drc-ners-nlp/notebooks/names.ipynb

{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Names Analysis & Modeling",
   "id": "ae0a818eafd9bd24"
  },
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T21:41:26.227960Z",
     "start_time": "2025-09-28T21:41:26.224407Z"
    }
   },
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sys \n",
    "import os\n",
    "from collections import Counter"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T21:41:26.471906Z",
     "start_time": "2025-09-28T21:41:26.234143Z"
    }
   },
   "cell_type": "code",
   "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 build_category_distribution\n",
    "from research.statistics.utils import build_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_ngrams_count\n",
    "from research.statistics.plots import plot_transition_matrix\n",
    "from research.statistics.plots import plot_letter_frequencies"
   ],
   "id": "584a6fcfcbea71e4",
   "outputs": [],
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "id": "f1a69290-a9c0-40d0-9fe8-a06d8a466671",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T21:41:26.481898Z",
     "start_time": "2025-09-28T21:41:26.478191Z"
    }
   },
   "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": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T21:41:53.362337Z",
     "start_time": "2025-09-28T21:41:26.490902Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df = loader.load_csv_complete(config.paths.data_dir / \"names_featured.csv\")\n",
    "df['province'] = RegionMapper.clean_province(df['province'])\n",
    "df.columns"
   ],
   "id": "e48c6fd9a213bcd2",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'sex', 'region', 'words', 'length', 'probable_native',\n",
       "       'probable_surname', 'identified_name', 'identified_surname',\n",
       "       'ner_entities', 'ner_tagged', 'annotated', 'identified_category',\n",
       "       'province'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Name category distribution",
   "id": "46429fc98b67a89f"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T21:42:00.759309Z",
     "start_time": "2025-09-28T21:42:00.246322Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df_name_categories = build_category_distribution(df)\n",
    "df_name_categories.head(12)\n",
    "\n",
    "# save data\n",
    "df_name_categories.to_csv(\"../assets/identified_category_distribution.csv\", index=False)"
   ],
   "id": "378147d2abc9ab24",
   "outputs": [],
   "execution_count": 5
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Simple vs Compose (all provinces)",
   "id": "3c99d846cb37c469"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T21:42:04.015038Z",
     "start_time": "2025-09-28T21:42:00.766470Z"
    }
   },
   "cell_type": "code",
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(10, 10))\n",
    "\n",
    "# Pie 1: Simple vs Compose\n",
    "labels = [\"Simple\", \"Compose\"]\n",
    "values = [\n",
    "    len(df.query(\"identified_category == 'simple'\")),\n",
    "    len(df.query(\"identified_category == 'compose'\"))\n",
    "]\n",
    "axes[0].pie(values, labels=labels, autopct=\"%1.1f%%\", startangle=90)\n",
    "axes[0].set_title(\"Distribution by Category\")\n",
    "\n",
    "# Pie 2: Male vs Female\n",
    "labels = [\"Male\", \"Female\"]\n",
    "values = [\n",
    "    len(df.query(\"sex == 'm'\")),\n",
    "    len(df.query(\"sex == 'f'\"))\n",
    "]\n",
    "axes[1].pie(values, labels=labels, autopct=\"%1.1f%%\", startangle=90)\n",
    "axes[1].set_title(\"Distribution by Sex\")\n",
    "\n",
    "for ax in axes.flat:\n",
    "    ax.set_aspect(\"equal\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save figures\n",
    "plt.savefig(\"../assets/distribution_grid.png\")\n",
    "plt.savefig(\"../assets/distribution_grid.svg\")\n",
    "\n",
    "plt.show()"
   ],
   "id": "ae30e79a975010d4",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Simple vs Compose by Province",
   "id": "d31d8ae890cdfece"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T21:42:04.514885Z",
     "start_time": "2025-09-28T21:42:04.038820Z"
    }
   },
   "cell_type": "code",
   "source": [
    "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",
    "    width=0.85\n",
    ")\n",
    "\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",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Native Names vs Surnames",
   "id": "1973b08d18c2258e"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T21:42:05.285962Z",
     "start_time": "2025-09-28T21:42:04.523383Z"
    }
   },
   "cell_type": "code",
   "source": "df_base = df.query(\"identified_category == 'simple'\")",
   "id": "1e7dde234bb3504f",
   "outputs": [],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T21:42:36.611043Z",
