feat: statistics tests
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province,compose,simple
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AUTRES,0.2062165520477412,0.7937834479522587
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BANDUNDU,0.6269061385346485,0.3730938614653515
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BAS-CONGO,0.09081330148566008,0.90918669851434
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EQUATEUR,0.12423822403788959,0.8757617759621105
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KASAI-OCCIDENTAL,0.2612655252892886,0.7387344747107114
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KASAI-ORIENTAL,0.07622377139542966,0.9237762286045703
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KATANGA,0.18062352012628255,0.8193764798737174
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KINSHASA,0.07679244621346286,0.9232075537865372
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MANIEMA,0.4611502742287561,0.5388497257712439
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NORD-KIVU,0.11962561930536533,0.8803743806946347
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ORIENTALE,0.16090483213325235,0.8390951678667476
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SUD-KIVU,0.409646629226467,0.590353370773533
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||||||
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m,0.0,0.31932204396028435,0.038368037519108326,0.00011873135546683685,0.00011081593176904773,0.17818311343296872,0.0006362021797098007,5.2439681997852944e-05,5.936567773341842e-05,0.20796192681201364,2.374627109336737e-05,0.00010092165214681132,0.00023053671519810822,0.04005699105062408,0.00020184330429362263,0.08672138203297763,0.010948020402004582,2.968283886670921e-06,0.0001612767578424534,0.005299376165669818,0.0002523041303670283,0.047970435892488755,0.0004581051465095455,0.006624220207087273,9.894279622236404e-07,0.018100595140919277,0.00036806720194719424,0.03766554366592954
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||||||
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n,0.0,0.09272466504327583,6.54951531625727e-05,0.0676682588133141,0.04243183895879178,0.2208676970453058,0.0016707146854644294,0.033734710117275545,0.0024899923798154134,0.0862990806755178,0.006808358436540491,0.00222879614025689,0.0004584660721380089,4.667019896015659e-05,0.05049048810360941,0.03840682843838769,0.0003961084113425055,0.0005533752162418567,0.005650231566546017,0.015463131131101295,0.04694629828845869,0.019948568656372817,0.007369969570245905,0.00011177316557684563,1.686402147299776e-05,0.014610517580350198,0.003478302475442259,0.23906279965503272
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||||||
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o,0.0,0.003164071575712822,0.016308332918058378,0.029613095334402047,0.041853890953758216,0.032495642848868175,0.0018930066187277572,0.011876432294110452,0.012510670409103,0.07853874127032717,0.0004316702272449279,0.00471407186694461,0.0692342092506865,0.04876514486192701,0.2053281179113374,0.0007785596452408519,0.01644424108555678,7.118999249916351e-05,0.17295026214096784,0.10292454961004063,0.022388605459236936,0.03825103015155055,0.011361275621116504,0.0008639876362398481,0.0006983091082417949,0.007007036807167667,0.002350564115972381,0.06718329028496059
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p,0.0,0.26933300206615723,2.192209253680628e-05,0.0001096104626840314,0.00010778362163929754,0.08803364310467982,8.03810059682897e-05,2.5575774626273995e-05,0.34018520514511513,0.09085245883670416,2.9229456715741707e-05,2.5575774626273995e-05,0.04719826523174392,3.6536820894677135e-05,0.00012422519104190227,0.0354936946581341,0.013204407071336317,3.6536820894677134e-06,0.08679504487635026,0.003801656214091156,0.001415801809668739,0.0039459766566251305,1.8268410447338567e-06,0.0004694981484966012,3.6536820894677134e-06,0.01590082445336349,0.0,0.0028005473215770025
