notebooks/evaluate_clusters.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alpha: 0.75, Distance Threshold: 1.3, Linkage: complete, ARI: 0.17169867110239784, NMI: 0.6818697151061416\n",
      "Alpha: 0.75, Distance Threshold: 1.3, Linkage: average, ARI: 0.20356572950509144, NMI: 0.6845678606099577\n",
      "Alpha: 0.75, Distance Threshold: 1.3, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.75, Distance Threshold: 1.4, Linkage: complete, ARI: 0.1837063308504009, NMI: 0.6866795529424584\n",
      "Alpha: 0.75, Distance Threshold: 1.4, Linkage: average, ARI: 0.28598561287718716, NMI: 0.7082400001719825\n",
      "Alpha: 0.75, Distance Threshold: 1.4, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.75, Distance Threshold: 1.5, Linkage: complete, ARI: 0.18613144744924215, NMI: 0.685139070296069\n",
      "Alpha: 0.75, Distance Threshold: 1.5, Linkage: average, ARI: 0.3170754081351836, NMI: 0.7177827229178094\n",
      "Alpha: 0.75, Distance Threshold: 1.5, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.75, Distance Threshold: 1.6, Linkage: complete, ARI: 0.19843365515888264, NMI: 0.6855260397862122\n",
      "Alpha: 0.75, Distance Threshold: 1.6, Linkage: average, ARI: 0.3170754081351836, NMI: 0.7177827229178094\n",
      "Alpha: 0.75, Distance Threshold: 1.6, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.75, Distance Threshold: 1.7, Linkage: complete, ARI: 0.21570417249842797, NMI: 0.6916030943087682\n",
      "Alpha: 0.75, Distance Threshold: 1.7, Linkage: average, ARI: 0.3285345774446426, NMI: 0.7145814209487935\n",
      "Alpha: 0.75, Distance Threshold: 1.7, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.75, Distance Threshold: 1.8, Linkage: complete, ARI: 0.24219448503491656, NMI: 0.6941771978278894\n",
      "Alpha: 0.75, Distance Threshold: 1.8, Linkage: average, ARI: 0.3198257647096009, NMI: 0.7037829382831909\n",
      "Alpha: 0.75, Distance Threshold: 1.8, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.75, Distance Threshold: 1.9, Linkage: complete, ARI: 0.25578917224625924, NMI: 0.697749242849313\n",
      "Alpha: 0.75, Distance Threshold: 1.9, Linkage: average, ARI: 0.30425214069567824, NMI: 0.6881674627593213\n",
      "Alpha: 0.75, Distance Threshold: 1.9, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.75, Distance Threshold: 2.0, Linkage: complete, ARI: 0.2960083256097908, NMI: 0.7098044376905345\n",
      "Alpha: 0.75, Distance Threshold: 2.0, Linkage: average, ARI: 0.3594030600640257, NMI: 0.7024589489486371\n",
      "Alpha: 0.75, Distance Threshold: 2.0, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.75, Distance Threshold: 2.1, Linkage: complete, ARI: 0.2960083256097908, NMI: 0.7098044376905345\n",
      "Alpha: 0.75, Distance Threshold: 2.1, Linkage: average, ARI: 0.3441646303289779, NMI: 0.686098356762494\n",
      "Alpha: 0.75, Distance Threshold: 2.1, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.75, Distance Threshold: 2.15, Linkage: complete, ARI: 0.2960083256097908, NMI: 0.7098044376905345\n",
      "Alpha: 0.75, Distance Threshold: 2.15, Linkage: average, ARI: 0.3441646303289779, NMI: 0.686098356762494\n",
      "Alpha: 0.75, Distance Threshold: 2.15, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.75, Distance Threshold: 2.2, Linkage: complete, ARI: 0.32652900796267653, NMI: 0.7194988606852686\n",
      "Alpha: 0.75, Distance Threshold: 2.2, Linkage: average, ARI: 0.3441646303289779, NMI: 0.686098356762494\n",
      "Alpha: 0.75, Distance Threshold: 2.2, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.75, Distance Threshold: 2.25, Linkage: complete, ARI: 0.32652900796267653, NMI: 0.7194988606852686\n",
