2374 lines
503 KiB
Plaintext
Vendored
2374 lines
503 KiB
Plaintext
Vendored
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "b6cc3c6b-85c7-4a04-9aef-8ffc055aa93c",
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"metadata": {},
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"source": "# Names Analysis & Modeling"
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},
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{
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"cell_type": "code",
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"id": "initial_id",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-09-25T19:54:31.700090Z",
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"start_time": "2025-09-25T19:54:31.689969Z"
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}
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},
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"source": [
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"import pandas as pd \n",
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"import unicodedata \n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"import numpy as np \n",
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"import re \n",
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"import sys \n",
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"import os\n",
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"from collections import Counter\n",
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"\n",
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"from rich.jupyter import display\n",
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"\n",
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"sys.path.append(os.path.abspath(\"..\")) \n",
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"from collections import Counter \n",
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"from core.utils.data_loader import DataLoader\n",
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"from core.utils.region_mapper import RegionMapper\n",
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"from core.config.pipeline_config import PipelineConfig"
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],
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"outputs": [],
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"execution_count": 116
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-09-25T18:26:50.162866Z",
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"start_time": "2025-09-25T18:26:50.159601Z"
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}
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},
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"cell_type": "code",
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"source": "LETTERS = 'abcdefghijklmnopqrstuvwxyz'",
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"id": "16647cc71aea7594",
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"outputs": [],
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"execution_count": 47
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},
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{
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"cell_type": "code",
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"id": "f1a69290-a9c0-40d0-9fe8-a06d8a466671",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-09-25T17:39:49.310278Z",
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"start_time": "2025-09-25T17:39:49.304876Z"
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}
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},
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"source": [
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"config = PipelineConfig(\n",
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" paths={\n",
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" \"root_dir\": \"../data\",\n",
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" \"data_dir\": \"../data/dataset\",\n",
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" \"models_dir\": \"../models\",\n",
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" \"outputs_dir\": \"../data/processed\",\n",
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" \"logs_dir\": \"../logs\",\n",
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" \"configs_dir\": \"../configs\",\n",
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" \"checkpoints_dir\": \"../checkpoints\"\n",
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" }\n",
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")\n",
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"\n",
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"loader = DataLoader(config)\n",
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"\n",
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"def normalize_letters(s):\n",
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" \"\"\"Normalize accents -> ascii, lowercase, keep only a-z.\"\"\"\n",
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" s = str(s)\n",
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" s = unicodedata.normalize(\"NFKD\", s)\n",
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" s = s.encode(\"ascii\", errors=\"ignore\").decode(\"utf-8\")\n",
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" s = s.lower()\n",
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" s = re.sub(r\"[^a-z]\", \"\", s)\n",
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" return s"
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],
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"outputs": [],
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"execution_count": 3
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},
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{
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"metadata": {
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"ExecuteTime": {
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||
"end_time": "2025-09-25T17:59:34.598256Z",
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||
"start_time": "2025-09-25T17:58:54.210735Z"
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}
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||
},
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"cell_type": "code",
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"source": [
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"df = loader.load_csv_complete(config.paths.data_dir / \"names_featured.csv\")\n",
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"df['province'] = RegionMapper.clean_province(df['province'])"
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],
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"id": "e48c6fd9a213bcd2",
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"outputs": [],
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"execution_count": 23
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-09-25T17:59:38.255948Z",
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"start_time": "2025-09-25T17:59:38.249016Z"
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}
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},
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"cell_type": "code",
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"source": "df.columns",
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"id": "2715f291947f5158",
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Index(['name', 'sex', 'region', 'words', 'length', 'probable_native',\n",
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" 'probable_surname', 'identified_name', 'identified_surname',\n",
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" 'ner_entities', 'ner_tagged', 'annotated', 'identified_category',\n",
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" 'province'],\n",
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" dtype='object')"
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]
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},
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"execution_count": 24,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"execution_count": 24
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},
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{
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||
"metadata": {
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"ExecuteTime": {
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||
"end_time": "2025-09-25T18:01:22.242283Z",
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||
"start_time": "2025-09-25T18:01:21.580597Z"
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}
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},