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||||||
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q,0.0,0.00038185048063353973,4.9806584430461705e-05,0.00014941975329138513,4.9806584430461705e-05,0.0014941975329138511,0.0,4.9806584430461705e-05,3.320438962030781e-05,0.00029883950658277027,0.0,0.0,3.320438962030781e-05,0.00011621536367107731,4.9806584430461705e-05,0.00018262414291169292,0.0,8.30109740507695e-05,8.30109740507695e-05,0.00023243072734215463,9.961316886092341e-05,0.9891919711785898,4.9806584430461705e-05,4.9806584430461705e-05,0.0,0.00011621536367107731,0.0,0.007205352547606794
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r,0.0,0.1627793575149433,0.0035916362881505785,0.036496892212239035,0.040730844643505654,0.1503575411693532,0.0012619623047103333,0.015429811736916198,0.0012770756257248282,0.23819394035839017,0.00021692060750216365,0.000525410159974503,0.026246059979436992,0.01374067585882558,0.020855493981119907,0.0587779279725791,0.0009934786019822454,0.00013068577583122155,0.016929586592884027,0.006149343615133057,0.04790433801209155,0.02665767572236118,0.017815938419439997,0.00039161281805206186,2.5781547612962073e-05,0.01068822952333919,0.00010534873765986227,0.1017264302202411
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s,0.0,0.11064837045719457,0.00019155378133028236,0.03693078718830975,0.00084374879871672,0.1936315534685894,6.319971696951493e-05,3.192563022171373e-05,0.02285614506485138,0.058747068868796735,1.1076239056512926e-05,0.0013578165996337023,0.008225084814671716,0.006192269176064638,0.0010717890145861037,0.04918110758481304,0.02873958263428148,0.0002521473244041472,0.005270335160478415,0.04403717185827366,0.20958133770996804,0.02021609090855783,8.274602118689069e-05,0.0006600135390733879,1.954630421737575e-06,0.024633555661684747,5.863891265212725e-06,0.17653570425659867
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||||||
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t,0.0,0.06351954441846226,1.4753288139094001e-05,0.0013484505359131916,1.99169389877769e-05,0.16353651069982222,1.84416101738675e-05,3.39325627199162e-05,0.18805131193614777,0.2149259016103214,1.4753288139094001e-05,1.69662813599581e-05,0.0001386809085074836,0.00011802630511275201,0.00019253041021517672,0.07676357118092694,5.38495017076931e-05,2.2129932208641e-06,0.06875179805699196,0.010426886392304685,0.08126775004979235,0.009717990897221218,0.0003732581899190782,0.0005030871255431055,1.10649661043205e-05,0.008581250046104025,3.98338779755538e-05,0.11155772592816623
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||||||
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u,0.0,0.018356080355773654,0.01654418269968032,0.045542802543262595,0.07255847970462294,0.18801741120403903,0.0036002028947896146,0.04503556556922605,0.002494898139737888,0.03139678907199226,0.0007679331862739315,0.011800927182003705,0.10299977587203472,0.02639873544642752,0.06185106106897243,0.0003314734644285327,0.009195144680499687,9.672891132789921e-05,0.06532858339329739,0.1206587002937256,0.050628148113196415,0.00014745260873155367,0.0076580986870819715,0.0010781734750451204,0.0062708645441355145,0.020688190815471907,0.010810045651327663,0.07974355042289408
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||||||
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v,0.0,0.12830504252517705,1.01674063454783e-05,3.304407062280448e-05,0.00014742739200943535,0.30917049215330417,5.08370317273915e-06,7.625554759108725e-06,0.00013471813407758747,0.5037873588636906,2.541851586369575e-06,7.625554759108725e-06,0.0008693132425383947,2.541851586369575e-05,0.00019572257215045728,0.04173211934501568,5.08370317273915e-06,0.0,0.00263081639189251,5.8462586486500224e-05,2.7960367450065326e-05,0.00580050532009537,4.321147696828278e-05,0.00031010589353708815,0.0,0.002524058625264988,5.08370317273915e-06,0.004161011046886994
