == 'simple'\")", + "id": "1e7dde234bb3504f", "outputs": [], - "execution_count": 34 + "execution_count": 8 }, { "metadata": { "ExecuteTime": { - "end_time": "2025-09-25T18:01:37.658949Z", - "start_time": "2025-09-25T18:01:33.224026Z" + "end_time": "2025-09-28T14:58:19.334139Z", + "start_time": "2025-09-28T14:57:47.146184Z" } }, "cell_type": "code", - "source": "df_names.describe().T", - "id": "5ce61cb4109c1cee", + "source": [ + "df_names = explode_words_token(df_base, 'identified_name', 'name')\n", + "df_names = df_names[['name', 'province', 'sex']].reset_index(drop=True)\n", + "df_names.describe().T" + ], + "id": "24a4ad40319f441b", "outputs": [ { "data": { @@ -522,77 +334,35 @@ "" ] }, - "execution_count": 29, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 29 + "execution_count": 9 }, { - "cell_type": "code", - "id": "77abf83a2f6360f5", "metadata": { "ExecuteTime": { - "end_time": "2025-09-25T17:48:50.735911Z", - "start_time": "2025-09-25T17:48:49.779413Z" + "end_time": "2025-09-28T14:58:41.141954Z", + "start_time": "2025-09-28T14:58:19.869428Z" } }, + "cell_type": "code", "source": [ - "# Province counts before exploding\n", - "prov_counts_orig = (\n", - " df['province'].value_counts()\n", - " .rename_axis(\"province\")\n", - " .reset_index(name=\"count_original\")\n", - ")\n", - "\n", - "# Province counts after exploding into df_names\n", - "prov_counts_names = (\n", - " df_names['province'].value_counts()\n", - " .rename_axis(\"province\")\n", - " .reset_index(name=\"count_names\")\n", - ")\n", - "\n", - "# Merge both into one table\n", - "comparison = (\n", - " prov_counts_orig\n", - " .merge(prov_counts_names, on=\"province\", how=\"outer\")\n", - " .fillna(0)\n", - ")\n", - "\n", - "# Add augmentation percentage\n", - "comparison[\"augmentation\"] = (\n", - " (comparison[\"count_names\"] - comparison[\"count_original\"])\n", - " / comparison[\"count_original\"]\n", - " * 100\n", - ").round(2)\n", - "\n", - "# Sort and display top 12 provinces\n", - "comparison = (\n", - " comparison\n", - " .sort_values(\"count_original\", ascending=False)\n", - " .reset_index(drop=True)\n", - ")\n", - "\n", - "display(comparison.head(12))" + "df_surnames = explode_words_token(df_base, 'identified_surname', 'name')\n", + "df_surnames = df_surnames[['name', 'province', 'sex']].reset_index(drop=True)\n", + "df_surnames.describe().T" ], + "id": "736b0d787b9a6809", "outputs": [ { "data": { "text/plain": [ - " province count_original count_names augmentation\n", - "0 KINSHASA 1140620 2106077 84.64\n", - "1 AUTRES 1035751 1644326 58.76\n", - "2 KATANGA 836220 1370359 63.88\n", - "3 BANDUNDU 809949 604374 -25.38\n", - "4 KASAI-ORIENTAL 434497 802757 84.76\n", - "5 NORD-KIVU 394999 695494 76.07\n", - "6 KASAI-OCCIDENTAL 367626 543156 47.75\n", - "7 EQUATEUR 356404 624252 75.15\n", - "8 SUD-KIVU 346152 408708 18.07\n", - "9 ORIENTALE 322756 541646 67.82\n", - "10 BAS-CONGO 295155 536702 81.84\n", - "11 MANIEMA 127813 137744 7.77" + " count unique top freq\n", + "name 5007877 253743 jean 89564\n", + "province 5007877 12 KINSHASA 1053047\n", + "sex 5007877 2 m 3017009" ], "text/html": [ "
\n", @@ -613,102 +383,140 @@ " \n", " \n", " \n", - " province\n", - " count_original\n", - " count_names\n", - " augmentation\n", + " count\n", + " unique\n", + " top\n", + " freq\n", " \n", " \n", " \n", " \n", - " 0\n", + " name\n", + " 5007877\n", + " 253743\n", + " jean\n", + " 89564\n", + " \n", + " \n", + " province\n", + " 5007877\n", + " 12\n", " KINSHASA\n", - " 1140620\n", - " 2106077\n", - " 84.64\n", + " 1053047\n", " \n", " \n", - " 1\n", - " AUTRES\n", - " 1035751\n", - " 1644326\n", - " 58.76\n", - " \n", - " \n", - " 2\n", - " KATANGA\n", - " 836220\n", - " 1370359\n", - " 63.88\n", - " \n", - " \n", - " 3\n", - " BANDUNDU\n", - " 809949\n", - " 604374\n", - " -25.38\n", - " \n", - " \n", - " 4\n", - " KASAI-ORIENTAL\n", - " 434497\n", - " 802757\n", - " 84.76\n", - " \n", - " \n", - " 5\n", - " NORD-KIVU\n", - " 394999\n", - " 695494\n", - " 76.07\n", - " \n", - " \n", - " 6\n", - " KASAI-OCCIDENTAL\n", - " 367626\n", - " 543156\n", - " 47.75\n", - " \n", - " \n", - " 7\n", - " EQUATEUR\n", - " 356404\n", - " 624252\n", - " 75.15\n", - " \n", - " \n", - " 8\n", - " SUD-KIVU\n", - " 346152\n", - " 408708\n", - " 18.07\n", - " \n", - " \n", - " 9\n", - " ORIENTALE\n", - " 322756\n", - " 541646\n", - " 67.82\n", - " \n", - " \n", - " 10\n", - " BAS-CONGO\n", - " 295155\n", - " 536702\n", - " 81.84\n", - " \n", - " \n", - " 11\n", - " MANIEMA\n", - " 127813\n", - " 137744\n", - " 7.77\n", + " sex\n", + " 5007877\n", + " 2\n", + " m\n", + " 3017009\n", " \n", " \n", "\n", "