      "Alpha: 0.75, Distance Threshold: 2.25, Linkage: average, ARI: 0.3738546765047627, NMI: 0.6914809216913451\n",
      "Alpha: 0.75, Distance Threshold: 2.25, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.8, Distance Threshold: 1.3, Linkage: complete, ARI: 0.18316723679025154, NMI: 0.6845421741091308\n",
      "Alpha: 0.8, Distance Threshold: 1.3, Linkage: average, ARI: 0.27978939091420685, NMI: 0.704165194980816\n",
      "Alpha: 0.8, Distance Threshold: 1.3, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.8, Distance Threshold: 1.4, Linkage: complete, ARI: 0.18939566289306792, NMI: 0.6847286103595338\n",
      "Alpha: 0.8, Distance Threshold: 1.4, Linkage: average, ARI: 0.3110026754225952, NMI: 0.7136476327589361\n",
      "Alpha: 0.8, Distance Threshold: 1.4, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.8, Distance Threshold: 1.5, Linkage: complete, ARI: 0.19145756163888342, NMI: 0.6778057573410032\n",
      "Alpha: 0.8, Distance Threshold: 1.5, Linkage: average, ARI: 0.2924230534052566, NMI: 0.6908468075860806\n",
      "Alpha: 0.8, Distance Threshold: 1.5, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.8, Distance Threshold: 1.6, Linkage: complete, ARI: 0.22283986459242996, NMI: 0.687256607943783\n",
      "Alpha: 0.8, Distance Threshold: 1.6, Linkage: average, ARI: 0.34154257178974545, NMI: 0.7027857678120625\n",
      "Alpha: 0.8, Distance Threshold: 1.6, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.8, Distance Threshold: 1.7, Linkage: complete, ARI: 0.30115447028237846, NMI: 0.7107139482710674\n",
      "Alpha: 0.8, Distance Threshold: 1.7, Linkage: average, ARI: 0.3626723591839568, NMI: 0.7050657835047905\n",
      "Alpha: 0.8, Distance Threshold: 1.7, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.8, Distance Threshold: 1.8, Linkage: complete, ARI: 0.30115447028237846, NMI: 0.7107139482710674\n",
      "Alpha: 0.8, Distance Threshold: 1.8, Linkage: average, ARI: 0.40201645992002943, NMI: 0.7048524021517423\n",
      "Alpha: 0.8, Distance Threshold: 1.8, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.8, Distance Threshold: 1.9, Linkage: complete, ARI: 0.30115447028237846, NMI: 0.7107139482710674\n",
      "Alpha: 0.8, Distance Threshold: 1.9, Linkage: average, ARI: 0.40201645992002943, NMI: 0.7048524021517423\n",
      "Alpha: 0.8, Distance Threshold: 1.9, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.8, Distance Threshold: 2.0, Linkage: complete, ARI: 0.33153382619039173, NMI: 0.7204334012493541\n",
      "Alpha: 0.8, Distance Threshold: 2.0, Linkage: average, ARI: 0.3961193448962041, NMI: 0.6955894881860566\n",
      "Alpha: 0.8, Distance Threshold: 2.0, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.8, Distance Threshold: 2.1, Linkage: complete, ARI: 0.33153382619039173, NMI: 0.7204334012493541\n",
      "Alpha: 0.8, Distance Threshold: 2.1, Linkage: average, ARI: 0.3961193448962041, NMI: 0.6955894881860566\n",
      "Alpha: 0.8, Distance Threshold: 2.1, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.8, Distance Threshold: 2.15, Linkage: complete, ARI: 0.33153382619039173, NMI: 0.7204334012493541\n",
      "Alpha: 0.8, Distance Threshold: 2.15, Linkage: average, ARI: 0.3961193448962041, NMI: 0.6955894881860566\n",
      "Alpha: 0.8, Distance Threshold: 2.15, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.8, Distance Threshold: 2.2, Linkage: complete, ARI: 0.36834801252657123, NMI: 0.7315090126527524\n",
      "Alpha: 0.8, Distance Threshold: 2.2, Linkage: average, ARI: 0.3961193448962041, NMI: 0.6955894881860566\n",