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w,0.0,0.36613233287858116,0.0001534788540245566,1.7053206002728514e-05,0.0002899045020463847,0.16732605729877217,1.7053206002728514e-05,6.821282401091406e-05,0.001892905866302865,0.4231412005457026,1.7053206002728514e-05,5.1159618008185536e-05,0.0003751705320600273,0.00042633015006821284,0.0036493860845839016,0.013250341064120055,1.7053206002728514e-05,5.1159618008185536e-05,0.0007162346521145975,0.0006309686221009549,0.0020804911323328784,0.007929740791268758,3.410641200545703e-05,3.410641200545703e-05,0.0,0.004280354706684857,6.821282401091406e-05,0.007349931787175989
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||||||
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x,0.0,0.42772328342798144,3.971248163297724e-05,0.012668281640919741,0.00015884992653190897,0.0298439299471824,1.985624081648862e-05,3.971248163297724e-05,0.0013899368571542036,0.19326079186688377,0.0,3.971248163297724e-05,0.00013899368571542036,0.00013899368571542036,0.00011913744489893174,0.02454231364917994,0.00230332393471268,3.971248163297724e-05,1.985624081648862e-05,0.0007148246693935904,0.002521742583694055,0.0016480679877685556,1.985624081648862e-05,0.0005956872244946587,7.942496326595449e-05,0.005877447281680632,0.00011913744489893174,0.29593741312894645
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||||||
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||||||
|
@@ -0,0 +1,3 @@
|
|||||||
|
,l2,kl_mf,kl_fm,jsd,permutation_p_value
|
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|
names,0.3189041485139616,0.04320097944655348,0.0215380760498496,0.03236952774820154,0.979
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||||||
|
surnames,1.2770018925640299,0.2936188220992242,0.23989460296618093,0.26675671253270256,0.001
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||||||
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||||||
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"source": [
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"import geopandas as gpd\n",
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"import matplotlib.pyplot as plt\n",
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"source": [
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" \"root_dir\": \"../data\",\n",
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" \"data_dir\": \"../data/dataset\",\n",
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||||||
")\n",
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||||||
"outputs": [],
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||||||
"execution_count": 5,
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||||||
"source": [
|
|
||||||
"gdf = gpd.read_file(\"../osm/provinces.shp\")\n",
|
|
||||||
"gdf_proj = gdf.to_crs(epsg=32732)\n",
|
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||||||
"gdf['centroid'] = gdf_proj.geometry.centroid.to_crs(gdf.crs)\n",
|
|
||||||
"\n",
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|
||||||
"df = loader.load_csv_complete(config.paths.data_dir / \"names_featured.csv\")"
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||||||
],
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|
||||||
"id": "b38394ce38864379"
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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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||||||
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||||||
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|
||||||
"start_time": "2025-09-21T14:14:47.279199Z"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"source": [
|
|
||||||
"import pandas as pd\n",
|
|
||||||
"import geopandas as gpd\n",
|
|
||||||
"import matplotlib.pyplot as plt\n",
|
|
||||||
"import seaborn as sns\n",
|
|
||||||
"import sys\n",
|
|
||||||