     "start_time": "2025-09-28T21:42:05.308895Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df_names = build_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": {
      "text/plain": [
       "             count  unique       top     freq\n",
       "name      10015595  644336    ilunga    82342\n",
       "province  10015595      12  KINSHASA  2106077\n",
       "sex       10015595       2         m  6033856"
      ],
      "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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <td>10015595</td>\n",
       "      <td>644336</td>\n",
       "      <td>ilunga</td>\n",
       "      <td>82342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>province</th>\n",
       "      <td>10015595</td>\n",
       "      <td>12</td>\n",
       "      <td>KINSHASA</td>\n",
       "      <td>2106077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>10015595</td>\n",
       "      <td>2</td>\n",
       "      <td>m</td>\n",
       "      <td>6033856</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T21:42:59.059882Z",
     "start_time": "2025-09-28T21:42:37.124804Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df_surnames = build_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": [
       "            count  unique       top     freq\n",
       "name      5007877  253743      jean    89564\n",
       "province  5007877      12  KINSHASA  1053047\n",
       "sex       5007877       2         m  3017009"
      ],
      "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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <td>5007877</td>\n",
       "      <td>253743</td>\n",
       "      <td>jean</td>\n",
       "      <td>89564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>province</th>\n",
       "      <td>5007877</td>\n",
       "      <td>12</td>\n",
       "      <td>KINSHASA</td>\n",
       "      <td>1053047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>5007877</td>\n",
       "      <td>2</td>\n",
       "      <td>m</td>\n",
       "      <td>3017009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T21:43:01.097806Z",
     "start_time": "2025-09-28T21:42:59.860815Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df_names_m = df_names.query(\"sex == 'm'\")\n",
    "df_names_f = df_names.query(\"sex == 'f'\")\n",
    "\n",
    "df_surnames_m = df_surnames.query(\"sex == 'm'\")\n",
    "df_surnames_f = df_surnames.query(\"sex == 'f'\")"
   ],
   "id": "7980efe654809722",
   "outputs": [],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T21:43:06.100459Z",
     "start_time": "2025-09-28T21:43:01.376200Z"
    }
   },
   "cell_type": "code",
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(10, 10))\n",
    "\n",
    "labels = [\"Only Male\", \"Only Female\", \"Shared\"]\n",
    "\n",
    "# Native names\n",
    "names_m = set(df_names_m[\"name\"].str.lower())\n",
    "names_f = set(df_names_f[\"name\"].str.lower())\n",
    "\n",
    "values = [\n",
    "    len(names_m - names_f),\n",
    "    len(names_f - names_m),\n",
    "    len(names_m & names_f)\n",
    "]\n",
    "axes[0].pie(values, labels=labels, autopct=\"%1.1f%%\", startangle=90)\n",
    "axes[0].set_title(\"Native - Distribution by Gender\")\n",
    "\n",
    "# Surnames\n",
    "surnames_m = set(df_surnames_m[\"name\"].str.lower())\n",
    "surnames_f = set(df_surnames_f[\"name\"].str.lower())\n",
    "\n",
    "values = [\n",
    "    len(surnames_m - surnames_f),\n",
    "    len(surnames_f - surnames_m),\n",
    "    len(surnames_m & surnames_f)\n",
    "]\n",
    "axes[1].pie(values, labels=labels, autopct=\"%1.1f%%\", startangle=90)\n",
    "axes[1].set_title(\"Surnames - Distribution by Gender\")\n",
    "\n",
    "# Formatting\n",
    "for ax in axes.flat:\n",
    "    ax.set_aspect(\"equal\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save figures\n",
    "plt.savefig(\"../assets/name_by_gender_distribution_grid.png\")\n",
    "plt.savefig(\"../assets/name_by_gender_distribution_grid.svg\")\n",
    "\n",
    "plt.show()"
   ],
   "id": "9568492106dd2169",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T21:43:30.859924Z",
     "start_time": "2025-09-28T21:43:07.728723Z"
    }
   },
   "cell_type": "code",
   "source": [
    "names_transitions = build_transition_probabilities(df_names['name'])\n",
    "names_transitions_males   = build_transition_probabilities(df_names_m['name'])\n",
    "names_transitions_females = build_transition_probabilities(df_names_f['name'])\n",
    "\n",
    "names_transitions['df_probs'].to_csv(\"../assets/names_transition_probs.csv\", index=False)"
   ],
   "id": "12a7094d94ad519f",
   "outputs": [],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T21:43:44.217712Z",