      "Alpha: 0.8, Distance Threshold: 2.2, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.8, Distance Threshold: 2.25, Linkage: complete, ARI: 0.37957893345032967, NMI: 0.7332926993163529\n",
      "Alpha: 0.8, Distance Threshold: 2.25, Linkage: average, ARI: 0.3563278964065025, NMI: 0.6647945078384372\n",
      "Alpha: 0.8, Distance Threshold: 2.25, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.825, Distance Threshold: 1.3, Linkage: complete, ARI: 0.18709073518471586, NMI: 0.679244255560118\n",
      "Alpha: 0.825, Distance Threshold: 1.3, Linkage: average, ARI: 0.3020914822870445, NMI: 0.7086580610933764\n",
      "Alpha: 0.825, Distance Threshold: 1.3, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.825, Distance Threshold: 1.4, Linkage: complete, ARI: 0.19318850280938762, NMI: 0.6793871991277654\n",
      "Alpha: 0.825, Distance Threshold: 1.4, Linkage: average, ARI: 0.2837577663146637, NMI: 0.6857389620347345\n",
      "Alpha: 0.825, Distance Threshold: 1.4, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.825, Distance Threshold: 1.5, Linkage: complete, ARI: 0.2246159666291676, NMI: 0.6888584965898636\n",
      "Alpha: 0.825, Distance Threshold: 1.5, Linkage: average, ARI: 0.36651957708371286, NMI: 0.6971515718916433\n",
      "Alpha: 0.825, Distance Threshold: 1.5, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.825, Distance Threshold: 1.6, Linkage: complete, ARI: 0.29029222057973536, NMI: 0.7085332431713723\n",
      "Alpha: 0.825, Distance Threshold: 1.6, Linkage: average, ARI: 0.36651957708371286, NMI: 0.6971515718916433\n",
      "Alpha: 0.825, Distance Threshold: 1.6, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.825, Distance Threshold: 1.7, Linkage: complete, ARI: 0.29742819107666424, NMI: 0.7082364028446859\n",
      "Alpha: 0.825, Distance Threshold: 1.7, Linkage: average, ARI: 0.41847643624059455, NMI: 0.7119778445811085\n",
      "Alpha: 0.825, Distance Threshold: 1.7, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.825, Distance Threshold: 1.8, Linkage: complete, ARI: 0.29742819107666424, NMI: 0.7082364028446859\n",
      "Alpha: 0.825, Distance Threshold: 1.8, Linkage: average, ARI: 0.41847643624059455, NMI: 0.7119778445811085\n",
      "Alpha: 0.825, Distance Threshold: 1.8, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.825, Distance Threshold: 1.9, Linkage: complete, ARI: 0.3277195595642236, NMI: 0.7179408410794786\n",
      "Alpha: 0.825, Distance Threshold: 1.9, Linkage: average, ARI: 0.39581782382669434, NMI: 0.6956355305646138\n",
      "Alpha: 0.825, Distance Threshold: 1.9, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.825, Distance Threshold: 2.0, Linkage: complete, ARI: 0.3266600861175881, NMI: 0.7132036983586361\n",
      "Alpha: 0.825, Distance Threshold: 2.0, Linkage: average, ARI: 0.39581782382669434, NMI: 0.6956355305646138\n",
      "Alpha: 0.825, Distance Threshold: 2.0, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.825, Distance Threshold: 2.1, Linkage: complete, ARI: 0.3386189387285611, NMI: 0.7146884391935109\n",
      "Alpha: 0.825, Distance Threshold: 2.1, Linkage: average, ARI: 0.3954564828870327, NMI: 0.6850052506146665\n",
      "Alpha: 0.825, Distance Threshold: 2.1, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.825, Distance Threshold: 2.15, Linkage: complete, ARI: 0.3386189387285611, NMI: 0.7146884391935109\n",
      "Alpha: 0.825, Distance Threshold: 2.15, Linkage: average, ARI: 0.3954564828870327, NMI: 0.6850052506146665\n",
      "Alpha: 0.825, Distance Threshold: 2.15, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.825, Distance Threshold: 2.2, Linkage: complete, ARI: 0.32243694887215935, NMI: 0.699339342171543\n",