"import os\n",
|
|
||||||
"\n",
|
|
||||||
"sys.path.append(os.path.abspath(\"..\"))\n",
|
|
||||||
"from core.utils.data_loader import DataLoader\n",
|
|
||||||
"from core.config.pipeline_config import PipelineConfig"
|
|
||||||
],
|
|
||||||
"outputs": [],
|
|
||||||
"execution_count": 30
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"id": "c0b00261",
|
|
||||||
"metadata": {
|
|
||||||
"ExecuteTime": {
|
|
||||||
"end_time": "2025-09-21T14:14:47.315980Z",
|
|
||||||
"start_time": "2025-09-21T14:14:47.308376Z"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"source": [
|
|
||||||
"config = PipelineConfig(\n",
|
|
||||||
" paths={\n",
|
|
||||||
" \"root_dir\": \"../data\",\n",
|
|
||||||
" \"data_dir\": \"../data/dataset\",\n",
|
|
||||||
" \"models_dir\": \"../models\",\n",
|
|
||||||
" \"outputs_dir\": \"../data/processed\",\n",
|
|
||||||
" \"logs_dir\": \"../logs\",\n",
|
|
||||||
" \"configs_dir\": \"../configs\",\n",
|
|
||||||
" \"checkpoints_dir\": \"../checkpoints\"\n",
|
|
||||||
" }\n",
|
|
||||||
")\n",
|
|
||||||
"\n",
|
|
||||||
"loader = DataLoader(config)"
|
|
||||||
],
|
|
||||||
"outputs": [],
|
|
||||||
"execution_count": 31
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"metadata": {
|
|
||||||
"ExecuteTime": {
|
|
||||||
"end_time": "2025-09-21T14:15:47.899044Z",
|
|
||||||
"start_time": "2025-09-21T14:14:47.339266Z"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"cell_type": "code",
|
|
||||||
"source": [
|
|
||||||
"gdf = gpd.read_file(\"../osm/provinces.shp\")\n",
|
|
||||||
"gdf_proj = gdf.to_crs(epsg=32732)\n",
|
|
||||||
"gdf['centroid'] = gdf_proj.geometry.centroid.to_crs(gdf.crs)\n",
|
|
||||||
"\n",
|
|
||||||
"df = loader.load_csv_complete(config.paths.data_dir / \"names_featured.csv\")"
|
|
||||||
],
|
|
||||||
"id": "b38394ce38864379",
|
|
||||||
"outputs": [],
|
|
||||||
"execution_count": 32
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"metadata": {},
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"source": "## Exploration",
|
|
||||||
"id": "a1af5626d2a948d6"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"metadata": {
|
|
||||||
"kernelspec": {
|
|
||||||
"display_name": "Python 3 (ipykernel)",
|
|
||||||
"language": "python",
|
|
||||||
"name": "python3"
|
|
||||||
},
|
|
||||||
"language_info": {
|
|
||||||
"codemirror_mode": {
|
|
||||||
"name": "ipython",
|
|
||||||
"version": 3
|
|
||||||
},
|
|
||||||
"file_extension": ".py",
|
|
||||||
"mimetype": "text/x-python",
|
|
||||||
"name": "python",
|
|
||||||
"nbconvert_exporter": "python",
|
|
||||||
"pygments_lexer": "ipython3",
|
|
||||||
"version": "3.11.11"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"nbformat": 4,
|
|
||||||
"nbformat_minor": 5
|
|
||||||
}
|
|
||||||
@@ -0,0 +1 @@
|
|||||||
|
LETTERS = 'abcdefghijklmnopqrstuvwxyz'
|
||||||
@@ -0,0 +1,15 @@
|
|||||||
|
import seaborn as sns
|
||||||
|
|
||||||
|
from research.statistics.utils import LETTERS
|
||||||
|
|
||||||
|
|
||||||
|
def plot_transition_matrix(ax, df_probs, title=""):
|
||||||
|
hm = sns.heatmap(
|
||||||
|
df_probs.loc[list(LETTERS), list(LETTERS)],
|
||||||
|
cmap="Reds",
|
||||||
|
annot=False,
|
||||||
|
cbar=False,
|
||||||
|
ax=ax
|
||||||
|
)
|
||||||
|
ax.set_title(title, fontsize=12)
|
||||||
|
return hm
|
||||||
@@ -0,0 +1,211 @@
|
|||||||
|
import re
|
||||||
|
import unicodedata
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
import pandas as pd
|
||||||
|
from scipy.spatial.distance import euclidean
|
||||||
|
from scipy.stats import entropy
|
||||||
|
from scipy.spatial.distance import euclidean
|
||||||
|
from scipy.stats import entropy
|
||||||
|
from typing import Dict, Any
|
||||||
|
|
||||||
|
LETTERS = 'abcdefghijklmnopqrstuvwxyz'
|
||||||
|
START_TOKEN = '^'
|
||||||
|
END_TOKEN = '$'
|
||||||
|
|
||||||
|
def normalize_letters(s):
|
||||||
|
"""Normalize accents -> ascii, lowercase, keep only a-z."""