     "start_time": "2025-09-28T21:43:30.869697Z"
    }
   },
   "cell_type": "code",
   "source": [
    "surnames_transitions = build_transition_probabilities(df_surnames['name'])\n",
    "surnames_transitions_males = build_transition_probabilities(df_surnames_m['name'])\n",
    "surnames_transitions_females = build_transition_probabilities(df_surnames_f['name'])\n",
    "\n",
    "surnames_transitions['df_probs'].to_csv(\"../assets/surnames_transition_probs.csv\", index=False)"
   ],
   "id": "684b467b17955d65",
   "outputs": [],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T21:43:49.373562Z",
     "start_time": "2025-09-28T21:43:45.960221Z"
    }
   },
   "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": [
       "<Figure size 2000x600 with 4 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Transition Probability Differences",
   "id": "1f59136fe68b02a8"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T21:43:50.242298Z",
     "start_time": "2025-09-28T21:43:49.382953Z"
    }
   },
   "cell_type": "code",
   "source": [
    "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",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 6), sharex=True, sharey=True)\n",
    "\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",
    "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",
    "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.savefig(\"../assets/transition_difference.png\")\n",
    "plt.show()"
   ],
   "id": "51ec0792317364fc",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x600 with 3 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T21:43:53.422514Z",
     "start_time": "2025-09-28T21:43:51.881491Z"
    }
   },
   "cell_type": "code",
   "source": [
    "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.to_csv(\"../assets/transition_comparisons.csv\", index_label=\"category\")\n",
    "df_comparisons.head(3)"
   ],
   "id": "dae836cd8a6c26e6",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                l2     kl_mf     kl_fm       jsd  permutation_p_value\n",
       "names     0.318904  0.043201  0.021538  0.032370                0.973\n",
       "surnames  1.277002  0.293619  0.239895  0.266757                0.003"
      ],
      "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>l2</th>\n",
       "      <th>kl_mf</th>\n",
       "      <th>kl_fm</th>\n",
       "      <th>jsd</th>\n",
       "      <th>permutation_p_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>names</th>\n",
       "      <td>0.318904</td>\n",
       "      <td>0.043201</td>\n",
       "      <td>0.021538</td>\n",
       "      <td>0.032370</td>\n",
       "      <td>0.973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surnames</th>\n",
       "      <td>1.277002</td>\n",
       "      <td>0.293619</td>\n",
       "      <td>0.239895</td>\n",
       "      <td>0.266757</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "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": "30078f474d5f456e"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Letters frequency",
   "id": "c49254df7eee9ae2"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T21:44:08.716709Z",
     "start_time": "2025-09-28T21:43:53.482784Z"
    }
   },
   "cell_type": "code",
   "source": "plot_letter_frequencies(df_names_m, df_names_f, title=\"names\")",
   "id": "9265e47639c4319d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T21:44:18.302146Z",
     "start_time": "2025-09-28T21:44:10.480696Z"
    }
   },
   "cell_type": "code",
   "source": "plot_letter_frequencies(df_surnames_m, df_surnames_f, title=\"surnames\")",
   "id": "395dace523f1bed4",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 19
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### N-grams analysis",
   "id": "d3dc057ce0869f93"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### Prefixes and Suffixes",
   "id": "67994bde86f2f6fc"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T21:44:19.998062Z",
     "start_time": "2025-09-28T21:44:19.989365Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import re\n",
    "import pandas as pd\n",
    "\n",
    "def _pick_ngram_col(df: pd.DataFrame) -> str:\n",
    "    \"\"\"Detect the n-gram column name like '2-gram', '3-gram', etc.\"\"\"\n",
    "    for c in df.columns:\n",
    "        if isinstance(c, str) and c.endswith(\"-gram\"):\n",
    "            return c\n",
    "    raise ValueError(f\"Could not find an n-gram column in: {list(df.columns)}\")\n",
    "\n",
    "def build_ngram_summary_table(results: dict, top_k: int = 20, sep: str = \", \") -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    Convert dict of {subset_key: df} into rows of:\n",
