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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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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
y,0.0,0.07903953601739935,0.0008749671887304226,0.020399235028686423,0.025884029348899416,0.017196855117933077,0.0011899553766733747,0.0022049173156006648,0.00033248753171756057,0.011642063422621652,9.499643763358873e-05,0.0003999850005624789,0.07594465207554717,0.013214504456082897,0.04049848130695099,0.024181593190255365,0.006899741259702761,5.749784383085634e-05,0.01051710560853968,0.03773608489681637,0.0037698586303013637,0.0075047185730535105,0.028846418259315276,0.00024249090659100284,0.00047998200067497467,6.749746884491831e-05,0.0010774595952651777,0.5897028861417697
|
||||
z,0.0,0.40388373911101183,0.0005723698905517087,0.0004886084431538977,0.0008794951976770158,0.21811480902389993,2.792048246593701e-05,0.00011168192986374804,0.000991177127540764,0.20157192316283226,2.792048246593701e-05,0.00015356265356265356,0.0005584096493187402,0.0017589903953540316,0.0011168192986374804,0.07377987491623855,5.584096493187402e-05,2.792048246593701e-05,0.004844203707840071,6.980120616484253e-05,4.1880723698905516e-05,0.027669198123743577,6.980120616484253e-05,0.0018846325664507483,1.3960241232968505e-05,0.018092472637927185,0.004606879606879607,0.03858610676792495
|
||||
$,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
|
||||
|
@@ -0,0 +1,3 @@
|
||||
,l2,kl_mf,kl_fm,jsd,permutation_p_value
|
||||
names,0.3189041485139616,0.04320097944655348,0.0215380760498496,0.03236952774820154,0.979
|
||||
surnames,1.2770018925640299,0.2936188220992242,0.23989460296618093,0.26675671253270256,0.001
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||||
|
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|
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Vendored
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@@ -459,14 +459,6 @@
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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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||||
{
|
||||
"metadata": {},
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||||
"cell_type": "markdown",
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||||
@@ -798,14 +790,6 @@
|
||||
}
|
||||
],
|
||||
"execution_count": 49
|
||||
},
|
||||
{
|
||||
"metadata": {},
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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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|
||||
],
|
||||
"metadata": {
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||||
|
||||
Vendored
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@@ -1,107 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"metadata": {},
|
||||
"cell_type": "markdown",
|
||||
"source": "# Qualitative Analysis",
|
||||
"id": "d20715dd63f57364"
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"id": "c93a55c8",
|
||||
"metadata": {
|
||||
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||||
"end_time": "2025-09-21T13:34:50.973298Z",
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||||
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|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import geopandas as gpd\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import seaborn as sns\n",
|
||||
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|
||||
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|
||||
"\n",
|
||||
"sys.path.append(os.path.abspath(\"..\"))\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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||||
"config = PipelineConfig(\n",
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||||
" paths={\n",
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||||
" \"root_dir\": \"../data\",\n",
|
||||
" \"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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||||
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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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|
||||
"execution_count": 5,
|
||||
"source": [
|
||||
"gdf = gpd.read_file(\"../osm/provinces.shp\")\n",
|
||||
"gdf_proj = gdf.to_crs(epsg=32732)\n",
|
||||
"gdf['centroid'] = gdf_proj.geometry.centroid.to_crs(gdf.crs)\n",
|
||||
"\n",
|
||||
"df = loader.load_csv_complete(config.paths.data_dir / \"names_featured.csv\")"
|
||||
],
|
||||
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|
||||
},
|
||||
{
|
||||
"metadata": {},
|
||||
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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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|
||||
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||||
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|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import geopandas as gpd\n",
|
||||
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|
||||
"import seaborn as sns\n",
|
||||
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|
||||
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|
||||
"\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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||||
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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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||||
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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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|
||||
"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",
|
||||
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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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|
||||
},
|
||||
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|
||||
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|
||||
"name": "ipython",
|
||||
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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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|
||||
"version": "3.11.11"
|
||||
}
|
||||
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|
||||
"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