|
||||||
|
s = str(s)
|
||||||
|
s = unicodedata.normalize("NFKD", s)
|
||||||
|
s = s.encode("ascii", errors="ignore").decode("utf-8")
|
||||||
|
s = s.lower()
|
||||||
|
s = re.sub(r"[^a-z]", "", s)
|
||||||
|
return s
|
||||||
|
|
||||||
|
|
||||||
|
def identified_category_dist(df: pd.DataFrame) -> pd.DataFrame:
|
||||||
|
return (
|
||||||
|
df.groupby("province")["identified_category"]
|
||||||
|
.value_counts(normalize=True) # get proportions
|
||||||
|
.unstack(fill_value=0) # reshape into columns per word count
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
def explode_words_token(df: pd.DataFrame, source: str, target: str) -> pd.DataFrame:
|
||||||
|
# Normalize + split once (vectorized)
|
||||||
|
s = df[source].fillna('').astype(str)
|
||||||
|
s = (
|
||||||
|
s.str.lower()
|
||||||
|
.str.replace(r"[^\w'\-]+", " ", regex=True)
|
||||||
|
.str.strip()
|
||||||
|
.str.split()
|
||||||
|
)
|
||||||
|
|
||||||
|
# Explode the token list into rows under `target`
|
||||||
|
out = (
|
||||||
|
df.assign(**{target: s})
|
||||||
|
.explode(target, ignore_index=True)
|
||||||
|
)
|
||||||
|
|
||||||
|
# Drop NA/empty tokens and strip whitespace
|
||||||
|
out[target] = out[target].astype(str).str.strip()
|
||||||
|
out = out[out[target].ne('')].dropna(subset=[target]).reset_index(drop=True)
|
||||||
|
|
||||||
|
return out
|
||||||
|
|
||||||
|
|
||||||
|
def build_letter_frequencies(series: pd.Series) -> pd.DataFrame:
|
||||||
|
s = series.astype(str).str.lower().str.replace(r'[^a-z]', '', regex=True).str.cat(sep='')
|
||||||
|
out = (
|
||||||
|
s.value_counts(normalize=False)
|
||||||
|
.reindex(list(LETTERS), fill_value=0)
|
||||||
|
.rename_axis("letter").reset_index(name="count")
|
||||||
|
)
|
||||||
|
total = out["count"].sum()
|
||||||
|
out["freq"] = out["count"] / (total if total > 0 else 1)
|
||||||
|
return out
|
||||||
|
|
||||||
|
|
||||||
|
def build_transition_probabilities(names: pd.Series, alpha: float = 0.0) -> dict:
|
||||||
|
# 1) Normalize
|
||||||
|
names = (
|
||||||
|
names.astype(str)
|
||||||
|
.str.lower()
|
||||||
|
.str.replace(fr"[^{LETTERS}]", "", regex=True)
|
||||||
|
)
|
||||||
|
names = names[names.str.len() > 0]
|
||||||
|
|
||||||
|
# 2) Prepare sequences
|
||||||
|
sequences = (START_TOKEN + names + END_TOKEN).tolist()
|
||||||
|
|
||||||
|
# 3) Tokens and indices
|
||||||
|
tokens = [START_TOKEN] + list(LETTERS) + [END_TOKEN]
|
||||||
|
index = {t: i for i, t in enumerate(tokens)}
|
||||||
|
V = len(tokens)
|
||||||
|
|
||||||
|
# 4) ASCII lookup table (O(1) char -> idx); others -> -1
|
||||||
|
lut = np.full(128, -1, dtype=np.int32)
|
||||||
|
for ch, i in index.items():
|
||||||
|
lut[ord(ch)] = i
|
||||||
|
|
||||||
|
# 5) Concatenate with a separator that’s not in vocab to kill cross-boundary pairs
|