    "      sex | 2-grams | 3-grams | 4-grams | position\n",
    "\n",
    "    Expected subset_key pattern: '{sex}_{position}_{n}', e.g. 'male_prefix_3', 'female_suffix_4', 'male_any_2'\n",
    "    \"\"\"\n",
    "    rows = {}\n",
    "\n",
    "    for key, df in results.items():\n",
    "        # Parse key -> sex, position, n\n",
    "        m = re.match(r\"^(male|female)_(prefix|suffix|any)_(\\d+)$\", key)\n",
    "        if not m:\n",
    "            # Skip keys that don't match the expected pattern\n",
    "            continue\n",
    "        sex, position, n_str = m.groups()\n",
    "        n = int(n_str)\n",
    "\n",
    "        if df is None or df.empty:\n",
    "            ng_list_str = \"\"\n",
    "        else:\n",
    "            ng_col = _pick_ngram_col(df)\n",
    "            # take top_k, cast to string, join\n",
    "            ng_list_str = sep.join(df[ng_col].astype(str).head(top_k).tolist())\n",
    "\n",
    "        # group rows by (sex, position)\n",
    "        rows.setdefault((sex, position), {}).update({f\"{n}-grams\": ng_list_str})\n",
    "\n",
    "    # Build final table, ensuring all n columns are present\n",
    "    records = []\n",
    "    for (sex, position), cols in rows.items():\n",
    "        record = {\n",
    "            \"sex\": sex,\n",
    "            \"position\": position,\n",
    "            \"2-grams\": cols.get(\"2-grams\", \"\"),\n",
    "            \"3-grams\": cols.get(\"3-grams\", \"\"),\n",
    "            \"4-grams\": cols.get(\"4-grams\", \"\"),\n",
    "        }\n",
    "        records.append(record)\n",
    "\n",
    "    # Order: sex (male,female), then position (prefix, suffix, any)\n",
    "    order_pos = {\"prefix\": 0, \"suffix\": 1, \"any\": 2}\n",
    "    df_out = pd.DataFrame(records)\n",
    "    if not df_out.empty:\n",
    "        df_out[\"__pos_order\"] = df_out[\"position\"].map(order_pos).fillna(99)\n",
    "        df_out = (\n",
    "            df_out.sort_values([\"sex\", \"__pos_order\"])\n",
    "            .drop(columns=\"__pos_order\")\n",
    "            .reset_index(drop=True)\n",
    "        )\n",
    "    return df_out"
   ],
   "id": "a685260c6ab8213d",
   "outputs": [],
   "execution_count": 20
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T21:46:12.618810Z",
     "start_time": "2025-09-28T21:44:20.007216Z"
    }
   },
   "cell_type": "code",
   "source": [
    "names_ngrams = {\n",
    "    # male\n",
    "    \"male_prefix_2\": build_ngrams_count(df_names_m, 2, \"prefix\"),\n",
    "    \"male_prefix_3\": build_ngrams_count(df_names_m, 3, \"prefix\"),\n",
    "    \"male_prefix_4\": build_ngrams_count(df_names_m, 4, \"prefix\"),\n",
    "    \"male_suffix_2\": build_ngrams_count(df_names_m, 2, \"suffix\"),\n",
    "    \"male_suffix_3\": build_ngrams_count(df_names_m, 3, \"suffix\"),\n",
    "    \"male_suffix_4\": build_ngrams_count(df_names_m, 4, \"suffix\"),\n",
    "    \"male_any_2\": build_ngrams_count(df_names_m, 2, \"any\"),\n",
    "    \"male_any_3\": build_ngrams_count(df_names_m, 3, \"any\"),\n",
    "    \"male_any_4\": build_ngrams_count(df_names_m, 4, \"any\"),\n",
    "\n",
    "    # female\n",
    "    \"female_prefix_2\": build_ngrams_count(df_names_f, 2, \"prefix\"),\n",
    "    \"female_prefix_3\": build_ngrams_count(df_names_f, 3, \"prefix\"),\n",
    "    \"female_prefix_4\": build_ngrams_count(df_names_f, 4, \"prefix\"),\n",
    "    \"female_suffix_2\": build_ngrams_count(df_names_f, 2, \"suffix\"),\n",
    "    \"female_suffix_3\": build_ngrams_count(df_names_f, 3, \"suffix\"),\n",
    "    \"female_suffix_4\": build_ngrams_count(df_names_f, 4, \"suffix\"),\n",
    "    \"female_any_2\": build_ngrams_count(df_names_f, 2, \"any\"),\n",
    "    \"female_any_3\": build_ngrams_count(df_names_f, 3, \"any\"),\n",
    "    \"female_any_4\": build_ngrams_count(df_names_f, 4, \"any\"),\n",
    "}"
   ],
   "id": "3924bde83845366f",
   "outputs": [],
   "execution_count": 21
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T21:46:14.363513Z",
     "start_time": "2025-09-28T21:46:14.334119Z"
    }
   },
   "cell_type": "code",
   "source": [
    "names_ngrams_summary = build_ngram_summary_table(names_ngrams, top_k=10)\n",
    "\n",
    "names_ngrams_summary.to_csv(\"../assets/names_ngrams_summary.csv\", index=False)\n",
    "names_ngrams_summary"
   ],
   "id": "9ceaae3cc9040c31",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      sex position                                 2-grams  \\\n",
       "0  female   prefix  ka, ma, mu, mb, ng, ba, ki, lu, ts, bo   \n",
       "1  female   suffix  ba, ga, la, ka, ma, da, go, ya, bo, na   \n",
       "2  female      any  ng, ka, mb, an, ba, ma, nd, ga, la, am   \n",