" ] }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 10 + }, + { + "cell_type": "markdown", + "id": "98ba6e48-a7c4-4aae-8b46-872489c95faf", + "metadata": {}, + "source": "### Transition probabilities" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-28T14:59:04.922560Z", + "start_time": "2025-09-28T14:58:41.384503Z" + } + }, + "cell_type": "code", + "source": [ + "names_transitions = build_transition_probabilities(df_names['name'])\n", + "names_transitions_males = build_transition_probabilities(df_names.query(\"sex == 'm'\")['name'])\n", + "names_transitions_females = build_transition_probabilities(df_names.query(\"sex == 'f'\")['name'])\n", + "\n", + "names_transitions['df_probs'].to_csv(\"../assets/names_transition_probs.csv\")" + ], + "id": "12a7094d94ad519f", + "outputs": [], + "execution_count": 11 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-28T14:59:17.020745Z", + "start_time": "2025-09-28T14:59:04.964198Z" + } + }, + "cell_type": "code", + "source": [ + "surnames_transitions = build_transition_probabilities(df_surnames['name'])\n", + "surnames_transitions_males = build_transition_probabilities(df_surnames.query(\"sex == 'm'\")['name'])\n", + "surnames_transitions_females = build_transition_probabilities(df_surnames.query(\"sex == 'f'\")['name'])\n", + "\n", + "surnames_transitions['df_probs'].to_csv(\"../assets/surnames_transition_probs.csv\")" + ], + "id": "684b467b17955d65", + "outputs": [], + "execution_count": 12 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-28T14:59:21.075645Z", + "start_time": "2025-09-28T14:59:17.790264Z" + } + }, + "cell_type": "code", + "source": [ + "fig, axes = plt.subplots(1, 3, figsize=(20, 6), sharex=True, sharey=True)\n", + "hm1 = plot_transition_matrix(axes[0], names_transitions['df_probs'], \"Native - Male\")\n", + "hm2 = plot_transition_matrix(axes[1], names_transitions_females['df_probs'], \"Native - Female\")\n", + "hm3 = plot_transition_matrix(axes[2], names_transitions_males['df_probs'], \"Native\")\n", + "cbar = fig.colorbar(hm3.collections[0], ax=axes, orientation=\"vertical\", fraction=0.03, pad=0.02)\n", + "cbar.set_label(\"Transition probability\")\n", + "plt.savefig(\"../assets/names_transition_probabilities.png\")\n", + "plt.savefig(\"../assets/names_transition_probabilities.svg\")\n", + "\n", + "fig, axes = plt.subplots(1, 3, figsize=(20, 6), sharex=True, sharey=True)\n", + "hm4 = plot_transition_matrix(axes[0], surnames_transitions['df_probs'], \"Surnames - Male\")\n", + "hm5 = plot_transition_matrix(axes[1], surnames_transitions_females['df_probs'], \"Surnames - Female\")\n", + "hm6 = plot_transition_matrix(axes[2], surnames_transitions_males['df_probs'], \"Surnames\")\n", + "cbar = fig.colorbar(hm6.collections[0], ax=axes, orientation=\"vertical\", fraction=0.03, pad=0.02)\n", + "cbar.set_label(\"Transition probability\")\n", + "plt.savefig(\"../assets/surnames_transition_probabilities.png\")\n", + "plt.savefig(\"../assets/surnames_transition_probabilities.svg\")\n", + "plt.show()" + ], + "id": "f15ad6eb4df27527", + "outputs": [ + { + "data": { + "text/plain": [ + "
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+ }, "metadata": {}, "output_type": "display_data", "jetTransient": { @@ -719,193 +527,38 @@ "execution_count": 13 }, { - "cell_type": "markdown", - "id": "98ba6e48-a7c4-4aae-8b46-872489c95faf", "metadata": {}, - "source": "## Transition probabilities" - }, - { - "cell_type": "code", - "id": "e324e1c9e8da4bd8", - "metadata": { - "ExecuteTime": { - "end_time": "2025-09-25T19:59:00.238808Z", - "start_time": "2025-09-25T19:59:00.225716Z" - } - }, - "source": [ - "def build_transition_probs(\n", - " names: pd.Series,\n", - " *,\n", - " letters: str = \"abcdefghijklmnopqrstuvwxyz\",\n", - " start_token: str = \"^\",\n", - " end_token: str = \"$\",\n", - " alpha: float = 0.0, # Laplace smoothing; e.g., 1.0 to avoid zeros\n", - ") -> dict:\n", - " # 1) Normalize\n", - " names = (\n", - " names.astype(str)\n", - " .str.lower()\n", - " .str.replace(fr\"[^{letters}]\", \"\", regex=True)\n", - " )\n", - " names = names[names.str.len() > 0]\n", - "\n", - " # 2) Prepare sequences\n", - " sequences = (start_token + names + end_token).tolist()\n", - "\n", - " # 3) Tokens and indices\n", - " tokens = [start_token] + list(letters) + [end_token]\n", - " index = {t: i for i, t in enumerate(tokens)}\n", - " V = len(tokens)\n", - "\n", - " # 4) ASCII lookup table (O(1) char -> idx); others -> -1\n", - " lut = np.full(128, -1, dtype=np.int32)\n", - " for ch, i in index.items():\n", - " lut[ord(ch)] = i\n", - "\n", - " # 5) Concatenate with a separator that’s not in vocab to kill cross-boundary pairs\n", - " concat = (\" \".join(sequences)).encode(\"ascii\", errors=\"ignore\")\n", - "\n", - " # 6) Map bytes to indices\n", - " arr = np.frombuffer(concat, dtype=np.uint8)\n", - " idx = lut[arr]\n", - "\n", - " # 7) Build bigram pairs; drop invalid ones (separator & OOV)\n", - " a = idx[:-1]\n", - " b = idx[1:]\n", - " mask = (a >= 0) & (b >= 0)\n", - " a, b = a[mask], b[mask]\n", - "\n", - " # 8) Count with a single bincount\n", - " lin = a * V + b\n", - " counts = np.bincount(lin, minlength=V * V).reshape(V, V)\n", - "\n", - " # 9) Optional Laplace smoothing\n", - " if alpha and alpha > 0:\n", - " counts = counts + alpha\n", - "\n", - " # 10) Row-normalize to probabilities\n", - " row_sums = counts.sum(axis=1, keepdims=True)\n", - " # avoid division by zero\n", - " probs = np.divide(counts, np.where(row_sums == 0, 1.0, row_sums), where=True)\n", - "\n", - " # 11) DataFrames\n", - " df_counts = pd.DataFrame(counts, index=tokens, columns=tokens)\n", - " df_probs = pd.DataFrame(probs, index=tokens, columns=tokens)\n", - "\n", - " return {\n", - " \"tokens\": tokens,\n", - " \"index\": index,\n", - " \"counts\": counts,\n", - " \"df_counts\": df_counts,\n", - " \"probs\": probs,\n", - " \"df_probs\": df_probs,\n", - " }\n" - ], - "outputs": [], - "execution_count": 133 + "cell_type": "markdown", + "source": "### Transition Probability Differences", + "id": "1f59136fe68b02a8" }, { "metadata": { "ExecuteTime": { - "end_time": "2025-09-25T19:59:41.506588Z", - "start_time": "2025-09-25T19:59:03.640342Z" + "end_time": "2025-09-28T14:59:22.072443Z", + "start_time": "2025-09-28T14:59:21.120458Z" } }, "cell_type": "code", "source": [ - "transitions_both = build_transition_probs(df_names['name'])\n", - "transitions_male = build_transition_probs(df_names.loc[df_names['sex'].str.lower() == 'm', 'name'])\n", - "transitions_female = build_transition_probs(df_names.loc[df_names['sex'].str.lower() == 'f', 'name'])\n", + "names_transitions_diff = names_transitions_males['df_probs'] - names_transitions_females['df_probs']\n", + "surnames_transitions_diff = surnames_transitions_males['df_probs'] - surnames_transitions_females['df_probs']\n", "\n", - "# Access the probability matrices\n", - "P_both = transitions_both['probs']\n", - "P_male = transitions_male['probs']\n", - "P_female = transitions_female['probs']" - ], - "id": "12a7094d94ad519f", - "outputs": [], - "execution_count": 134 - }, - { - "cell_type": "code", - "id": "67b3e0f724af646b", - "metadata": { - "ExecuteTime": { - "end_time": "2025-09-25T20:02:32.577754Z", - "start_time": "2025-09-25T20:02:32.569329Z" - } - }, - "source": [ - "def plot_transition_matrix(ax, df_probs, title=\"\", letters=\"abcdefghijklmnopqrstuvwxyz\"):\n", - " hm = sns.heatmap(\n", - " df_probs.loc[list(letters), list(letters)],\n", - " cmap=\"Reds\",\n", - " annot=False,\n", - " cbar=False,\n", - " ax=ax\n", - " )\n", - " ax.set_title(title, fontsize=12)\n", - " return hm" - ], - "outputs": [], - "execution_count": 135 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2025-09-25T20:02:36.087443Z", - "start_time": "2025-09-25T20:02:34.953458Z" - } - }, - "cell_type": "code", - "source": [ - "# Create 1x3 grid\n", - "fig, axes = plt.subplots(1, 3, figsize=(20, 6), sharex=True, sharey=True)\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 6), sharex=True, sharey=True)\n", "\n", - "hm1 = plot_transition_matrix(axes[0], transitions_male['df_probs'], \"Male\")\n", - "hm2 = plot_transition_matrix(axes[1], transitions_female['df_probs'], \"Female\")\n", - "hm3 = plot_transition_matrix(axes[2], transitions_both['df_probs'], \"All\")\n", + "hm1 = sns.heatmap(names_transitions_diff.loc[list(LETTERS), list(LETTERS)], cmap=\"RdBu_r\", center=0, ax=axes[0], cbar=False)\n", + "axes[0].set_title(\"Names Transition Difference (Male − Female)\")\n", + "axes[0].set_xlabel(\"Next letter\"); axes[0].set_ylabel(\"Current letter\")\n", "\n", - "# Add one shared colorbar\n", - "cbar = fig.colorbar(hm3.collections[0], ax=axes, orientation=\"vertical\", fraction=0.03, pad=0.02)\n", - "cbar.set_label(\"Transition probability\")\n", + "hm2 = sns.heatmap(surnames_transitions_diff.loc[list(LETTERS), list(LETTERS)], cmap=\"RdBu_r\", center=0, ax=axes[1], cbar=False)\n", + "axes[1].set_title(\"Surnames Transition Difference (Male − Female)\")\n", + "axes[1].set_xlabel(\"Next letter\"); axes[1].set_ylabel(\"\")\n", "\n", - "plt.show()" - ], - "id": "f15ad6eb4df27527", - "outputs": 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- }, - "metadata": {}, - "output_type": "display_data", - "jetTransient": { - "display_id": null - } - } - ], - "execution_count": 136 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2025-09-25T20:03:42.518370Z", - "start_time": "2025-09-25T20:03:42.145944Z" - } - }, - "cell_type": "code", - "source": [ - "diff = transitions_male['df_probs'] - transitions_female['df_probs']\n", + "# Shared colorbar\n", + "cbar = fig.colorbar(hm2.collections[0], ax=axes, orientation=\"vertical\", fraction=0.03, pad=0.02)\n", + "cbar.set_label(\"Difference in transition probability (Male − Female)\")\n", "\n", - "plt.figure(figsize=(6, 5))\n", - "sns.heatmap(diff.loc[list(LETTERS), list(LETTERS)], cmap=\"RdBu_r\", center=0, annot=False)\n", - "plt.title(\"Male – Female Transition Probability Differences\")\n", - "plt.tight_layout()\n", + "plt.savefig(\"../assets/transition_difference.png\")\n", "plt.show()" ], "id": "51ec0792317364fc", @@ -913,9 +566,9 @@ { "data": { "text/plain": [ - "
" + "
" ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data", @@ -924,168 +577,37 @@ } } ], - "execution_count": 142 + "execution_count": 14 }, { "metadata": { "ExecuteTime": { - "end_time": "2025-09-25T19:54:41.378255Z", - "start_time": "2025-09-25T19:54:41.361591Z" + "end_time": "2025-09-28T14:59:24.145603Z", + "start_time": "2025-09-28T14:59:22.140871Z" } }, "cell_type": "code", "source": [ - "import numpy as np\n", - "from scipy.spatial.distance import euclidean\n", - "from scipy.stats import entropy\n", - "\n", - "P_m = transitions_male['probs'].flatten()\n", - "P_f = transitions_female['probs'].flatten()\n", - "\n", - "# L2 distance\n", - "l2 = euclidean(P_m, P_f)\n", - "\n", - "# KL divergence (use smoothing to avoid log(0))\n", - "kl_mf = entropy(P_m + 1e-12, P_f + 1e-12) # D_KL(male || female)\n", - "kl_fm = entropy(P_f + 1e-12, P_m + 1e-12)\n", - "\n", - "# Symmetric Jensen-Shannon divergence\n", - "jsd = 0.5 * (kl_mf + kl_fm)\n", - "\n", - "print(f\"L2 distance: {l2:.4f}\")\n", - "print(f\"KL(male||female): {kl_mf:.4f}\")\n", - "print(f\"KL(female||male): {kl_fm:.4f}\")\n", - "print(f\"JSD: {jsd:.4f}\")\n" + "df_comparisons = build_transition_comparisons(\n", + " {\n", + " 'm': names_transitions_males,\n", + " 'f': names_transitions_females\n", + " },\n", + " {\n", + " 'm': surnames_transitions_males,\n", + " 'f': surnames_transitions_females\n", + " }\n", + ")\n", + "df_comparisons.head(3)" ], "id": "dae836cd8a6c26e6", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "L2 