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"cell_type": "code",
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"source": "df.describe().T",
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"id": "93f8859e3e9c4350",
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"outputs": [
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{
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"data": {
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"text/plain": [
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" count mean std min 25% 50% 75% max\n",
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"words 6467942.0 2.869578 0.46841 1.0 3.0 3.0 3.0 11.0\n",
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"length 6467942.0 20.141236 3.796574 0.0 18.0 21.0 23.0 60.0\n",
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"ner_tagged 5018124.0 0.997939 0.045348 0.0 1.0 1.0 1.0 1.0\n",
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"annotated 5018124.0 0.997939 0.045348 0.0 1.0 1.0 1.0 1.0"
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],
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>count</th>\n",
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||
" <th>mean</th>\n",
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" <th>std</th>\n",
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" <th>min</th>\n",
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" <th>25%</th>\n",
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" <th>50%</th>\n",
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" <th>75%</th>\n",
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" <th>max</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>words</th>\n",
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||
" <td>6467942.0</td>\n",
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||
" <td>2.869578</td>\n",
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||
" <td>0.46841</td>\n",
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||
" <td>1.0</td>\n",
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||
" <td>3.0</td>\n",
|
||
" <td>3.0</td>\n",
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||
" <td>3.0</td>\n",
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||
" <td>11.0</td>\n",
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||
" </tr>\n",
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||
" <tr>\n",
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||
" <th>length</th>\n",
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||
" <td>6467942.0</td>\n",
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" <td>20.141236</td>\n",
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" <td>3.796574</td>\n",
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" <td>0.0</td>\n",
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" <td>18.0</td>\n",
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" <td>21.0</td>\n",
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" <td>23.0</td>\n",
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" <td>60.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>ner_tagged</th>\n",
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" <td>5018124.0</td>\n",
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" <td>0.997939</td>\n",
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" <td>0.045348</td>\n",
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" <td>0.0</td>\n",
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" <td>1.0</td>\n",
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||
" <td>1.0</td>\n",
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||
" <td>1.0</td>\n",
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" <td>1.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>annotated</th>\n",
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" <td>5018124.0</td>\n",
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||
" <td>0.997939</td>\n",
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||
" <td>0.045348</td>\n",
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||
" <td>0.0</td>\n",
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||
" <td>1.0</td>\n",
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" <td>1.0</td>\n",
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" <td>1.0</td>\n",
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" <td>1.0</td>\n",
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||
" </tr>\n",
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||
" </tbody>\n",
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||
"</table>\n",
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"</div>"
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]
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},
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||
"execution_count": 28,
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||
"metadata": {},
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||
"output_type": "execute_result"
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}
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],
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"execution_count": 28
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||
},
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||
{
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||
"metadata": {
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||
"ExecuteTime": {
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"end_time": "2025-09-25T18:02:53.214472Z",
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"start_time": "2025-09-25T18:02:52.427559Z"
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}
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},
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"cell_type": "code",
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"source": [
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"# Group by province and compute counts\n",
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"word_stats = (\n",
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" df.groupby(\"province\")[\"identified_category\"]\n",
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" .value_counts(normalize=True) # get proportions\n",
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" .unstack(fill_value=0) # reshape into columns per word count\n",
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||
")\n",
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"\n",
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||
"display(word_stats.head(12))"
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],
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||
"id": "378147d2abc9ab24",
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"outputs": [
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{
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||
"data": {
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||
"text/plain": [
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||
"identified_category compose simple\n",
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"province \n",
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||
"AUTRES 0.206217 0.793783\n",
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||
"BANDUNDU 0.626906 0.373094\n",
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||
"BAS-CONGO 0.090813 0.909187\n",
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||
"EQUATEUR 0.124238 0.875762\n",
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||
"KASAI-OCCIDENTAL 0.261266 0.738734\n",
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||
"KASAI-ORIENTAL 0.076224 0.923776\n",
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||
"KATANGA 0.180624 0.819376\n",
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||
"KINSHASA 0.076792 0.923208\n",
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||
"MANIEMA 0.461150 0.538850\n",
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||
"NORD-KIVU 0.119626 0.880374\n",
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||
"ORIENTALE 0.160905 0.839095\n",
|
||
"SUD-KIVU 0.409647 0.590353"
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||
],
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||
"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
|
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" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
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||
" <th>identified_category</th>\n",
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" <th>compose</th>\n",
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||
" <th>simple</th>\n",
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||
" </tr>\n",
|
||
" <tr>\n",