       "3    male   prefix  ka, mu, ma, ba, mb, ng, ki, lu, ts, bo   \n",
       "4    male   suffix  ba, ga, la, go, ka, da, bo, le, di, ma   \n",
       "5    male      any  ng, ka, mb, ba, an, ma, mu, nd, am, al   \n",
       "\n",
       "                                            3-grams  \\\n",
       "0  tsh, kab, ngo, mas, kas, kal, muk, kav, mbu, man   \n",
       "1  nga, mba, ngo, nda, ala, mbo, ngu, ndo, mbe, mbu   \n",
       "2  nga, mba, ang, ngo, amb, ong, nda, ala, mbo, eng   \n",
       "3  kab, tsh, kal, kas, muk, ngo, kam, mut, mul, mbu   \n",
       "4  nga, mba, ngo, nda, ala, mbo, ngu, mbe, ndo, ele   \n",
       "5  nga, mba, ngo, amb, ang, ong, ala, nda, shi, mbo   \n",
       "\n",
       "                                             4-grams  \n",
       "0  tshi, kavi, ngoy, kaso, ilun, mbuy, kaba, ntum...  \n",
       "1  anga, amba, ongo, umba, inga, ombo, unga, enga...  \n",
       "2  anga, amba, ongo, tshi, umba, inga, ombo, unga...  \n",
       "3  tshi, ngoy, ilun, kaba, kaso, kamb, muke, kabe...  \n",
       "4  amba, ongo, anga, umba, unga, ombo, anda, enga...  \n",
       "5  amba, ongo, anga, tshi, umba, unga, ombo, anda...  "
      ],
      "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>sex</th>\n",
       "      <th>position</th>\n",
       "      <th>2-grams</th>\n",
       "      <th>3-grams</th>\n",
       "      <th>4-grams</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>prefix</td>\n",
       "      <td>ka, ma, mu, mb, ng, ba, ki, lu, ts, bo</td>\n",
       "      <td>tsh, kab, ngo, mas, kas, kal, muk, kav, mbu, man</td>\n",
       "      <td>tshi, kavi, ngoy, kaso, ilun, mbuy, kaba, ntum...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>suffix</td>\n",
       "      <td>ba, ga, la, ka, ma, da, go, ya, bo, na</td>\n",
       "      <td>nga, mba, ngo, nda, ala, mbo, ngu, ndo, mbe, mbu</td>\n",
       "      <td>anga, amba, ongo, umba, inga, ombo, unga, enga...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>any</td>\n",
       "      <td>ng, ka, mb, an, ba, ma, nd, ga, la, am</td>\n",
       "      <td>nga, mba, ang, ngo, amb, ong, nda, ala, mbo, eng</td>\n",
       "      <td>anga, amba, ongo, tshi, umba, inga, ombo, unga...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>prefix</td>\n",
       "      <td>ka, mu, ma, ba, mb, ng, ki, lu, ts, bo</td>\n",
       "      <td>kab, tsh, kal, kas, muk, ngo, kam, mut, mul, mbu</td>\n",
       "      <td>tshi, ngoy, ilun, kaba, kaso, kamb, muke, kabe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>suffix</td>\n",
       "      <td>ba, ga, la, go, ka, da, bo, le, di, ma</td>\n",
       "      <td>nga, mba, ngo, nda, ala, mbo, ngu, mbe, ndo, ele</td>\n",
       "      <td>amba, ongo, anga, umba, unga, ombo, anda, enga...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>male</td>\n",
       "      <td>any</td>\n",
       "      <td>ng, ka, mb, ba, an, ma, mu, nd, am, al</td>\n",
       "      <td>nga, mba, ngo, amb, ang, ong, ala, nda, shi, mbo</td>\n",
       "      <td>amba, ongo, anga, tshi, umba, unga, ombo, anda...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T21:47:17.247916Z",
     "start_time": "2025-09-28T21:46:14.408394Z"
    }
   },
   "cell_type": "code",
   "source": [
    "surnames_ngrams = {\n",
    "    # male\n",
    "    \"male_prefix_2\": build_ngrams_count(df_surnames_m, 2, \"prefix\"),\n",
    "    \"male_prefix_3\": build_ngrams_count(df_surnames_m, 3, \"prefix\"),\n",
    "    \"male_prefix_4\": build_ngrams_count(df_surnames_m, 4, \"prefix\"),\n",
    "    \"male_suffix_2\": build_ngrams_count(df_surnames_m, 2, \"suffix\"),\n",
    "    \"male_suffix_3\": build_ngrams_count(df_surnames_m, 3, \"suffix\"),\n",
    "    \"male_suffix_4\": build_ngrams_count(df_surnames_m, 4, \"suffix\"),\n",
    "    \"male_any_2\": build_ngrams_count(df_surnames_m, 2, \"any\"),\n",
    "    \"male_any_3\": build_ngrams_count(df_surnames_m, 3, \"any\"),\n",
    "    \"male_any_4\": build_ngrams_count(df_surnames_m, 4, \"any\"),\n",
    "\n",
    "    # female\n",
    "    \"female_prefix_2\": build_ngrams_count(df_surnames_f, 2, \"prefix\"),\n",
    "    \"female_prefix_3\": build_ngrams_count(df_surnames_f, 3, \"prefix\"),\n",
    "    \"female_prefix_4\": build_ngrams_count(df_surnames_f, 4, \"prefix\"),\n",
    "    \"female_suffix_2\": build_ngrams_count(df_surnames_f, 2, \"suffix\"),\n",
    "    \"female_suffix_3\": build_ngrams_count(df_surnames_f, 3, \"suffix\"),\n",
    "    \"female_suffix_4\": build_ngrams_count(df_surnames_f, 4, \"suffix\"),\n",
    "    \"female_any_2\": build_ngrams_count(df_surnames_f, 2, \"any\"),\n",
    "    \"female_any_3\": build_ngrams_count(df_surnames_f, 3, \"any\"),\n",
    "    \"female_any_4\": build_ngrams_count(df_surnames_f, 4, \"any\"),\n",