distance: 0.3189\n", - "KL(male||female): 0.0432\n", - "KL(female||male): 0.0215\n", - "JSD: 0.0324\n" - ] - } - ], - "execution_count": 117 - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "The transition probabilities of characters in male and female names from your dataset are very similar. There are measurable but small differences: male names diverge slightly more from the female pattern than the reverse. However, the overall structure of how characters follow each other is largely shared between the two groups.", - "id": "f0bbe49cf3e65f10" - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2025-09-25T20:20:11.270081Z", - "start_time": "2025-09-25T20:20:11.244279Z" - } - }, - "cell_type": "code", - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "from scipy.stats import chi2_contingency\n", - "\n", - "def chi2_row_test(trans_male, trans_female, row_token, alpha=0.5, min_expected=1e-12):\n", - " \"\"\"\n", - " Chi-square test comparing next-letter distributions for a given starting letter.\n", - " Handles zeros by dropping all-zero columns and adding pseudocount alpha.\n", - "\n", - " Args:\n", - " trans_male, trans_female: dicts returned by build_transition_probs (need 'counts','index','tokens')\n", - " row_token: e.g., 'a'\n", - " alpha: pseudocount added to every cell (0.5 is common; set 0 to disable)\n", - " min_expected: tiny floor to avoid expected==0 after drop (defensive)\n", - " \"\"\"\n", - " i_m = trans_male['index'][row_token]\n", - " i_f = trans_female['index'][row_token]\n", - " cm = trans_male['counts'][i_m].astype(float)\n", - " cf = trans_female['counts'][i_f].astype(float)\n", - "\n", - " # Stack into 2xK\n", - " table = np.vstack([cm, cf])\n", - "\n", - " # Drop columns with zero total across both groups\n", - " keep = table.sum(axis=0) > 0\n", - " table = table[:, keep]\n", - "\n", - " # If everything got dropped or only 1 column left, test is undefined\n", - " if table.size == 0 or table.shape[1] < 2:\n", - " return np.nan, np.nan, 0, None\n", - "\n", - " # Add pseudocounts (helps with sparsity and zero expected)\n", - " if alpha and alpha > 0:\n", - " table = table + alpha\n", - "\n", - " chi2, p, dof, expected = chi2_contingency(table, correction=False)\n", - " # Defensive floor (optional)\n", - " if np.any(expected < min_expected):\n", - " # fall back: jitter + rerun (very rare)\n", - " table = table + 1e-9\n", - " chi2, p, dof, expected = chi2_contingency(table, correction=False)\n", - " return chi2, p, dof, expected\n", - "\n", - "# Example: a single row\n", - "chi2, p, dof, expected = chi2_row_test(transitions_male, transitions_female, row_token='a', alpha=0.5)\n", - "print(f\"Row 'a' → χ²={chi2:.3f}, dof={dof}, p={p:.3e}\")\n", - "\n", - "# Run for all letters and assemble a summary (with FDR correction)\n", - "from statsmodels.stats.multitest import multipletests\n", - "\n", - "tokens = [t for t in transitions_both['tokens'] if t not in ('^','$')]\n", - "results = []\n", - "for t in tokens:\n", - " chi2, p, dof, _ = chi2_row_test(transitions_male, transitions_female, row_token=t, alpha=0.5)\n", - " results.append((t, chi2, dof, p))\n", - "\n", - "df_tests = pd.DataFrame(results, columns=['row_token','chi2','dof','p'])\n", - "# FDR (Benjamini–Hochberg)\n", - "mask = df_tests['p'].notna()\n", - "rej, p_adj, _, _ = multipletests(df_tests.loc[mask, 'p'], method='fdr_bh')\n", - "df_tests.loc[mask, 'p_adj'] = p_adj\n", - "df_tests.loc[mask, 'significant'] = rej\n", - "\n", - "df_tests = df_tests.sort_values('chi2', ascending=False).reset_index(drop=True)" - ], - "id": "d029bbd85794df95", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Row 'a' → χ²=66636.402, dof=26, p=0.000e+00\n" - ] - } - ], - "execution_count": 153 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2025-09-25T20:20:17.405785Z", - "start_time": "2025-09-25T20:20:17.395818Z" - } - }, - "cell_type": "code", - "source": "df_tests.head(7)", - "id": "80e7f52285a073ea", "outputs": [ { "data": { "text/plain": [ - " row_token chi2 dof p p_adj significant\n", - "0 a 66636.401639 26 0.0 0.0 True\n", - "1 i 36497.202246 26 0.0 0.0 True\n", - "2 n 32505.512092 25 0.0 0.0 True\n", - "3 u 31904.897504 26 0.0 0.0 True\n", - "4 g 24595.637377 26 0.0 0.0 True\n", - "5 m 23254.272134 26 0.0 0.0 True\n", - "6 e 19945.183545 26 0.0 0.0 True" + " l2 kl_mf kl_fm jsd permutation_p_value\n", + "names 0.318904 0.043201 0.021538 0.032370 0.979\n", + "surnames 1.277002 0.293619 0.239895 0.266757 0.001" ], "text/html": [ "
\n", @@ -1106,140 +628,86 @@ " \n", " \n", " \n", - " row_token\n", - " chi2\n", - " dof\n", - " p\n", - " p_adj\n", - " significant\n", + " l2\n", + " kl_mf\n", + " kl_fm\n", + " jsd\n", + " permutation_p_value\n", " \n", " \n", " \n", " \n", - " 0\n", - " a\n", - " 66636.401639\n", - " 26\n", - " 0.0\n", - " 0.0\n", - " True\n", + " names\n", + " 0.318904\n", + " 0.043201\n", + " 0.021538\n", + " 0.032370\n", + " 0.979\n", " \n", " \n", - " 1\n", - " i\n", - " 36497.202246\n", - " 26\n", - " 0.0\n", - " 0.0\n", - " True\n", - " \n", - " \n", - " 2\n", - " n\n", - " 32505.512092\n", - " 25\n", - " 0.0\n", - " 0.0\n", - " True\n", - " \n", - " \n", - " 3\n", - " u\n", - " 31904.897504\n", - " 26\n", - " 0.0\n", - " 0.0\n", - " True\n", - " \n", - " \n", - " 4\n", - " g\n", - " 24595.637377\n", - " 26\n", - " 0.0\n", - " 0.0\n", - " True\n", - " \n", - " \n", - " 5\n", - " m\n", - " 23254.272134\n", - " 26\n", - " 0.0\n", - " 0.0\n", - " True\n", - " \n", - " \n", - " 6\n", - " e\n", - " 19945.183545\n", - " 26\n", - " 0.0\n", - " 0.0\n", - " True\n", + " surnames\n", + " 1.277002\n", + " 0.293619\n", + " 0.239895\n", + " 0.266757\n", + " 0.001\n", " \n", " \n", "\n", "