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||
" <th>province</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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||
" </thead>\n",
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" <tbody>\n",
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||
" <tr>\n",
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||
" <th>AUTRES</th>\n",
|
||
" <td>0.206217</td>\n",
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||
" <td>0.793783</td>\n",
|
||
" </tr>\n",
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||
" <tr>\n",
|
||
" <th>BANDUNDU</th>\n",
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||
" <td>0.626906</td>\n",
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||
" <td>0.373094</td>\n",
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||
" </tr>\n",
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||
" <tr>\n",
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||
" <th>BAS-CONGO</th>\n",
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||
" <td>0.090813</td>\n",
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||
" <td>0.909187</td>\n",
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||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>EQUATEUR</th>\n",
|
||
" <td>0.124238</td>\n",
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" <td>0.875762</td>\n",
|
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" </tr>\n",
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" <tr>\n",
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||
" <th>KASAI-OCCIDENTAL</th>\n",
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||
" <td>0.261266</td>\n",
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||
" <td>0.738734</td>\n",
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||
" </tr>\n",
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||
" <tr>\n",
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" <th>KASAI-ORIENTAL</th>\n",
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||
" <td>0.076224</td>\n",
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||
" <td>0.923776</td>\n",
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||
" </tr>\n",
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||
" <tr>\n",
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||
" <th>KATANGA</th>\n",
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||
" <td>0.180624</td>\n",
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||
" <td>0.819376</td>\n",
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||
" </tr>\n",
|
||
" <tr>\n",
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||
" <th>KINSHASA</th>\n",
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||
" <td>0.076792</td>\n",
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||
" <td>0.923208</td>\n",
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||
" </tr>\n",
|
||
" <tr>\n",
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||
" <th>MANIEMA</th>\n",
|
||
" <td>0.461150</td>\n",
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||
" <td>0.538850</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>NORD-KIVU</th>\n",
|
||
" <td>0.119626</td>\n",
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||
" <td>0.880374</td>\n",
|
||
" </tr>\n",
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||
" <tr>\n",
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||
" <th>ORIENTALE</th>\n",
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||
" <td>0.160905</td>\n",
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||
" <td>0.839095</td>\n",
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||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>SUD-KIVU</th>\n",
|
||
" <td>0.409647</td>\n",
|
||
" <td>0.590353</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data",
|
||
"jetTransient": {
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"display_id": null
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}
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}
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],
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"execution_count": 30
|
||
},
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||
{
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"metadata": {
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||
"ExecuteTime": {
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"end_time": "2025-09-25T18:05:12.951347Z",
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"start_time": "2025-09-25T18:05:12.736698Z"
|
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}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"ax = word_stats.plot(\n",
|
||
" kind=\"bar\",\n",
|
||
" stacked=True,\n",
|
||
" figsize=(12, 6),\n",
|
||
" colormap=\"tab20c\"\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",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
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||
],
|
||
"id": "6d5c1abb55b7076a",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
],
|
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"
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data",
|
||
"jetTransient": {
|
||
"display_id": null
|
||
}
|
||
}
|
||
],
|
||
"execution_count": 33
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "24a4ad40319f441b",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-25T18:07:02.981141Z",
|
||
"start_time": "2025-09-25T18:06:14.115049Z"
|
||
}
|
||
},
|
||
"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)"
|
||
],
|
||
"outputs": [],
|
||
"execution_count": 34
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-25T18:01:37.658949Z",
|
||
"start_time": "2025-09-25T18:01:33.224026Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": "df_names.describe().T",
|
||
"id": "5ce61cb4109c1cee",
|
||
"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": 29,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"execution_count": 29
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "77abf83a2f6360f5",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-25T17:48:50.735911Z",
|
||
"start_time": "2025-09-25T17:48:49.779413Z"
|
||
}
|
||
},
|
||
"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))"
|
||
],
|
||
"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"
|
||
],
|
||
"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>province</th>\n",
|
||
" <th>count_original</th>\n",
|
||
" <th>count_names</th>\n",
|
||
" <th>augmentation</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>KINSHASA</td>\n",
|
||
" <td>1140620</td>\n",
|
||
" <td>2106077</td>\n",
|
||
" <td>84.64</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>AUTRES</td>\n",
|
||
" <td>1035751</td>\n",
|
||
" <td>1644326</td>\n",
|
||
" <td>58.76</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>KATANGA</td>\n",
|
||
" <td>836220</td>\n",
|
||
" <td>1370359</td>\n",
|
||
" <td>63.88</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>BANDUNDU</td>\n",
|
||
" <td>809949</td>\n",
|
||
" <td>604374</td>\n",
|
||
" <td>-25.38</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>KASAI-ORIENTAL</td>\n",
|
||
" <td>434497</td>\n",
|
||
" <td>802757</td>\n",
|
||
" <td>84.76</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>NORD-KIVU</td>\n",
|
||
" <td>394999</td>\n",
|
||
" <td>695494</td>\n",
|
||
" <td>76.07</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>KASAI-OCCIDENTAL</td>\n",
|
||
" <td>367626</td>\n",
|
||
" <td>543156</td>\n",
|
||
" <td>47.75</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>EQUATEUR</td>\n",
|
||
" <td>356404</td>\n",
|
||
" <td>624252</td>\n",
|
||
" <td>75.15</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>SUD-KIVU</td>\n",
|
||
" <td>346152</td>\n",
|
||
" <td>408708</td>\n",
|
||
" <td>18.07</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>ORIENTALE</td>\n",
|
||
" <td>322756</td>\n",
|
||
" <td>541646</td>\n",
|
||
" <td>67.82</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>BAS-CONGO</td>\n",
|
||
" <td>295155</td>\n",
|
||
" <td>536702</td>\n",
|
||
" <td>81.84</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>MANIEMA</td>\n",
|
||
" <td>127813</td>\n",
|
||
" <td>137744</td>\n",
|
||
" <td>7.77</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data",
|
||
"jetTransient": {
|
||
"display_id": null
|
||
}
|
||
}
|
||
],
|
||
"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
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-25T19:59:41.506588Z",
|
||
"start_time": "2025-09-25T19:59:03.640342Z"
|
||
}
|
||
},
|
||
"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",
|
||
"\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
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-25T19:20:21.846303Z",
|
||