    "}"
   ],
   "id": "951e8301584e549c",
   "outputs": [],
   "execution_count": 23
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T21:47:19.856774Z",
     "start_time": "2025-09-28T21:47:19.831696Z"
    }
   },
   "cell_type": "code",
   "source": [
    "surnames_ngrams_summary = build_ngram_summary_table(surnames_ngrams, top_k=10)\n",
    "\n",
    "surnames_ngrams_summary.to_csv(\"../assets/surnames_ngrams_summary.csv\", index=False)\n",
    "surnames_ngrams_summary"
   ],
   "id": "f7ee40432896ef54",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      sex position                                 2-grams  \\\n",
       "0  female   prefix  ma, ch, be, na, an, sa, es, jo, ju, me   \n",
       "1  female   suffix  ne, ie, te, le, ce, ia, se, el, ah, th   \n",
       "2  female      any  ne, in, el, an, ie, ri, ra, li, ar, er   \n",
       "3    male   prefix  jo, je, pa, ch, ma, da, al, ju, be, fr   \n",
       "4    male   suffix  in, el, an, ck, on, re, ce, er, se, is   \n",
       "5    male      any  an, er, el, ie, in, ri, is, on, en, re   \n",
       "\n",
       "                                            3-grams  \\\n",
       "0  mar, cha, est, chr, gra, dor, sar, rut, ben, mer   \n",
       "1  ine, tte, lle, rah, nce, ene, nne, her, lie, rie   \n",
       "2  ine, tte, ett, mar, ari, lle, lin, eli, the, ell   \n",
       "3  jea, jos, chr, pat, mar, jon, fra, dan, cha, ben   \n",
       "4  ean, tin, ick, ier, ard, uel, ert, ain, iel, ise   \n",
       "5  ric, sti, jea, ean, tin, ris, ier, ist, ick, jos   \n",
       "\n",
       "                                             4-grams  \n",
       "0  mari, chri, esth, grac, sara, dorc, ruth, rach...  \n",
       "1  ette, line, tine, elle, ther, arie, ille, rcas...  \n",
       "2  ette, line, ther, mari, tine, elle, rist, chri...  \n",
       "3  jean, chri, jose, jona, patr, fran, mich, emma...  \n",
       "4  jean, stin, rick, bert, seph, than, oise, avid...  \n",
       "5  jean, stin, rist, rick, chri, hris, usti, bert...  "
      ],
      "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>sex</th>\n",
       "      <th>position</th>\n",
       "      <th>2-grams</th>\n",
       "      <th>3-grams</th>\n",
       "      <th>4-grams</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>prefix</td>\n",
       "      <td>ma, ch, be, na, an, sa, es, jo, ju, me</td>\n",
       "      <td>mar, cha, est, chr, gra, dor, sar, rut, ben, mer</td>\n",
       "      <td>mari, chri, esth, grac, sara, dorc, ruth, rach...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>suffix</td>\n",
       "      <td>ne, ie, te, le, ce, ia, se, el, ah, th</td>\n",
       "      <td>ine, tte, lle, rah, nce, ene, nne, her, lie, rie</td>\n",
       "      <td>ette, line, tine, elle, ther, arie, ille, rcas...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>any</td>\n",
       "      <td>ne, in, el, an, ie, ri, ra, li, ar, er</td>\n",
       "      <td>ine, tte, ett, mar, ari, lle, lin, eli, the, ell</td>\n",
       "      <td>ette, line, ther, mari, tine, elle, rist, chri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>prefix</td>\n",
       "      <td>jo, je, pa, ch, ma, da, al, ju, be, fr</td>\n",
       "      <td>jea, jos, chr, pat, mar, jon, fra, dan, cha, ben</td>\n",
       "      <td>jean, chri, jose, jona, patr, fran, mich, emma...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>suffix</td>\n",
       "      <td>in, el, an, ck, on, re, ce, er, se, is</td>\n",
       "      <td>ean, tin, ick, ier, ard, uel, ert, ain, iel, ise</td>\n",
       "      <td>jean, stin, rick, bert, seph, than, oise, avid...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>male</td>\n",
       "      <td>any</td>\n",
       "      <td>an, er, el, ie, in, ri, is, on, en, re</td>\n",
       "      <td>ric, sti, jea, ean, tin, ris, ier, ist, ick, jos</td>\n",
       "      <td>jean, stin, rist, rick, chri, hris, usti, bert...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 24
  },
  {
   "cell_type": "markdown",
   "id": "28e65639-9082-464d-9330-71e2a028c878",
   "metadata": {},
   "source": [
    "## Name generation"
   ]
  },
  {
   "cell_type": "code",
   "id": "28fe77f9-f9b0-4267-9dbb-3969e04b83fb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T21:47:19.912431Z",
     "start_time": "2025-09-28T21:47:19.901973Z"
    }
   },
   "source": [
    "# tokens must include '^' (start) and '$' (end)\n",
    "tokens = [\"^\"] + list(LETTERS) + [\"$\"]\n",
    "token_to_idx = {t: i for i, t in enumerate(tokens)}\n",
    "idx_to_token = np.array(tokens)\n",
    "V = len(tokens)\n",
    "\n",
    "# Prepare a well-formed probability matrix (rows sum to 1, no NaNs/negatives)\n",