||||||
|
concat = (" ".join(sequences)).encode("ascii", errors="ignore")
|
||||||
|
|
||||||
|
# 6) Map bytes to indices
|
||||||
|
arr = np.frombuffer(concat, dtype=np.uint8)
|
||||||
|
idx = lut[arr]
|
||||||
|
|
||||||
|
# 7) Build bigram pairs; drop invalid ones (separator & OOV)
|
||||||
|
a = idx[:-1]
|
||||||
|
b = idx[1:]
|
||||||
|
mask = (a >= 0) & (b >= 0)
|
||||||
|
a, b = a[mask], b[mask]
|
||||||
|
|
||||||
|
# 8) Count with a single bincount
|
||||||
|
lin = a * V + b
|
||||||
|
counts = np.bincount(lin, minlength=V * V).reshape(V, V)
|
||||||
|
|
||||||
|
# 9) Optional Laplace smoothing
|
||||||
|
if alpha and alpha > 0:
|
||||||
|
counts = counts + alpha
|
||||||
|
|
||||||
|
# 10) Row-normalize to probabilities
|
||||||
|
row_sums = counts.sum(axis=1, keepdims=True)
|
||||||
|
# avoid division by zero
|
||||||
|
probs = np.divide(counts, np.where(row_sums == 0, 1.0, row_sums), where=True)
|
||||||
|
|
||||||
|
# 11) DataFrames
|
||||||
|
df_counts = pd.DataFrame(counts, index=tokens, columns=tokens)
|
||||||
|
df_probs = pd.DataFrame(probs, index=tokens, columns=tokens)
|
||||||
|
|
||||||
|
return {
|
||||||
|
"tokens": tokens,
|
||||||
|
"index": index,
|
||||||
|
"counts": counts,
|
||||||
|
"df_counts": df_counts,
|
||||||
|
"probs": probs,
|
||||||
|
"df_probs": df_probs,
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def build_transition_comparisons(names_transitions: Dict[str, Any], surnames_transitions: Dict[str, Any], n_permutations: int = 1000) -> pd.DataFrame:
|
||||||
|
"""
|
||||||
|
Compares letter transition probability matrices for names and surnames using
|
||||||
|
various distance metrics and a permutation test for statistical significance.
|
||||||
|
"""
|
||||||
|
|
||||||
|
# Helper function to flatten and smooth matrices
|
||||||
|
def prepare_data(data):
|
||||||
|
return {
|
||||||
|
'm': data['m']['probs'].flatten(),
|
||||||
|
'f': data['f']['probs'].flatten()
|
||||||
|
}
|
||||||
|
|
||||||
|
prepared_names = prepare_data(names_transitions)
|
||||||
|
prepared_surnames = prepare_data(surnames_transitions)
|
||||||
|
|
||||||
|
# Distance Metrics
|
||||||
|
names_l2 = euclidean(prepared_names['m'], prepared_names['f'])
|
||||||
|
surnames_l2 = euclidean(prepared_surnames['m'], prepared_surnames['f'])
|
||||||
|
|
||||||
|
kl_names_mf = entropy(prepared_names['m'] + 1e-12, prepared_names['f'] + 1e-12)
|
||||||
|
kl_names_fm = entropy(prepared_names['f'] + 1e-12, prepared_names['m'] + 1e-12)
|
||||||
|
|
||||||
|
kl_surnames_mf = entropy(prepared_surnames['m'] + 1e-12, prepared_surnames['f'] + 1e-12)
|
||||||
|
kl_surnames_fm = entropy(prepared_surnames['f'] + 1e-12, prepared_surnames['m'] + 1e-12)
|
||||||
|
|
||||||
|
jsd_names = 0.5 * (kl_names_mf + kl_names_fm)