"start_time": "2025-09-25T19:20:21.826623Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": "P_both",
|
||
"id": "e18d66243a2e84ce",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([[0.00000000e+00, 3.24013508e-02, 9.26023186e-02, 8.07567504e-03,\n",
|
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" 1.78577669e-02, 2.02887868e-02, 8.45799629e-03, 5.69657652e-03,\n",
|
||
" 3.69820166e-03, 2.68854009e-02, 3.07494515e-03, 1.88787613e-01,\n",
|
||
" 6.02969225e-02, 2.67433060e-01, 1.19960803e-01, 1.46038926e-02,\n",
|
||
" 1.25087642e-02, 9.48564064e-06, 5.46732357e-03, 3.25790818e-02,\n",
|
||
" 4.21499933e-02, 4.79364338e-03, 5.06603104e-03, 1.06228192e-02,\n",
|
||
" 8.28745445e-06, 1.07286590e-02, 5.94460106e-03, 0.00000000e+00],\n",
|
||
" [0.00000000e+00, 9.79188615e-04, 4.85819282e-02, 2.02181039e-03,\n",
|
||
" 2.09905376e-02, 1.13126896e-03, 6.78761374e-03, 6.18601611e-03,\n",
|
||
" 1.18322753e-02, 4.85108866e-03, 3.21792803e-03, 4.38667298e-02,\n",
|
||
" 7.63473398e-02, 8.29327106e-02, 1.35175537e-01, 1.80842017e-03,\n",
|
||
" 1.14416792e-02, 6.45832751e-06, 1.04753187e-02, 4.64873953e-02,\n",
|
||
" 3.37630746e-02, 4.56718766e-03, 1.12748243e-02, 8.25896230e-03,\n",
|
||
" 1.62785241e-05, 3.24132841e-02, 1.16528576e-02, 3.82932286e-01],\n",
|
||
" [0.00000000e+00, 3.94208050e-01, 1.26062555e-04, 2.54671827e-07,\n",
|
||
" 6.20889915e-04, 1.16581886e-01, 1.27335914e-06, 5.78105048e-05,\n",
|
||
" 1.60163112e-03, 1.14119974e-01, 6.36679568e-06, 1.42616223e-05,\n",
|
||
" 3.71820868e-04, 2.21564490e-05, 3.82007741e-05, 1.91314061e-01,\n",
|
||
" 6.36679568e-06, 2.54671827e-07, 1.33906447e-03, 1.88457152e-05,\n",
|
||
" 9.42285761e-06, 1.43654266e-01, 1.22242477e-05, 2.73840976e-02,\n",
|
||
" 2.54671827e-07, 4.38137412e-03, 1.32429350e-05, 4.09588700e-03],\n",
|
||
" [0.00000000e+00, 7.01929373e-02, 3.05958231e-05, 4.77906756e-03,\n",
|
||
" 1.83574938e-05, 1.01235459e-01, 0.00000000e+00, 7.95491400e-05,\n",
|
||
" 3.31719914e-01, 3.23593663e-01, 1.83574938e-05, 3.78347948e-02,\n",
|
||
" 1.57140147e-02, 8.56683046e-05, 4.28341523e-05, 3.82264213e-02,\n",
|
||
" 2.44766584e-05, 8.97069532e-03, 3.20644226e-03, 8.56683046e-05,\n",
|
||
" 1.32235147e-02, 1.80454164e-02, 6.11916461e-06, 3.76940540e-03,\n",
|
||
" 1.83574938e-05, 8.32818304e-03, 1.22383292e-05, 2.07378489e-02],\n",
|
||
" [0.00000000e+00, 2.42481024e-01, 1.59597766e-05, 5.91102838e-07,\n",
|
||
" 6.72675030e-04, 1.00302467e-01, 9.45764541e-06, 4.07860958e-05,\n",
|
||
" 4.88723827e-03, 3.10415882e-01, 6.58571316e-02, 1.59597766e-05,\n",
|
||
" 2.60085249e-05, 1.21767185e-04, 7.15234434e-05, 1.40599130e-01,\n",
|
||
" 1.77330851e-06, 5.91102838e-07, 9.87496402e-03, 9.39853513e-05,\n",
|
||
" 1.47775710e-05, 9.32293308e-02, 3.01462448e-05, 8.75600634e-03,\n",
|
||
" 5.91102838e-07, 4.08629392e-03, 2.26924380e-03, 1.61246943e-02],\n",
|
||
" [0.00000000e+00, 6.20521666e-03, 2.89410812e-02, 1.14746975e-03,\n",
|
||
" 1.42575234e-02, 3.73036745e-03, 4.63992522e-03, 5.86618568e-03,\n",
|
||
" 3.56149357e-03, 1.63971914e-03, 3.86254077e-03, 5.41582112e-02,\n",
|
||
" 1.01515014e-01, 9.48049725e-02, 1.71123389e-01, 2.71532767e-03,\n",
|
||
" 8.68032252e-03, 5.64623900e-06, 3.42218546e-02, 3.32745697e-02,\n",
|
||
" 3.46981918e-02, 2.61574854e-03, 2.96196565e-03, 5.42783221e-03,\n",
|
||
" 2.82055303e-04, 2.88689634e-02, 1.29332237e-02, 3.37861189e-01],\n",
|
||
" [0.00000000e+00, 2.30773207e-01, 3.23098645e-06, 6.46197290e-06,\n",
|
||
" 3.55408510e-05, 8.83093217e-02, 1.52825659e-03, 3.87718374e-05,\n",
|
||
" 1.32470445e-04, 1.43985680e-01, 6.46197290e-06, 9.69295936e-05,\n",
|
||
" 3.84810486e-03, 3.55408510e-05, 1.93859187e-05, 8.54951325e-02,\n",
|
||
" 6.46197290e-06, 6.46197290e-06, 1.35184473e-02, 2.00321160e-04,\n",
|
||
" 5.04033887e-04, 3.97640734e-01, 0.00000000e+00, 2.65296298e-02,\n",
|
||
" 0.00000000e+00, 4.81416981e-03, 6.46197290e-06, 2.45878069e-03],\n",
|
||
" [0.00000000e+00, 3.84041201e-01, 2.13788468e-02, 5.90704090e-06,\n",
|
||
" 4.26150808e-05, 9.36232228e-02, 5.48510940e-06, 4.51466697e-05,\n",
|
||
" 1.54637892e-02, 7.89180664e-02, 1.05482873e-05, 1.43456708e-05,\n",
|
||
" 8.78039436e-04, 2.91132730e-05, 5.13490627e-04, 2.51988458e-01,\n",
|
||
" 7.17283538e-06, 8.43862985e-07, 1.54637892e-03, 2.32062321e-05,\n",
|
||
" 2.36281636e-05, 1.18102844e-01, 5.48510940e-06, 2.03531313e-02,\n",
|
||
" 2.53158896e-06, 2.25100451e-03, 6.75090388e-06, 1.07187476e-02],\n",
|
||
" [0.00000000e+00, 2.61423040e-01, 4.45304937e-05, 3.87221684e-06,\n",
|
||
" 2.51694095e-05, 9.37415295e-02, 2.51694095e-05, 3.48499516e-05,\n",
|
||
" 2.42013553e-05, 4.19773475e-01, 1.16166505e-05, 9.48693127e-05,\n",
|
||
" 7.45401742e-05, 2.46853824e-04, 6.03097773e-04, 1.15829622e-01,\n",
|
||
" 2.32333011e-05, 9.68054211e-07, 2.96708616e-03, 2.09099710e-04,\n",
|
||
" 2.07163601e-04, 7.81529526e-02, 1.16166505e-05, 7.23910939e-03,\n",
|
||
" 0.00000000e+00, 3.30493708e-03, 1.06485963e-05, 1.59167473e-02],\n",
|
||
" [0.00000000e+00, 5.75254317e-02, 4.19969803e-02, 3.39123813e-03,\n",
|
||
" 1.60229517e-02, 2.87569967e-02, 7.80762981e-03, 6.44852009e-03,\n",
|
||
" 5.03957227e-03, 3.53360355e-04, 2.39100365e-03, 5.83105862e-02,\n",
|
||
" 8.42198661e-02, 7.33745628e-02, 1.08158907e-01, 1.16997001e-02,\n",
|
||
" 9.22495206e-03, 4.54262098e-04, 3.30700356e-02, 5.04862075e-02,\n",
|
||
" 4.80878507e-02, 2.13139613e-03, 5.38803052e-03, 5.74179938e-03,\n",
|
||
" 9.10975250e-05, 1.78020087e-02, 1.32783834e-02, 3.08746670e-01],\n",
|
||
" [0.00000000e+00, 2.24793956e-01, 3.15239740e-05, 4.20319653e-05,\n",
|
||
" 2.41683801e-04, 1.02046606e-01, 1.05079913e-05, 2.10159827e-05,\n",
|
||
" 2.34678473e-04, 4.28078053e-01, 9.45719219e-05, 8.75665944e-05,\n",
|
||
" 3.50266378e-05, 7.32056729e-04, 1.26095896e-04, 1.26747391e-01,\n",
|
||
" 2.80213102e-05, 0.00000000e+00, 3.50266378e-05, 1.05079913e-05,\n",
|
||
" 7.35559393e-05, 6.34962889e-02, 7.00532755e-06, 1.98601036e-03,\n",
|
||
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" 7.86578661e-07, 1.03498020e-02, 1.02255226e-05, 2.63372755e-03],\n",
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" 6.44255799e-07, 4.12613627e-03, 2.09383135e-05, 1.19158331e-02],\n",
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||
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||
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||
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||
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||
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||
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||
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||
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||
" 9.14138458e-03, 7.10082466e-02, 6.97993223e-06, 9.78772630e-03,\n",
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||
" 1.39598645e-06, 3.76497544e-03, 8.37591868e-06, 1.04926995e-02],\n",
|
||
" [0.00000000e+00, 2.22707152e-01, 1.49959178e-05, 4.28716627e-03,\n",
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||
" 6.85368983e-04, 1.37875245e-01, 7.38687802e-05, 6.10944798e-06,\n",
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||
" 2.33114319e-02, 8.05514055e-02, 6.66485235e-06, 9.71957634e-05,\n",
|
||
" 2.22161745e-05, 6.83147365e-05, 4.66539664e-05, 1.44580642e-01,\n",
|
||
" 6.10944798e-06, 1.66621309e-06, 3.28466140e-03, 1.91457881e-01,\n",
|
||
" 3.36630584e-03, 1.64937323e-01, 8.33106543e-06, 1.11314142e-02,\n",
|
||
" 4.44323490e-06, 2.24494443e-03, 1.94391527e-05, 9.20305028e-03],\n",
|
||
" [0.00000000e+00, 5.22268427e-02, 3.07403981e-02, 1.42048426e-03,\n",
|
||
" 1.63779766e-02, 2.25253741e-02, 6.43072121e-03, 1.27090934e-02,\n",
|
||
" 1.31690965e-02, 9.17323132e-03, 7.25477034e-03, 9.02338353e-02,\n",
|
||
" 9.35486776e-02, 1.10365738e-01, 1.23813155e-01, 1.19810014e-03,\n",
|
||
" 7.63632510e-03, 1.59170639e-06, 1.47464776e-02, 5.06219479e-02,\n",
|
||
" 5.95875752e-02, 4.96612394e-04, 5.72923346e-03, 7.16608956e-03,\n",
|
||
" 7.64019068e-05, 3.06126069e-02, 1.88671780e-02, 2.13270465e-01],\n",
|
||
" [0.00000000e+00, 1.85400602e-01, 2.02656831e-05, 0.00000000e+00,\n",
|
||
" 1.68880693e-05, 8.22144987e-02, 3.37761385e-06, 4.05313662e-05,\n",
|
||
" 1.82391148e-04, 2.60012092e-01, 3.37761385e-06, 3.37761385e-06,\n",
|
||
" 9.11955740e-05, 6.75522770e-06, 4.39089801e-05, 5.67067589e-02,\n",
|
||
" 0.00000000e+00, 3.37761385e-06, 5.84327196e-04, 1.01328416e-05,\n",
|
||
" 1.68880693e-05, 3.65248407e-01, 2.36432970e-05, 1.16426349e-02,\n",
|
||
" 0.00000000e+00, 1.43818798e-02, 3.37761385e-05, 2.33089132e-02],\n",
|
||
" [0.00000000e+00, 5.74384483e-01, 2.79704006e-05, 0.00000000e+00,\n",
|
||
" 2.01386884e-05, 2.55957136e-01, 2.23763205e-06, 1.56634243e-05,\n",
|
||
" 1.47683715e-04, 9.64452977e-02, 4.47526410e-06, 2.12575045e-05,\n",
|
||
" 3.91585608e-05, 6.60101454e-05, 7.94359377e-05, 3.73102768e-02,\n",