" ] }, - "execution_count": 155, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 155 + "execution_count": 15 }, { "metadata": {}, "cell_type": "markdown", "source": [ - "Male and female names in your dataset have very similar character transition patterns overall (global JSD ≈ 0.03),\n", - "But for certain letters — especially a, i, n, u, g, m, e — the differences are statistically huge.\n", - "These letters likely correspond to prefixes or suffixes that carry strong gender information in local naming traditions." + "l2 (Euclidean Distance): This metric shows the overall magnitude of the difference between the two matrices. The value for surnames (1.277) is significantly higher than for names (0.3189), indicating that the male and female surname transition matrices are farther apart than those for first names.\n", + "\n", + "kl_mf and kl_fm (Kullback-Leibler Divergence): These values measure the information lost when one gender's probability distribution is used to approximate the other. The values for surnames (0.2936 and 0.2399) are much larger than for names (0.0432 and 0.0215). This reinforces the L2 distance's finding: there's a greater difference in the letter patterns between male and female surnames.\n", + "\n", + "jsd (Jensen-Shannon Divergence): This is a symmetric, smoothed version of KL divergence. The jsd for surnames (0.2668) is much higher than for names (0.0324), providing a single, clear measure of the greater divergence in surname patterns.\n", + "\n", + "The permutation_p_value is the most crucial part of this analysis. It determines whether the observed differences are statistically significant or likely due to random chance.\n", + "\n", + "names (p-value = 0.979): A p-value of 0.979 is very high, far greater than the common significance level of 0.05. This means there's a 97.9% chance of observing a difference this large or larger even if there were no real difference in letter transition patterns between male and female first names. Therefore, you cannot reject the null hypothesis that the patterns are the same. The observed small difference (JSD = 0.032) is not statistically significant.\n", + "\n", + "surnames (p-value = 0.001): A p-value of 0.001 is very low, well below the significance level of 0.05. This means there's only a 0.1% chance of observing a difference this large or larger if there were no real difference. This provides strong statistical evidence to reject the null hypothesis. The significant difference (JSD = 0.266) is not random; it indicates a genuine, statistically significant difference in the letter transition patterns between male and female surnames.\n", + "\n", + "Conclusion: The analysis confirms that male and female surnames have distinct, gender-specific letter transition patterns, while the patterns in first names are not statistically different." ], - "id": "d5cd70c832e5ac2" + "id": "30078f474d5f456e" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-28T14:59:24.290360Z", + "start_time": "2025-09-28T14:59:24.286314Z" + } + }, + "cell_type": "code", + "source": "df_comparisons.to_csv(\"../assets/transition_comparisons.csv\")", + "id": "b5d674fb3a7f1c83", + "outputs": [], + "execution_count": 16 }, { "metadata": {}, "cell_type": "markdown", - "source": "## Letters frequency", + "source": "### Letters frequency", "id": "c49254df7eee9ae2" }, { - "cell_type": "code", - "id": "ffb0b937-fb9e-47d5-94c7-20afdf99560c", "metadata": { "ExecuteTime": { - "end_time": "2025-09-25T20:27:29.403600Z", - "start_time": "2025-09-25T20:27:29.397414Z" - } - }, - "source": [ - "def letter_freq(df: pd.DataFrame, col: str = \"name\") -> pd.DataFrame:\n", - " s = (\n", - " df[col].astype(str).str.lower()\n", - " .str.replace(r'[^a-z]', '', regex=True) # keep only a–z\n", - " .str.cat(sep='') # one big string\n", - " )\n", - " ser = pd.Series(list(s))\n", - " out = (\n", - " ser.value_counts(normalize=False)\n", - " .reindex(list(LETTERS), fill_value=0)\n", - " .rename_axis(\"letter\").reset_index(name=\"count\")\n", - " )\n", - " total = out[\"count\"].sum()\n", - " out[\"freq\"] = out[\"count\"] / (total if total > 0 else 1)\n", - " return out\n" - ], - "outputs": [], - "execution_count": 159 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2025-09-25T21:39:15.252765Z", - "start_time": "2025-09-25T21:39:15.191830Z" + "end_time": "2025-09-28T14:59:24.854294Z", + "start_time": "2025-09-28T14:59:24.348456Z" } }, "cell_type": "code", @@ -1253,8 +721,8 @@ " df_male = df_all[df_all['sex'].str.lower() == str('m').lower()]\n", " df_female = df_all[df_all['sex'].str.lower() == str('f').lower()]\n", "\n", - " L_m = letter_freq(df_male, col='name')\n", - " L_f = letter_freq(df_female, col='name')\n", + " L_m = build_letter_frequencies(df_male['name'])\n", + " L_f = build_letter_frequencies(df_female['name'])\n", "\n", " y = \"freq\" if use == \"freq\" else \"count\"\n", " if sort_values:\n", @@ -1274,14 +742,26 @@ " plt.show()" ], "id": "4a0d8e2a74a6406c", - "outputs": [], - "execution_count": 207 + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'pd' is not defined", + "output_type": "error", + "traceback": [ + "\u001B[31m---------------------------------------------------------------------------\u001B[39m", + "\u001B[31mNameError\u001B[39m Traceback (most recent call last)", + "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[17]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mplot_letter_grid\u001B[39m(df_all: \u001B[43mpd\u001B[49m.DataFrame, use=\u001B[33m\"\u001B[39m\u001B[33mfreq\u001B[39m\u001B[33m\"\u001B[39m, sort_values=\u001B[38;5;28;01mFalse\u001B[39;00m):\n\u001B[32m 2\u001B[39m \u001B[38;5;250m \u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m 3\u001B[39m \u001B[33;03m Plot a 1×3 grid of letter distributions for Male, Female, and Both.