    "def prepare_prob_matrix(df_probs, tokens):\n",
    "    import pandas as pd, numpy as np\n",
    "    if isinstance(df_probs, pd.DataFrame):\n",
    "        P = df_probs.loc[tokens, tokens].to_numpy(dtype=float, copy=True)\n",
    "    else:\n",
    "        P = np.array(df_probs, dtype=float, copy=True)\n",
    "        if P.shape[0] != len(tokens) or P.shape[1] != len(tokens):\n",
    "            raise ValueError(\n",
    "                f\"Matrix shape {P.shape} does not match tokens length {len(tokens)}. \"\n",
    "                \"Use the same tokens used to build the matrix (e.g., transitions['tokens']).\"\n",
    "            )\n",
    "    # clean & renormalize\n",
    "    P[~np.isfinite(P) | (P < 0)] = 0.0\n",
    "    rs = P.sum(axis=1, keepdims=True)\n",
    "    P = np.divide(P, np.where(rs == 0, 1.0, rs), out=np.zeros_like(P), where=True)\n",
    "    return P\n",
    "\n",
    "\n",
    "def generate_name(P, token_to_idx, idx_to_token, *,\n",
    "                  target_len=None,   # exact character length (letters only), if provided\n",
    "                  min_len=1,         # minimum character length\n",
    "                  max_len=12,        # hard cap on steps\n",
    "                  temperature=1.0):\n",
    "    start = token_to_idx['^']\n",
    "    end   = token_to_idx['$']\n",
    "    cur = start\n",
    "    out = []\n",
    "\n",
    "    for _ in range(max_len):\n",
    "        row = P[cur]\n",
    "\n",
    "        # Temperature scaling (τ<1 = sharper, τ>1 = flatter)\n",
    "        if temperature != 1.0:\n",
    "            row = np.power(row, 1.0 / temperature)\n",
    "            s = row.sum()\n",
    "            row = row / s if s > 0 else row\n",
    "\n",
    "        row_mod = row.copy()\n",
    "\n",
    "        # 1) Prevent early stop before min_len\n",
    "        if len(out) < min_len:\n",
    "            row_mod[end] = 0.0\n",
    "\n",
    "        # 2) If target_len reached or exceeded, force end\n",
    "        if target_len is not None and len(out) >= target_len:\n",
    "            row_mod[:] = 0.0\n",
    "            row_mod[end] = 1.0\n",
    "\n",
    "        s = row_mod.sum()\n",
    "        if s == 0.0:\n",
    "            # Fallback: uniform over valid next tokens\n",
    "            candidates = np.arange(V)\n",
    "            # exclude '^'\n",
    "            candidates = candidates[candidates != start]\n",
    "            # exclude '$' if below min_len\n",
    "            if len(out) < min_len:\n",
    "                candidates = candidates[candidates != end]\n",
    "            probs = np.ones(len(candidates)) / len(candidates)\n",
    "            nxt_idx = np.random.choice(candidates, p=probs)\n",
    "        else:\n",
    "            row_mod = row_mod / s\n",
    "            nxt_idx = np.random.choice(V, p=row_mod)\n",
    "\n",
    "        if nxt_idx == end:\n",
    "            break\n",
    "        out.append(idx_to_token[nxt_idx])\n",
    "        cur = nxt_idx\n",
    "\n",
    "    return \"\".join(out).capitalize()"
   ],
   "outputs": [],
   "execution_count": 25
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T21:48:59.032408Z",
     "start_time": "2025-09-28T21:48:59.020841Z"
    }
   },
   "cell_type": "code",
   "source": [
    "P = prepare_prob_matrix(names_transitions_males['df_probs'], tokens)\n",
    "generated_var = [generate_name(P, token_to_idx, idx_to_token, min_len=5, max_len=12, temperature=0.5) for _ in range(10)]\n",
    "\n",
    "names = pd.DataFrame(generated_var, columns=[\"name\"])\n",
    "names"
   ],
   "id": "a01eb547985d5a62",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        name\n",
       "0      Monga\n",
       "1  Ngonganga\n",
       "2      Kanda\n",
       "3  Mbombonda\n",
       "4     Mbanga\n",
       "5     Mbanda\n",
       "6      Manga\n",
       "7      Bambi\n",
       "8      Mbale\n",
       "9      Mamba"
      ],
      "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>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Monga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ngonganga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kanda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mbombonda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mbanga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mbanda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Manga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bambi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mbale</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Mamba</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 33
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T21:48:52.059908Z",
     "start_time": "2025-09-28T21:48:52.049436Z"