|
||
" 1.34257923e-05, 5.59408012e-06, 2.57327686e-05, 3.13268487e-05,\n",
|
||
" 3.69209288e-05, 2.98142094e-02, 7.83171217e-06, 5.25843531e-05,\n",
|
||
" 2.23763205e-06, 1.34257923e-04, 3.46832967e-05, 5.31997019e-03],\n",
|
||
" [0.00000000e+00, 2.52709146e-01, 1.30039012e-03, 8.66926745e-03,\n",
|
||
" 0.00000000e+00, 4.59471175e-02, 0.00000000e+00, 0.00000000e+00,\n",
|
||
" 3.46770698e-03, 2.49241439e-01, 0.00000000e+00, 0.00000000e+00,\n",
|
||
" 8.66926745e-04, 0.00000000e+00, 4.33463372e-04, 3.03424361e-02,\n",
|
||
" 2.60078023e-03, 0.00000000e+00, 0.00000000e+00, 2.60078023e-03,\n",
|
||
" 7.36887733e-03, 1.43042913e-02, 0.00000000e+00, 1.30039012e-03,\n",
|
||
" 9.10273082e-03, 3.90117035e-03, 0.00000000e+00, 3.65843086e-01],\n",
|
||
" [0.00000000e+00, 3.76521746e-01, 2.51256281e-04, 2.29570471e-04,\n",
|
||
" 4.61384302e-04, 1.33664603e-01, 3.51459679e-05, 9.49688921e-05,\n",
|
||
" 8.00131610e-05, 2.04184614e-01, 6.73007897e-06, 3.70154343e-04,\n",
|
||
" 1.13289663e-03, 1.04690117e-03, 7.94149318e-04, 1.08618988e-01,\n",
|
||
" 3.69406557e-04, 7.47786552e-07, 4.12030390e-04, 7.15631730e-04,\n",
|
||
" 2.73689878e-04, 5.13063831e-02, 6.64034458e-04, 1.75655061e-03,\n",
|
||
" 3.73893276e-06, 3.06592486e-05, 1.05437904e-04, 1.16868569e-01],\n",
|
||
" [0.00000000e+00, 4.20307825e-01, 1.39054123e-04, 5.15015270e-06,\n",
|
||
" 3.34759926e-04, 1.17668114e-01, 3.86261453e-06, 1.15878436e-05,\n",
|
||
" 1.27466279e-04, 2.31387348e-01, 6.30893706e-05, 1.00427978e-04,\n",
|
||
" 3.09009162e-05, 9.65653632e-05, 1.66092425e-04, 1.13824812e-01,\n",
|
||
" 3.86261453e-06, 0.00000000e+00, 2.06006108e-04, 7.33896760e-05,\n",
|
||
" 3.34759926e-05, 9.47087331e-02, 2.57507635e-04, 5.94585129e-03,\n",
|
||
" 0.00000000e+00, 2.51584959e-03, 2.60082711e-04, 1.17281852e-02],\n",
|
||
" [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
|
||
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
|
||
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
|
||
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
|
||
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
|
||
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
|
||
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]])"
|
||
]
|
||
},
|
||
"execution_count": 85,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"execution_count": 85
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-25T19:20:36.015007Z",
|
||
"start_time": "2025-09-25T19:20:36.006734Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": "P_male",
|
||
"id": "3cad91be032ba586",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([[0.00000000e+00, 2.96327947e-02, 9.45715253e-02, 8.33096048e-03,\n",
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||
" 1.81781176e-02, 2.09494693e-02, 5.91876912e-03, 6.34754052e-03,\n",
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||
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||
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||
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" 3.25922121e-03, 5.50337540e-05, 3.05743078e-05, 9.80028862e-02,\n",
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" 1.22297231e-05, 6.11486156e-06, 1.63755993e-02, 4.89188925e-05,\n",
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||
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||
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||
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||
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||
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||
" 5.57730705e-06, 8.41615634e-03, 1.14334795e-04, 3.87343975e-02],\n",
|
||
" [0.00000000e+00, 2.31631941e-01, 3.90448383e-05, 1.77497835e-03,\n",
|
||
" 4.76347027e-05, 1.29882655e-01, 5.46627736e-06, 1.24943483e-05,\n",
|
||
" 2.64154730e-01, 1.30285597e-01, 1.95224192e-05, 5.05240208e-04,\n",
|
||
" 2.81122836e-04, 8.12132637e-04, 9.83929925e-05, 1.16052973e-01,\n",
|
||
" 1.50947345e-03, 9.37076119e-06, 3.06111532e-04, 1.66034270e-02,\n",
|
||
" 9.57926063e-03, 7.13684982e-02, 7.80896766e-06, 8.95063873e-03,\n",
|
||
" 7.80896766e-07, 3.09859837e-03, 8.58986443e-06, 1.29535156e-02],\n",
|
||
" [0.00000000e+00, 2.22413048e-01, 1.46867275e-05, 4.73738754e-03,\n",
|
||
" 8.82121571e-04, 1.43931765e-01, 9.54637288e-05, 7.34336376e-06,\n",
|
||
" 2.02080191e-02, 7.74100690e-02, 7.34336376e-06, 1.14740059e-04,\n",
|
||
" 2.11121708e-05, 5.96648305e-05, 4.31422621e-05, 1.49431945e-01,\n",
|
||
" 5.50752282e-06, 2.75376141e-06, 3.55418806e-03, 1.97188593e-01,\n",
|
||
" 5.86551180e-04, 1.53063238e-01, 4.58960235e-06, 1.22845296e-02,\n",
|
||
" 2.75376141e-06, 2.18924032e-03, 2.11121708e-05, 1.17190906e-02],\n",
|
||
" [0.00000000e+00, 5.35634736e-02, 3.28028743e-02, 1.52987963e-03,\n",
|
||
" 1.70062776e-02, 2.38347288e-02, 6.50862086e-03, 9.64538997e-03,\n",
|
||
" 1.57409581e-02, 9.01383561e-03, 3.95587373e-03, 9.52401657e-02,\n",
|
||
" 9.63311998e-02, 1.12735032e-01, 1.24990650e-01, 8.85428891e-04,\n",
|
||
" 8.03850050e-03, 1.84234060e-06, 1.36027376e-02, 4.91941786e-02,\n",
|
||
" 6.07441803e-02, 5.59703074e-04, 5.30409858e-03, 5.91243945e-03,\n",
|
||
" 8.06945182e-05, 3.03908820e-02, 1.65836447e-02, 2.05802710e-01],\n",
|
||
" [0.00000000e+00, 2.12080469e-01, 3.59494981e-05, 0.00000000e+00,\n",
|
||
" 7.18989963e-06, 7.64070634e-02, 7.18989963e-06, 3.59494981e-05,\n",
|
||
" 1.94127290e-04, 1.97405884e-01, 0.00000000e+00, 7.18989963e-06,\n",
|
||
" 7.90888959e-05, 1.43797993e-05, 5.75191970e-05, 6.47881856e-02,\n",
|
||
" 0.00000000e+00, 7.18989963e-06, 7.62129361e-04, 0.00000000e+00,\n",
|
||
" 2.87595985e-05, 3.85536798e-01, 2.15696989e-05, 1.39915447e-02,\n",
|
||
" 0.00000000e+00, 1.35313911e-02, 3.59494981e-05, 3.49644819e-02],\n",
|
||
" [0.00000000e+00, 5.66106778e-01, 2.64068733e-05, 0.00000000e+00,\n",
|
||
" 1.69758471e-05, 2.68518291e-01, 3.77241048e-06, 1.50896419e-05,\n",
|
||
" 1.60327445e-04, 9.63794290e-02, 5.65861571e-06, 2.26344629e-05,\n",
|
||
" 3.96103100e-05, 7.35620043e-05, 8.86516462e-05, 3.75203946e-02,\n",
|
||
" 1.50896419e-05, 3.77241048e-06, 3.58378995e-05, 3.77241048e-05,\n",
|
||
" 4.33827205e-05, 2.45678232e-02, 7.54482095e-06, 6.79033886e-05,\n",
|
||
" 3.77241048e-06, 1.37692982e-04, 3.96103100e-05, 6.06226363e-03],\n",
|
||
" [0.00000000e+00, 2.45483528e-01, 5.31349628e-04, 7.97024442e-03,\n",
|
||
" 0.00000000e+00, 4.72901169e-02, 0.00000000e+00, 0.00000000e+00,\n",
|
||
" 3.18809777e-03, 2.58767269e-01, 0.00000000e+00, 0.00000000e+00,\n",
|
||
" 5.31349628e-04, 0.00000000e+00, 0.00000000e+00, 2.60361318e-02,\n",
|
||
" 2.12539851e-03, 0.00000000e+00, 0.00000000e+00, 3.18809777e-03,\n",
|
||
" 9.03294368e-03, 1.27523911e-02, 0.00000000e+00, 1.59404888e-03,\n",
|
||
" 1.11583422e-02, 3.18809777e-03, 0.00000000e+00, 3.67162593e-01],\n",
|
||
" [0.00000000e+00, 3.60629461e-01, 2.15365459e-04, 1.67093891e-04,\n",
|
||
" 1.53478833e-04, 1.33551339e-01, 2.22791854e-05, 9.15922067e-05,\n",
|
||
" 7.79771490e-05, 2.13031096e-01, 6.18866262e-06, 3.50278304e-04,\n",
|
||
" 9.86472821e-04, 1.29714368e-03, 5.34700450e-04, 1.05187461e-01,\n",
|
||
" 3.35425514e-04, 1.23773252e-06, 3.98549873e-04, 4.83953417e-04,\n",
|
||
" 2.82203015e-04, 5.35603995e-02, 4.28255453e-04, 1.64494652e-03,\n",
|
||
" 2.47546505e-06, 2.84678480e-05, 9.53054043e-05, 1.26436853e-01],\n",
|
||
" [0.00000000e+00, 4.31452786e-01, 1.79085500e-04, 6.71570625e-06,\n",
|
||
" 7.83499062e-05, 1.23560041e-01, 4.47713750e-06, 1.56699812e-05,\n",
|
||
" 1.29836987e-04, 2.17718719e-01, 1.34314125e-05, 1.14167006e-04,\n",
|
||
" 3.13399625e-05, 1.09689869e-04, 1.81324069e-04, 1.12608962e-01,\n",
|
||
" 4.47713750e-06, 0.00000000e+00, 2.48481131e-04, 2.01471187e-05,\n",
|
||
" 3.58171000e-05, 9.18551915e-02, 1.11928437e-05, 6.35529668e-03,\n",
|
||
" 0.00000000e+00, 2.32587293e-03, 3.02206781e-04, 1.26367206e-02],\n",
|
||
" [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
|
||
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
|
||
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
|
||
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
|
||
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
|
||
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
|
||
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]])"
|
||
]
|
||
},
|
||
"execution_count": 86,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"execution_count": 86
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-25T19:20:46.999357Z",
|
||
"start_time": "2025-09-25T19:20:46.990660Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": "P_female",
|
||
"id": "78142f56e835ff4d",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([[0.00000000e+00, 3.65966617e-02, 8.96182961e-02, 7.68883017e-03,\n",
|
||
" 1.73723258e-02, 1.92876266e-02, 1.23057951e-02, 4.71014311e-03,\n",
|
||