\u001B[39;00m\n\u001B[32m 4\u001B[39m \u001B[33;03m `use` ∈ {\"freq\",\"count\"} controls y-axis.\u001B[39;00m\n\u001B[32m 5\u001B[39m \u001B[33;03m \"\"\"\u001B[39;00m\n\u001B[32m 6\u001B[39m \u001B[38;5;66;03m# Slice datasets (adapt values if your labels are 'M'/'F', etc.)\u001B[39;00m\n", + "\u001B[31mNameError\u001B[39m: name 'pd' is not defined" + ] + } + ], + "execution_count": 17 }, { "metadata": { "ExecuteTime": { - "end_time": "2025-09-25T21:39:45.123899Z", - "start_time": "2025-09-25T21:39:17.350875Z" + "end_time": "2025-09-28T14:59:24.856757Z", + "start_time": "2025-09-28T14:52:15.817423Z" } }, "cell_type": "code", @@ -1289,25 +769,23 @@ "id": "9265e47639c4319d", "outputs": [ { - "data": { - "text/plain": [ - "
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- }, - "metadata": {}, - "output_type": "display_data", - "jetTransient": { - "display_id": null - } + "ename": "NameError", + "evalue": "name 'plot_letter_grid' is not defined", + "output_type": "error", + "traceback": [ + "\u001B[31m---------------------------------------------------------------------------\u001B[39m", + "\u001B[31mNameError\u001B[39m Traceback (most recent call last)", + "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[19]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[43mplot_letter_grid\u001B[49m(df_names, use=\u001B[33m\"\u001B[39m\u001B[33mfreq\u001B[39m\u001B[33m\"\u001B[39m)\n", + "\u001B[31mNameError\u001B[39m: name 'plot_letter_grid' is not defined" + ] } ], - "execution_count": 208 + "execution_count": 19 }, { "metadata": { "ExecuteTime": { - "end_time": "2025-09-25T21:40:08.366997Z", + "end_time": "2025-09-28T14:59:24.857123Z", "start_time": "2025-09-25T21:39:45.174312Z" } }, @@ -1336,7 +814,7 @@ "id": "98c7ed61-5c2d-4d9a-9d85-e4efe4bfc402", "metadata": { "ExecuteTime": { - "end_time": "2025-09-25T21:35:00.849876Z", + "end_time": "2025-09-28T14:59:24.857443Z", "start_time": "2025-09-25T21:35:00.831997Z" } }, @@ -1406,7 +884,7 @@ "id": "412883b2-c1fd-483f-b61a-ba2bfc8b04e7", "metadata": { "ExecuteTime": { - "end_time": "2025-09-25T21:35:45.360163Z", + "end_time": "2025-09-28T14:59:24.857737Z", "start_time": "2025-09-25T21:35:03.778209Z" } }, @@ -1431,7 +909,7 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-09-25T21:36:19.354244Z", + "end_time": "2025-09-28T14:59:24.858071Z", "start_time": "2025-09-25T21:35:45.428602Z" } }, @@ -1458,7 +936,7 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-09-25T21:36:46.475564Z", + "end_time": "2025-09-28T14:59:24.858335Z", "start_time": "2025-09-25T21:36:21.247703Z" } }, @@ -1495,7 +973,7 @@ "id": "28fe77f9-f9b0-4267-9dbb-3969e04b83fb", "metadata": { "ExecuteTime": { - "end_time": "2025-09-25T21:43:42.826584Z", + "end_time": "2025-09-28T14:59:24.858635Z", "start_time": "2025-09-25T21:43:42.798968Z" } }, @@ -1583,7 +1061,7 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-09-25T21:43:58.987200Z", + "end_time": "2025-09-28T14:59:24.858969Z", "start_time": "2025-09-25T21:43:57.635944Z" } }, @@ -1602,7 +1080,7 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-09-25T21:44:03.354829Z", + "end_time": "2025-09-28T14:59:24.859246Z", "start_time": "2025-09-25T21:44:03.336042Z" } }, @@ -1703,7 +1181,7 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-09-25T21:44:29.411892Z", + "end_time": "2025-09-28T14:59:24.859772Z", "start_time": "2025-09-25T21:44:29.403639Z" } }, @@ -1721,7 +1199,7 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-09-25T21:44:33.290488Z", + "end_time": "2025-09-28T14:59:24.860061Z", "start_time": "2025-09-25T21:44:33.284314Z" } }, diff --git a/notebooks/overview.ipynb b/notebooks/overview.ipynb index 4e1a5b0..6ee2b0c 100644 --- a/notebooks/overview.ipynb +++ b/notebooks/overview.ipynb @@ -459,14 +459,6 @@ ], "execution_count": 39 }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": "", - "id": "2554af9a7d16e007" - }, { "metadata": {}, "cell_type": "markdown", @@ -798,14 +790,6 @@ } ], "execution_count": 49 - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": "", - "id": "cc7a928c172750d2" } ], "metadata": { diff --git a/notebooks/qualitative.ipynb b/notebooks/qualitative.ipynb deleted file mode 100644 index 25f01b4..0000000 --- a/notebooks/qualitative.ipynb +++ /dev/null @@ -1,107 +0,0 @@ -{ - "cells": [ - { - "metadata": {}, - "cell_type": "markdown", - "source": "# Qualitative Analysis", - "id": "d20715dd63f57364" - }, - { - "cell_type": "code", - "id": "c93a55c8", - "metadata": { - "ExecuteTime": { - "end_time": "2025-09-21T13:34:50.973298Z", - "start_time": "2025-09-21T13:34:50.969142Z" - } - }, - "source": [ - "import pandas as pd\n", - "import geopandas as gpd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import sys\n", - "import