|
||||||
|
jsd_surnames = 0.5 * (kl_surnames_mf + kl_surnames_fm)
|
||||||
|
|
||||||
|
# Permutation Test
|
||||||
|
def run_permutation_test(transitions):
|
||||||
|
# Flattened probabilities for male and female
|
||||||
|
P_m = transitions['m']['probs'].flatten()
|
||||||
|
P_f = transitions['f']['probs'].flatten()
|
||||||
|
|
||||||
|
# Calculate the observed JSD (our test statistic)
|
||||||
|
observed_jsd = 0.5 * (entropy(P_m + 1e-12, P_f + 1e-12) + entropy(P_f + 1e-12, P_m + 1e-12))
|
||||||
|
|
||||||
|
# Concatenate male and female counts
|
||||||
|
counts_m = transitions['m']['counts']
|
||||||
|
counts_f = transitions['f']['counts']
|
||||||
|
all_counts = np.concatenate((counts_m, counts_f), axis=1)
|
||||||
|
total_counts = counts_m.shape[1] + counts_f.shape[1]
|
||||||
|
|
||||||
|
permuted_jsds = []
|
||||||
|
for _ in range(n_permutations):
|
||||||
|
# Shuffle the columns (names) and split back into two groups
|
||||||
|
shuffled_indices = np.random.permutation(total_counts)
|
||||||
|
|
||||||
|
# Note: This is a simplified approach, assuming counts are
|
||||||
|
# structured per name. A more robust implementation would
|
||||||
|
# shuffle the actual names themselves.
|
||||||
|
permuted_counts_m = all_counts[:, shuffled_indices[:counts_m.shape[1]]]
|
||||||
|
permuted_counts_f = all_counts[:, shuffled_indices[counts_m.shape[1]:]]
|
||||||
|
|
||||||
|
# Re-calculate probabilities and JSD for the permuted groups
|
||||||
|
# Add a small epsilon to the denominator to prevent division by zero
|
||||||
|
epsilon = 1e-12
|
||||||
|
permuted_probs_m = permuted_counts_m / (permuted_counts_m.sum(axis=0, keepdims=True) + epsilon)
|
||||||
|
permuted_probs_f = permuted_counts_f / (permuted_counts_f.sum(axis=0, keepdims=True) + epsilon)
|
||||||
|
|
||||||
|
permuted_jsd = 0.5 * (entropy(permuted_probs_m.mean(axis=1) + 1e-12, permuted_probs_f.mean(axis=1) + 1e-12) +
|
||||||
|
entropy(permuted_probs_f.mean(axis=1) + 1e-12, permuted_probs_m.mean(axis=1) + 1e-12))
|
||||||
|
permuted_jsds.append(permuted_jsd)
|
||||||
|
|
||||||
|
# Calculate the p-value
|
||||||
|
p_value = np.mean(np.array(permuted_jsds) >= observed_jsd)
|
||||||
|
return p_value
|
||||||
|
|
||||||
|
names_p_value = run_permutation_test(names_transitions)
|
||||||
|
surnames_p_value = run_permutation_test(surnames_transitions)
|
||||||
|
|
||||||
|
out = pd.DataFrame({
|
||||||
|
"l2": [names_l2, surnames_l2],
|
||||||
|
"kl_mf": [kl_names_mf, kl_surnames_mf],
|
||||||
|
"kl_fm": [kl_names_fm, kl_surnames_fm],
|
||||||
|
"jsd": [jsd_names, jsd_surnames],
|
||||||
|
"permutation_p_value": [names_p_value, surnames_p_value]
|
||||||
|
}, index=["names", "surnames"])
|
||||||
|
|
||||||
|
return out
|
||||||
Reference in New Issue
Block a user