" 3.06709329e-03, 2.40773856e-02, 2.20287220e-03, 1.82218544e-01,\n",
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||
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||
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||
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||
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||
" 7.72973148e-06, 3.09679545e-02, 1.09965369e-02, 3.95928773e-01],\n",
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||
" [0.00000000e+00, 3.79649312e-01, 6.00891368e-05, 6.82831100e-07,\n",
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" 3.04542670e-04, 1.11340391e-01, 2.04849330e-06, 6.35032923e-05,\n",
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" 1.52271335e-03, 1.28805162e-01, 7.51114210e-06, 1.29737909e-05,\n",
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" 3.48243861e-04, 1.91192708e-05, 2.79960751e-05, 1.90705167e-01,\n",
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" 6.14547990e-06, 6.82831100e-07, 8.17348826e-04, 1.84364397e-05,\n",
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" 4.09698660e-06, 1.51742824e-01, 1.02424665e-05, 2.67348860e-02,\n",
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" 0.00000000e+00, 3.35133504e-03, 1.09252976e-05, 4.43362233e-03],\n",
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" [0.00000000e+00, 8.68306351e-02, 6.22665006e-05, 1.12702366e-02,\n",
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" 4.66998755e-05, 1.31117684e-01, 0.00000000e+00, 4.66998755e-05,\n",
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" 3.19800747e-01, 3.20703611e-01, 3.11332503e-05, 1.42123288e-02,\n",
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" 1.91625156e-02, 7.78331258e-05, 4.66998755e-05, 3.08219178e-02,\n",
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" 3.11332503e-05, 7.90784558e-03, 2.11706102e-03, 7.78331258e-05,\n",
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" 1.85087173e-02, 1.25622665e-02, 1.55666252e-05, 2.73972603e-03,\n",
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" 0.00000000e+00, 8.26587796e-03, 1.55666252e-05, 1.35273973e-02],\n",
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" 4.08380330e-04, 1.03564033e-01, 1.21904576e-05, 3.19999512e-05,\n",
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" 5.23732535e-03, 3.11245240e-01, 6.02040987e-02, 1.21904576e-05,\n",
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" 1.21904576e-05, 2.74285296e-05, 6.24760953e-05, 1.47696537e-01,\n",
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" 3.04761440e-06, 0.00000000e+00, 7.81560714e-03, 7.92379745e-05,\n",
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" 0.00000000e+00, 3.27313787e-03, 2.16075861e-03, 1.20319817e-02],\n",
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" [0.00000000e+00, 5.16772302e-03, 2.82653521e-02, 1.43478612e-03,\n",
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" 1.54585575e-02, 6.89182815e-03, 4.39882987e-03, 5.27203532e-03,\n",
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" 4.33191254e-03, 2.30077499e-03, 2.84595436e-03, 5.26849263e-02,\n",
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" 1.03019874e-01, 9.56150128e-02, 1.67815523e-01, 2.03507386e-03,\n",
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" 7.20214083e-03, 3.28026090e-06, 2.77490390e-02, 3.76915098e-02,\n",
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" 3.78909497e-02, 1.98718205e-03, 3.58138885e-03, 5.86051412e-03,\n",
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" 9.57836182e-05, 2.86117477e-02, 1.65823749e-02, 3.35205925e-01],\n",
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" [0.00000000e+00, 2.59181870e-01, 6.85086355e-06, 0.00000000e+00,\n",
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" 3.42543178e-05, 8.70402214e-02, 1.10298903e-03, 4.79560449e-05,\n",
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" 1.23315544e-04, 1.45361623e-01, 6.85086355e-06, 6.85086355e-05,\n",
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" 4.50786822e-03, 1.37017271e-05, 6.85086355e-06, 7.14819103e-02,\n",
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" 0.00000000e+00, 4.35714922e-03, 6.85086355e-06, 1.39757616e-03],\n",
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" 8.38853691e-04, 3.06230813e-05, 8.12605335e-04, 2.34414219e-01,\n",
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" 2.18736295e-06, 2.45750227e-03, 7.65577032e-06, 1.18839429e-02],\n",
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" 2.40810085e-05, 3.83172191e-01, 9.63240341e-06, 9.15078323e-05,\n",
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" 9.63240341e-06, 0.00000000e+00, 3.12089870e-03, 1.99872371e-04,\n",
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" 4.55930056e-02, 1.85515004e-03, 6.53997221e-03, 6.53255951e-03,\n",
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" 1.82846435e-05, 1.86646675e-02, 1.43489975e-02, 2.90028050e-01],\n",
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" 2.68161219e-05, 6.88280461e-04, 1.96651560e-04, 1.01624163e-01,\n",
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" 2.68161219e-05, 0.00000000e+00, 1.78774146e-05, 8.93870728e-06,\n",
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" 0.00000000e+00, 2.59222511e-04, 8.93870728e-06, 6.06401902e-02],\n",
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" 8.54809715e-07, 5.03397441e-03, 2.05154332e-05, 8.09333838e-03],\n",
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" 6.03339262e-05, 2.89578320e-02, 4.35238152e-03, 6.57296432e-05,\n",
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" 6.67106826e-05, 3.00198072e-04, 3.89472662e-04, 5.28785169e-02,\n",
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" 4.90519725e-07, 6.74464622e-04, 6.91632813e-05, 2.88867066e-03],\n",
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" 1.81853155e-01, 3.63949719e-02, 6.35105084e-04, 3.78360356e-01,\n",
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" 7.97378629e-05, 5.11357516e-02, 7.03092104e-03, 4.05735157e-02,\n",
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" 3.46556607e-03, 3.68379601e-05, 2.70129497e-03, 1.36874005e-02,\n",
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" 4.19672963e-05, 7.92715596e-06, 2.70455909e-04, 3.91153854e-02,\n",
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" 3.59184100e-02, 1.10816977e-02, 1.28466557e-03, 6.02930157e-04,\n",
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" 0.00000000e+00, 4.30775644e-02, 6.88977103e-02, 4.88685850e-03],\n",
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" 4.05452251e-03, 7.97805745e-03, 1.35678762e-03, 6.59123407e-02,\n",
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" 8.86219315e-02, 9.94670938e-02, 1.90769092e-01, 4.03990064e-03,\n",
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" 7.46568278e-03, 1.21848911e-06, 1.00702033e-02, 2.97853571e-02,\n",
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" 3.38161190e-02, 3.99664428e-03, 3.13029852e-03, 8.21931829e-03,\n",
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" 3.83824070e-05, 4.19483153e-02, 5.63063818e-03, 3.43977648e-01],\n",
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" 8.39000080e-06, 2.24659051e-01, 4.25373040e-03, 0.00000000e+00,\n",
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" 6.19811309e-02, 1.73345806e-01, 4.19500040e-06, 2.01360019e-04,\n",
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" 9.31290088e-04, 7.13150068e-05, 7.97050076e-05, 1.29503857e-01,\n",
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" 9.01925086e-04, 0.00000000e+00, 3.06654529e-03, 1.38435013e-04,\n",
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" 0.00000000e+00, 5.16404549e-03, 0.00000000e+00, 2.23593521e-03],\n",
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" [0.00000000e+00, 2.79589935e-03, 0.00000000e+00, 0.00000000e+00,\n",
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||
" 4.65983225e-04, 4.65983225e-04, 0.00000000e+00, 0.00000000e+00,\n",
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" 0.00000000e+00, 4.65983225e-04, 0.00000000e+00, 0.00000000e+00,\n",
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" 9.31966449e-04, 9.91146319e-01, 0.00000000e+00, 0.00000000e+00,\n",
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" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.86393290e-03],\n",
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" [0.00000000e+00, 3.58105126e-01, 7.10890292e-04, 8.37053789e-03,\n",
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" 2.07798701e-03, 1.55411555e-01, 2.07017503e-04, 3.52320354e-03,\n",
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" 2.96074089e-02, 1.96041669e-01, 6.64018405e-05, 5.03872790e-04,\n",