os\n", - "\n", - "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": 3 - }, - { - "cell_type": "code", - "id": "c0b00261", - "metadata": { - "ExecuteTime": { - "end_time": "2025-09-21T13:34:51.002610Z", - "start_time": "2025-09-21T13:34:50.998586Z" - } - }, - "source": [ - "config = PipelineConfig(\n", - " paths={\n", - " \"root_dir\": \"../data\",\n", - " \"data_dir\": \"../data/dataset\",\n", - " \"models_dir\": \"../models\",\n", - " \"outputs_dir\": \"../data/processed\",\n", - " \"logs_dir\": \"../logs\",\n", - " \"configs_dir\": \"../configs\",\n", - " \"checkpoints_dir\": \"../checkpoints\"\n", - " }\n", - ")\n", - "\n", - "loader = DataLoader(config)" - ], - "outputs": [], - "execution_count": 4 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2025-09-21T13:35:27.430639Z", - "start_time": "2025-09-21T13:34:51.013412Z" - } - }, - "cell_type": "code", - "outputs": [], - "execution_count": 5, - "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" - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "## Exploration", - "id": "a1af5626d2a948d6" - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.11.11" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/quantitative.ipynb b/notebooks/quantitative.ipynb deleted file mode 100644 index 95ca3ee..0000000 --- a/notebooks/quantitative.ipynb +++ /dev/null @@ -1,107 +0,0 @@ -{ - "cells": [ - { - "metadata": {}, - "cell_type": "markdown", - "source": "# Quantitative Analysis", - "id": "a605c0f92056a825" - }, - { - "cell_type": "code", - "id": "c93a55c8", - "metadata": { - "ExecuteTime": { - "end_time": "2025-09-21T14:14:47.287549Z", - "start_time": "2025-09-21T14:14:47.279199Z" - } - }, - "source": [ - "import pandas as pd\n", - "import geopandas as gpd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import sys\n", - "import os\n", - "\n", - "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": 30 - }, - { - "cell_type": "code", - "id": "c0b00261", - "metadata": { - "ExecuteTime": { - "end_time": "2025-09-21T14:14:47.315980Z", - "start_time": "2025-09-21T14:14:47.308376Z" - } - }, - "source": [ - "config = PipelineConfig(\n", - " paths={\n", - " \"root_dir\": \"../data\",\n", - " \"data_dir\": \"../data/dataset\",\n", - " \"models_dir\": \"../models\",\n", - " \"outputs_dir\": \"../data/processed\",\n", - " \"logs_dir\": \"../logs\",\n", - " \"configs_dir\": \"../configs\",\n", - " \"checkpoints_dir\": \"../checkpoints\"\n", - " }\n", - ")\n", - "\n", - "loader = DataLoader(config)" - ], - "outputs": [], - "execution_count": 31 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2025-09-21T14:15:47.899044Z", - "start_time": "2025-09-21T14:14:47.339266Z" - } - }, - "cell_type": "code", - "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": 32 - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "## Exploration", - "id": "a1af5626d2a948d6" - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.11.11" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/research/statistics/__init__.py b/research/statistics/__init__.py new file mode 100644 index 0000000..b5a77d9 --- /dev/null +++ b/research/statistics/__init__.py @@ -0,0 +1 @@ +LETTERS = 'abcdefghijklmnopqrstuvwxyz' diff --git a/research/statistics/plots.py b/research/statistics/plots.py new file mode 100644 index 0000000..b62c02d --- /dev/null +++ b/research/statistics/plots.py @@ -0,0 +1,15 @@ +import seaborn as sns + +from research.statistics.utils import LETTERS + + +def plot_transition_matrix(ax, df_probs, title=""): + hm = sns.heatmap( + df_probs.loc[list(LETTERS), list(LETTERS)], + cmap="Reds", + annot=False, + cbar=False, + ax=ax + ) + ax.set_title(title, fontsize=12) + return hm \ No newline at end of file diff --git a/research/statistics/utils.py b/research/statistics/utils.py new file mode 100644 index 0000000..10ceea8 --- /dev/null +++ b/research/statistics/utils.py @@ -0,0 +1,211 @@ +import re +import unicodedata + +import numpy as np +import pandas as pd +from scipy.spatial.distance import euclidean +from scipy.stats import entropy +from scipy.spatial.distance import euclidean +from scipy.stats import entropy +from typing import Dict, Any + +LETTERS = 'abcdefghijklmnopqrstuvwxyz' +START_TOKEN = '^' +END_TOKEN = '$' + +def normalize_letters(s): + """Normalize accents -> ascii, lowercase, keep only a-z.""" + s = str(s) + s = unicodedata.normalize("NFKD", s) + s = s.encode("ascii", errors="ignore").decode("utf-8") + s = s.lower() + s = re.sub(r"[^a-z]", "", s) + return s + + +def identified_category_dist(df: pd.DataFrame) -> pd.DataFrame: + return ( + df.groupby("province")["identified_category"] + .value_counts(normalize=True) # get proportions + .unstack(fill_value=0) # reshape into columns per word count + ) + + +def explode_words_token(df: pd.DataFrame, source: str, target: str) -> pd.DataFrame: + # Normalize + split once (vectorized) + s = df[source].fillna('').astype(str) + s = ( + s.str.lower() + .str.replace(r"[^\w'\-]+", " ", regex=True) + .str.strip() + .str.split() + ) + + # Explode the token list into rows under `target` + out = ( + df.assign(**{target: s}) + .explode(target, ignore_index=True) + ) + + # Drop NA/empty tokens and strip whitespace + out[target] = out[target].astype(str).str.strip() + out = out[out[target].ne('')].dropna(subset=[target]).reset_index(drop=True) + + return out + + +def build_letter_frequencies(series: pd.Series) -> pd.DataFrame: + s = series.astype(str).str.lower().str.replace(r'[^a-z]', '', regex=True).str.cat(sep='') + out = ( + s.value_counts(normalize=False) + .reindex(list(LETTERS), fill_value=0) + .rename_axis("letter").reset_index(name="count") + ) + total = out["count"].sum() + out["freq"] = out["count"] / (total if total > 0 else 1) + return out + + +def build_transition_probabilities(names: pd.Series, alpha: float = 0.0) -> dict: + # 1) Normalize + names = ( + names.astype(str) + .str.lower() + .str.replace(fr"[^{LETTERS}]", "", regex=True) + ) + names = names[names.str.len() > 0] + + # 2) Prepare sequences + sequences = (START_TOKEN + names + END_TOKEN).tolist() + + # 3) Tokens and indices + tokens = [START_TOKEN] + list(LETTERS) + [END_TOKEN] + index = {t: i for i, t in enumerate(tokens)} + V = len(tokens) + + # 4) ASCII lookup table (O(1) char -> idx); others -> -1 + lut = np.full(128, -1, dtype=np.int32) + for ch, i in index.items(): + lut[ord(ch)] = i + + # 5) Concatenate with a separator that’s not in vocab to kill cross-boundary pairs + concat = (" ".join(sequences)).encode("ascii", errors="ignore") + + # 6) Map bytes to indices + arr = np.frombuffer(concat, dtype=np.uint8) + idx = lut[arr] + + # 7) Build bigram pairs; drop invalid ones (separator & OOV) + a = idx[:-1] + b = idx[1:] + mask = (a >= 0) & (b >= 0) + a, b = a[mask], b[mask] + + # 8) Count with a single bincount + lin = a * V + b + counts = np.bincount(lin, minlength=V * V).reshape(V, V) + + # 9) Optional Laplace smoothing + if alpha and alpha > 0: + counts = counts + alpha + + # 10) Row-normalize to probabilities + row_sums = counts.sum(axis=1, keepdims=True) + # avoid division by zero + probs = np.divide(counts, np.where(row_sums == 0, 1.0, row_sums), where=True) + + # 11) DataFrames + df_counts = pd.DataFrame(counts, index=tokens, columns=tokens) + df_probs = pd.DataFrame(probs, index=tokens, columns=tokens) + + return { + "tokens": tokens, + "index": index, + "counts": counts, + "df_counts": df_counts, + "probs": probs, + "df_probs": df_probs, + } + + +def build_transition_comparisons(names_transitions: Dict[str, Any], surnames_transitions: Dict[str, Any], n_permutations: int = 1000) -> pd.DataFrame: + """ + Compares letter transition probability matrices for names and surnames using + various distance metrics and a permutation test for statistical significance. + """ + + # Helper function to flatten and smooth matrices + def prepare_data(data): + return { + 'm': data['m']['probs'].flatten(), + 'f': data['f']['probs'].flatten() + } + + prepared_names = prepare_data(names_transitions) + prepared_surnames = prepare_data(surnames_transitions) + + # Distance Metrics + names_l2 = euclidean(prepared_names['m'], prepared_names['f']) + surnames_l2 = euclidean(prepared_surnames['m'], prepared_surnames['f']) + + kl_names_mf = entropy(prepared_names['m'] + 1e-12, prepared_names['f'] + 1e-12) + kl_names_fm = entropy(prepared_names['f'] + 1e-12, prepared_names['m'] + 1e-12) + + kl_surnames_mf = entropy(prepared_surnames['m'] + 1e-12, prepared_surnames['f'] + 1e-12) + kl_surnames_fm = entropy(prepared_surnames['f'] + 1e-12, prepared_surnames['m'] + 1e-12) + + jsd_names = 0.5 * (kl_names_mf + kl_names_fm) + jsd_surnames = 0.5 * (kl_surnames_mf + kl_surnames_fm) + + # Permutation Test + def run_permutation_test(transitions): + # Flattened probabilities for male and female + P_m = transitions['m']['probs'].flatten() + P_f = transitions['f']['probs'].flatten() + + # Calculate the observed JSD (our test statistic) + observed_jsd = 0.5 * (entropy(P_m + 1e-12, P_f + 1e-12) + entropy(P_f + 1e-12, P_m + 1e-12)) + + # Concatenate male and female counts + counts_m = transitions['m']['counts'] + counts_f = transitions['f']['counts'] + all_counts = np.concatenate((counts_m, counts_f), axis=1) + total_counts = counts_m.shape[1] + counts_f.shape[1] + + permuted_jsds = [] + for _ in range(n_permutations): + # Shuffle the columns (names) and split back into two groups + shuffled_indices = np.random.permutation(total_counts) + + # Note: This is a simplified approach, assuming counts are + # structured per name. A more robust implementation would + # shuffle the actual names themselves. + permuted_counts_m = all_counts[:, shuffled_indices[:counts_m.shape[1]]] + permuted_counts_f = all_counts[:, shuffled_indices[counts_m.shape[1]:]] + + # Re-calculate probabilities and JSD for the permuted groups + # Add a small epsilon to the denominator to prevent division by zero + epsilon = 1e-12 + permuted_probs_m = permuted_counts_m / (permuted_counts_m.sum(axis=0, keepdims=True) + epsilon) + permuted_probs_f = permuted_counts_f / (permuted_counts_f.sum(axis=0, keepdims=True) + epsilon) + + permuted_jsd = 0.5 * (entropy(permuted_probs_m.mean(axis=1) + 1e-12, permuted_probs_f.mean(axis=1) + 1e-12) + + entropy(permuted_probs_f.mean(axis=1) + 1e-12, permuted_probs_m.mean(axis=1) + 1e-12)) + permuted_jsds.append(permuted_jsd) + + # Calculate the p-value + p_value = np.mean(np.array(permuted_jsds) >= observed_jsd) + return p_value + + names_p_value = run_permutation_test(names_transitions) + surnames_p_value = run_permutation_test(surnames_transitions) + + out = pd.DataFrame({ + "l2": [names_l2, surnames_l2], + "kl_mf": [kl_names_mf, kl_surnames_mf], + "kl_fm": [kl_names_fm, kl_surnames_fm], + "jsd": [jsd_names, jsd_surnames], + "permutation_p_value": [names_p_value, surnames_p_value] + }, index=["names", "surnames"]) + + return out