    }
   },
   "cell_type": "code",
   "source": [
    "P = prepare_prob_matrix(names_transitions_females['df_probs'], tokens)\n",
    "generated_var = [generate_name(P, token_to_idx, idx_to_token, min_len=5, max_len=12, temperature=0.5) for _ in range(10)]\n",
    "\n",
    "names = pd.DataFrame(generated_var, columns=[\"name\"])\n",
    "names"
   ],
   "id": "5d5ec37da189b7a8",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "          name\n",
       "0        Mamba\n",
       "1        Kanga\n",
       "2  Mbokangango\n",
       "3      Belango\n",
       "4       Mbanga\n",
       "5        Mungo\n",
       "6        Langa\n",
       "7        Kambo\n",
       "8        Konga\n",
       "9        Mbulu"
      ],
      "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>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mamba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kanga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mbokangango</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Belango</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mbanga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mungo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Langa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Kambo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Konga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Mbulu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 32
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T21:49:58.339225Z",
     "start_time": "2025-09-28T21:49:58.329362Z"
    }
   },
   "cell_type": "code",
   "source": [
    "P = prepare_prob_matrix(surnames_transitions_females['df_probs'], tokens)\n",
    "generated_var = [generate_name(P, token_to_idx, idx_to_token, min_len=5, max_len=12, temperature=0.5) for _ in range(10)]\n",
    "\n",
    "names = pd.DataFrame(generated_var, columns=[\"name\"])\n",
    "names"
   ],
   "id": "746028258a8d8dfe",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       name\n",
       "0    Jomane\n",
       "1  Maielane\n",
       "2     Miela\n",
       "3   Canella\n",
       "4    Thelie\n",
       "5     Siele\n",
       "6   Mielice\n",
       "7     Juege\n",
       "8    Anella\n",
       "9    Sesane"
      ],
      "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>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jomane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Maielane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Miela</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Canella</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thelie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Siele</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mielice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Juege</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Anella</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sesane</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 35
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T21:49:30.676439Z",
     "start_time": "2025-09-28T21:49:30.664938Z"
    }
   },
   "cell_type": "code",
   "source": [
    "P = prepare_prob_matrix(surnames_transitions_males['df_probs'], tokens)\n",
    "generated_var = [generate_name(P, token_to_idx, idx_to_token, min_len=5, max_len=12, temperature=0.5) for _ in\n",
    "                 range(10)]\n",
    "\n",
    "names = pd.DataFrame(generated_var, columns=[\"name\"])\n",
    "names"
   ],
   "id": "7227ac855ee3688f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "           name\n",
       "0         Jaman\n",
       "1       Chistis\n",
       "2       Mannane\n",
       "3       Celerie\n",
       "4        Jeusus\n",
       "5        Drinie\n",
       "6        Jerien\n",
       "7  Seristinelel\n",
       "8   Distugueran\n",
       "9       Alanche"
      ],
      "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>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jaman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chistis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mannane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Celerie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jeusus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Drinie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jerien</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Seristinelel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Distugueran</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Alanche</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 34
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "a96630f0d2a9195b"
  }
 ],
 "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}