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" 4.89030025e-03, 3.41774179e-03, 3.83958878e-03, 6.54175309e-02,\n",
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" 3.67163118e-04, 6.64018405e-05, 1.71082389e-03, 1.32803681e-03,\n",
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" 9.59311296e-03, 9.37828347e-02, 2.22641465e-03, 2.84902956e-02,\n",
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||
" 0.00000000e+00, 8.46818766e-03, 9.76497654e-05, 2.16782479e-02],\n",
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" [0.00000000e+00, 2.36995345e-01, 2.30298271e-05, 1.35991129e-03,\n",
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" 3.56962320e-05, 1.30587180e-01, 1.03634222e-05, 1.15149135e-05,\n",
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" 2.15189553e-01, 1.84298494e-01, 9.21193083e-06, 5.17019618e-04,\n",
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" 2.29146779e-04, 4.51384611e-04, 1.37027471e-04, 1.12372890e-01,\n",
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||
" 8.46346145e-04, 9.21193083e-06, 7.48469380e-05, 1.52192612e-02,\n",
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" 8.49570321e-03, 7.04770283e-02, 5.75745677e-06, 1.10220752e-02,\n",
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||
" 2.30298271e-06, 4.74759885e-03, 8.06043948e-06, 6.86403996e-03],\n",
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||
" [0.00000000e+00, 2.23157744e-01, 1.54696226e-05, 3.59739042e-03,\n",
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" 3.83927906e-04, 1.28596160e-01, 4.07835504e-05, 4.21898798e-06,\n",
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||
" 2.80661144e-02, 8.53641901e-02, 5.62531730e-06, 7.03164663e-05,\n",
|
||
" 2.39075985e-05, 8.15671009e-05, 5.20341851e-05, 1.37148049e-01,\n",
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||
" 7.03164663e-06, 0.00000000e+00, 2.87172448e-03, 1.82677960e-01,\n",
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||
" 7.62511760e-03, 1.83129392e-01, 1.40632933e-05, 9.36474698e-03,\n",
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||
" 7.03164663e-06, 2.33028769e-03, 1.68759519e-05, 5.34827043e-03],\n",
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||
" [0.00000000e+00, 5.00725418e-02, 2.74162236e-02, 1.24416741e-03,\n",
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" 1.53653190e-02, 2.04150352e-02, 6.30516724e-03, 1.76469855e-02,\n",
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||
" 9.02392543e-03, 9.43013569e-03, 1.25717326e-02, 8.21649344e-02,\n",
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||
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" 6.98812310e-03, 1.18774932e-06, 1.65898886e-02, 5.29231401e-02,\n",
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" 5.77234290e-02, 3.94926648e-04, 6.41444018e-03, 9.18664708e-03,\n",
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" 6.94833350e-05, 3.09699695e-02, 2.25476391e-02, 2.25306543e-01],\n",
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" [0.00000000e+00, 1.61762739e-01, 6.37011651e-06, 0.00000000e+00,\n",
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" 2.54804660e-05, 8.73597778e-02, 0.00000000e+00, 4.45908156e-05,\n",
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||
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||
" 0.00000000e+00, 1.51353968e-02, 3.18505825e-05, 1.29822974e-02],\n",
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" [0.00000000e+00, 5.86452974e-01, 3.02499471e-05, 0.00000000e+00,\n",
|
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" 2.47499567e-05, 2.37643584e-01, 0.00000000e+00, 1.64999711e-05,\n",
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||
" 1.29249774e-04, 9.65413311e-02, 2.74999519e-06, 1.92499663e-05,\n",
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" 3.84999326e-05, 5.49999038e-05, 6.59998845e-05, 3.70039352e-02,\n",
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||
" 1.09999808e-05, 8.24998556e-06, 1.09999808e-05, 2.19999615e-05,\n",
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||
" 0.00000000e+00, 1.29249774e-04, 2.74999519e-05, 4.23774258e-03],\n",
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||
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||
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||
" 4.70588235e-03, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
|
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||
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||
" [0.00000000e+00, 4.00777556e-01, 3.06035126e-04, 3.24926183e-04,\n",
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||
" 9.31329118e-04, 1.33837473e-01, 5.47840658e-05, 1.00122603e-04,\n",
|
||
" 8.31206515e-05, 1.90682553e-01, 7.55642286e-06, 4.00490412e-04,\n",
|
||
" 1.35637790e-03, 6.64965212e-04, 1.19013660e-03, 1.13856402e-01,\n",
|
||
" 4.21270575e-04, 0.00000000e+00, 4.32605209e-04, 1.06923384e-03,\n",
|
||
" 2.60696589e-04, 4.78661606e-02, 1.02389530e-03, 1.92688783e-03,\n",
|
||
" 5.66731715e-06, 3.40039029e-05, 1.20902766e-04, 1.02264849e-01],\n",
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||
" [0.00000000e+00, 4.05219389e-01, 8.48582564e-05, 3.03065201e-06,\n",
|
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" 6.81896703e-04, 1.09691419e-01, 3.03065201e-06, 6.06130403e-06,\n",
|
||
" 1.24256733e-04, 2.49892412e-01, 1.30318037e-04, 8.18276044e-05,\n",
|
||
" 3.03065201e-05, 7.87969524e-05, 1.45471297e-04, 1.15470872e-01,\n",
|
||
" 3.03065201e-06, 0.00000000e+00, 1.48501949e-04, 1.45471297e-04,\n",
|
||
" 3.03065201e-05, 9.85719568e-02, 5.90977143e-04, 5.39152993e-03,\n",
|
||
" 0.00000000e+00, 2.77304659e-03, 2.03053685e-04, 1.04981786e-02],\n",
|
||
" [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
|
||
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
|
||
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
|
||
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
|
||
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
|
||
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
|
||
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]])"
|
||
]
|
||
},
|
||
"execution_count": 87,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"execution_count": 87
|
||
},
|
||
{
|
||
"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",
|
||
"\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",
|
||
"\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",
|
||
"\n",
|
||
"plt.show()"
|
||
],
|
||
"id": "f15ad6eb4df27527",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 2000x600 with 4 Axes>"
|
||
],
|
||
"image/png": 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},
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"metadata": {},
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"output_type": "display_data",
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||
"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",
|
||
"\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.show()"
|
||
],
|
||
"id": "51ec0792317364fc",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 600x500 with 2 Axes>"
|
||
],
|
||
"image/png": 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"
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data",
|
||
"jetTransient": {
|
||
"display_id": null
|
||
}
|
||
}
|
||
],
|
||
"execution_count": 142
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-25T19:54:41.378255Z",
|
||
"start_time": "2025-09-25T19:54:41.361591Z"
|
||
}
|
||
},
|
||
"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"
|
||
],
|
||
"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"
|
||
],
|
||
"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>row_token</th>\n",
|
||
" <th>chi2</th>\n",
|
||
" <th>dof</th>\n",
|
||
" <th>p</th>\n",
|
||
" <th>p_adj</th>\n",
|
||
" <th>significant</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>a</td>\n",
|
||
" <td>66636.401639</td>\n",
|
||
" <td>26</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>i</td>\n",
|
||
" <td>36497.202246</td>\n",
|
||
" <td>26</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>n</td>\n",
|
||
" <td>32505.512092</td>\n",
|
||
" <td>25</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>u</td>\n",
|
||
" <td>31904.897504</td>\n",
|
||
" <td>26</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>g</td>\n",
|
||
" <td>24595.637377</td>\n",
|
||
" <td>26</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>m</td>\n",
|
||
" <td>23254.272134</td>\n",
|
||
" <td>26</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>e</td>\n",
|
||
" <td>19945.183545</td>\n",
|
||
" <td>26</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
]
|
||
},
|
||
"execution_count": 155,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"execution_count": 155
|
||
},
|
||
{
|
||
"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."
|
||
],
|
||
"id": "d5cd70c832e5ac2"
|
||
},
|
||
{
|
||
"metadata": {},
|
||
"cell_type": "markdown",
|
||
"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": "2025-09-25T20:34:04.554985Z",
|
||
"start_time": "2025-09-25T20:34:04.545470Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"def plot_letter_grid(df_all: pd.DataFrame, use=\"freq\", sort_values=False):\n",
|
||
" \"\"\"\n",
|
||
" Plot a 1×3 grid of letter distributions for Male, Female, and Both.\n",
|
||
" `use` ∈ {\"freq\",\"count\"} controls y-axis.\n",
|
||
" \"\"\"\n",
|
||
" # Slice datasets (adapt values if your labels are 'M'/'F', etc.)\n",
|
||
" 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",
|
||
" df_both = df_all\n",
|
||
"\n",
|
||
" L_m = letter_freq(df_male, col='name')\n",
|
||
" L_f = letter_freq(df_female, col='name')\n",
|
||
" L_b = letter_freq(df_both, col='name')\n",
|
||
"\n",
|
||
" y = \"freq\" if use == \"freq\" else \"count\"\n",
|
||
" if sort_values:\n",
|
||
" L_m = L_m.sort_values(y, ascending=False)\n",
|
||
" L_f = L_f.sort_values(y, ascending=False)\n",
|
||
" L_b = L_b.sort_values(y, ascending=False)\n",
|
||
"\n",
|
||
" ymax = max(L_m[y].max(), L_f[y].max(), L_b[y].max()) * 1.10\n",
|
||
"\n",
|
||
" fig, axes = plt.subplots(1, 3, figsize=(18, 5), sharey=True, constrained_layout=True)\n",
|
||
" for ax, data, title in zip(axes, [L_m, L_f, L_b], [\"Male\", \"Female\", \"All\"]):\n",
|
||
" ax.bar(data[\"letter\"], data[y], color=\"steelblue\", alpha=0.8)\n",
|
||
" ax.set_title(title)\n",
|
||
" ax.set_xlabel(\"Letter\")\n",
|
||
" ax.set_ylim(0, ymax)\n",
|
||
" ax.grid(axis=\"y\", alpha=0.3)\n",
|
||
" axes[0].set_ylabel(\"Frequency\" if y == \"freq\" else \"Count\")\n",
|
||
" plt.show()"
|
||
],
|
||
"id": "4a0d8e2a74a6406c",
|
||
"outputs": [],
|
||
"execution_count": 167
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-25T20:34:46.748031Z",
|
||
"start_time": "2025-09-25T20:34:06.149889Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": "plot_letter_grid(df_names, use=\"freq\")",
|
||
"id": "9265e47639c4319d",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1800x500 with 3 Axes>"
|
||
],
|
||
"image/png": 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|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data",
|
||
"jetTransient": {
|
||
"display_id": null
|
||
}
|
||
}
|
||
],
|
||
"execution_count": 168
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-25T20:35:31.783422Z",
|
||
"start_time": "2025-09-25T20:34:54.827165Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": "plot_letter_grid(df_names, use=\"freq\", sort_values=True)",
|
||
"id": "395dace523f1bed4",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1800x500 with 3 Axes>"
|
||
],
|
||
"image/png": 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|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data",
|
||
"jetTransient": {
|
||
"display_id": null
|
||
}
|
||
}
|
||
],
|
||
"execution_count": 169
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "98c7ed61-5c2d-4d9a-9d85-e4efe4bfc402",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-25T21:03:55.917695Z",
|
||
"start_time": "2025-09-25T21:03:55.879223Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"def count_ngrams(df: pd.DataFrame, n: int, col: str = \"name\") -> pd.DataFrame:\n",
|
||
" # Normalize and clean\n",
|
||
" _names = (\n",
|
||
" df[col]\n",
|
||
" .astype(str)\n",
|
||
" .str.lower()\n",
|
||
" .str.replace(r\"[^a-z]\", \"\", regex=True)\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Collect n-grams from all names\n",
|
||
" ngrams = []\n",
|
||
" for name in _names:\n",
|
||
" if len(name) >= n:\n",
|
||
" ngrams.extend(name[i:i+n] for i in range(len(name) - n + 1))\n",
|
||
"\n",
|
||
" # Count\n",
|
||
" counter = Counter(ngrams)\n",
|
||
"\n",
|
||
" # Build DataFrame\n",
|
||
" df_ngrams = (\n",
|
||
" pd.DataFrame(counter.items(), columns=[f\"{n}-gram\", \"count\"])\n",
|
||
" .sort_values(\"count\", ascending=False)\n",
|
||
" .reset_index(drop=True)\n",
|
||
" )\n",
|
||
" df_ngrams[\"freq\"] = df_ngrams[\"count\"] / df_ngrams[\"count\"].sum()\n",
|
||
" return df_ngrams\n",
|
||
"\n",
|
||
"def plot_ngrams_grid(df: pd.DataFrame, n: int, top_k: int = 20,\n",
|
||
" gender_col=\"sex\", male_value=\"m\", female_value=\"f\", name_col=\"name\"):\n",
|
||
" \"\"\"\n",
|
||
" Plot top n-grams for Male, Female, and All in a 1×3 grid.\n",
|
||
" \"\"\"\n",
|
||
" # Split datasets\n",
|
||
" df_male = df[df[gender_col].str.lower() == str(male_value).lower()]\n",
|
||
" df_female = df[df[gender_col].str.lower() == str(female_value).lower()]\n",
|
||
" df_all = df\n",
|
||
"\n",
|
||
" # Compute n-grams\n",
|
||
" ng_male = count_ngrams(df_male, n, col=name_col)\n",
|
||
" ng_female = count_ngrams(df_female, n, col=name_col)\n",
|
||
" ng_all = count_ngrams(df_all, n, col=name_col)\n",
|
||
"\n",
|
||
" # Plot in a grid\n",
|
||
" fig, axes = plt.subplots(1, 3, figsize=(18, 6), sharey=True, constrained_layout=True)\n",
|
||
" for ax, data, title in zip(\n",
|
||
" axes, [ng_male, ng_female, ng_all], [\"Male\", \"Female\", \"All\"]\n",
|
||
" ):\n",
|
||
" sns.barplot(\n",
|
||
" data=data.head(top_k),\n",
|
||
" x=f\"{n}-gram\", y=\"count\",\n",
|
||
" ax=ax\n",
|
||
" )\n",
|
||
" ax.set_title(f\"{title} – Top {top_k} {n}-grams\")\n",
|
||
" ax.set_xlabel(f\"{n}-gram\")\n",
|
||
" ax.set_ylabel(\"Count\")\n",
|
||
" ax.tick_params(axis=\"x\", rotation=45)\n",
|
||
"\n",
|
||
" plt.show()"
|
||
],
|
||
"outputs": [],
|
||
"execution_count": 192
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "412883b2-c1fd-483f-b61a-ba2bfc8b04e7",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-25T21:05:12.622401Z",
|
||
"start_time": "2025-09-25T21:03:59.209917Z"
|
||
}
|
||
},
|
||
"source": "plot_ngrams_grid(df_names, n=3, top_k=20, gender_col=\"sex\")",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1800x600 with 3 Axes>"
|
||
],
|
||
"image/png": 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"
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data",
|
||
"jetTransient": {
|
||
"display_id": null
|
||
}
|
||
}
|
||
],
|
||
"execution_count": 193
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-25T21:06:14.580840Z",
|
||
"start_time": "2025-09-25T21:05:18.437587Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": "plot_ngrams_grid(df_names, n=4, top_k=20, gender_col=\"sex\")",
|
||
"id": "b4eda839e6cc5d8f",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1800x600 with 3 Axes>"
|
||
],
|
||
"image/png": 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"
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data",
|
||
"jetTransient": {
|
||
"display_id": null
|
||
}
|
||
}
|
||
],
|
||
"execution_count": 194
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-25T21:07:02.952457Z",
|
||
"start_time": "2025-09-25T21:06:20.917544Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": "plot_ngrams_grid(df_names, n=5, top_k=20, gender_col=\"sex\")",
|
||
"id": "55605ab05b9a10bb",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1800x600 with 3 Axes>"
|
||
],
|
||
"image/png": 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"
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data",
|
||
"jetTransient": {
|
||
"display_id": null
|
||
}
|
||
}
|
||
],
|
||
"execution_count": 195
|
||
},
|
||
{
|
||
"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": "2025-09-25T18:53:56.149023Z",
|
||
"start_time": "2025-09-25T18:53:56.131963Z"
|
||
}
|
||
},
|
||
"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",
|
||
" P = df_probs.loc[tokens, tokens].to_numpy(dtype=float)\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",
|
||
"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": 73
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-25T18:59:06.977707Z",
|
||
"start_time": "2025-09-25T18:59:06.964852Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Example preparation and usage\n",
|
||
"P = prepare_prob_matrix(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",
|
||
"display(pd.DataFrame(generated_var, columns=[\"name\"]))"
|
||
],
|
||
"id": "a01eb547985d5a62",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
" name\n",
|
||
"0 Mbamba\n",
|
||
"1 Ngokanga\n",
|
||
"2 Mimbango\n",
|
||
"3 Mundo\n",
|
||
"4 Musona\n",
|
||
"5 Bonda\n",
|
||
"6 Kanganga\n",
|
||
"7 Zandi\n",
|
||
"8 Vulunda\n",
|
||
"9 Mbangu"
|
||
],
|
||
"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>Mbamba</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Ngokanga</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Mimbango</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Mundo</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>Musona</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>Bonda</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>Kanganga</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>Zandi</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>Vulunda</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>Mbangu</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
]
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],
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},
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{
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"id": "f9da3dad4317ac10"
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}
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