      "Alpha: 0.825, Distance Threshold: 2.2, Linkage: average, ARI: 0.5230674849498798, NMI: 0.7126176855368139\n",
      "Alpha: 0.825, Distance Threshold: 2.2, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.825, Distance Threshold: 2.25, Linkage: complete, ARI: 0.32243694887215935, NMI: 0.699339342171543\n",
      "Alpha: 0.825, Distance Threshold: 2.25, Linkage: average, ARI: 0.5230674849498798, NMI: 0.7126176855368139\n",
      "Alpha: 0.825, Distance Threshold: 2.25, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.85, Distance Threshold: 1.3, Linkage: complete, ARI: 0.19318850280938762, NMI: 0.6748400350185004\n",
      "Alpha: 0.85, Distance Threshold: 1.3, Linkage: average, ARI: 0.2806140953273567, NMI: 0.6885144558406945\n",
      "Alpha: 0.85, Distance Threshold: 1.3, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.85, Distance Threshold: 1.4, Linkage: complete, ARI: 0.2246159666291676, NMI: 0.6842193813344181\n",
      "Alpha: 0.85, Distance Threshold: 1.4, Linkage: average, ARI: 0.32470538574918173, NMI: 0.6838971202142645\n",
      "Alpha: 0.85, Distance Threshold: 1.4, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.85, Distance Threshold: 1.5, Linkage: complete, ARI: 0.27102865417389715, NMI: 0.6976399055879234\n",
      "Alpha: 0.85, Distance Threshold: 1.5, Linkage: average, ARI: 0.3576069723427993, NMI: 0.6930188929452312\n",
      "Alpha: 0.85, Distance Threshold: 1.5, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.85, Distance Threshold: 1.6, Linkage: complete, ARI: 0.27858808633488613, NMI: 0.6971854260277929\n",
      "Alpha: 0.85, Distance Threshold: 1.6, Linkage: average, ARI: 0.40999334614164346, NMI: 0.7077303381146031\n",
      "Alpha: 0.85, Distance Threshold: 1.6, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.85, Distance Threshold: 1.7, Linkage: complete, ARI: 0.3158356254219068, NMI: 0.7133017529483497\n",
      "Alpha: 0.85, Distance Threshold: 1.7, Linkage: average, ARI: 0.40999334614164346, NMI: 0.7077303381146031\n",
      "Alpha: 0.85, Distance Threshold: 1.7, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.85, Distance Threshold: 1.8, Linkage: complete, ARI: 0.3149352777473189, NMI: 0.7085053020219698\n",
      "Alpha: 0.85, Distance Threshold: 1.8, Linkage: average, ARI: 0.41953763735073785, NMI: 0.7060929529005862\n",
      "Alpha: 0.85, Distance Threshold: 1.8, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.85, Distance Threshold: 1.9, Linkage: complete, ARI: 0.3149352777473189, NMI: 0.7085053020219698\n",
      "Alpha: 0.85, Distance Threshold: 1.9, Linkage: average, ARI: 0.3716181740513088, NMI: 0.6727142201959784\n",
      "Alpha: 0.85, Distance Threshold: 1.9, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.85, Distance Threshold: 2.0, Linkage: complete, ARI: 0.29908462811453407, NMI: 0.6932981654728393\n",
      "Alpha: 0.85, Distance Threshold: 2.0, Linkage: average, ARI: 0.49967611614465757, NMI: 0.6998969058241437\n",
      "Alpha: 0.85, Distance Threshold: 2.0, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.85, Distance Threshold: 2.1, Linkage: complete, ARI: 0.30425214069567824, NMI: 0.6898033527291018\n",
      "Alpha: 0.85, Distance Threshold: 2.1, Linkage: average, ARI: 0.49967611614465757, NMI: 0.6998969058241437\n",
      "Alpha: 0.85, Distance Threshold: 2.1, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.85, Distance Threshold: 2.15, Linkage: complete, ARI: 0.30425214069567824, NMI: 0.6898033527291018\n",
      "Alpha: 0.85, Distance Threshold: 2.15, Linkage: average, ARI: 0.4259990227219293, NMI: 0.6480149856070272\n",
      "Alpha: 0.85, Distance Threshold: 2.15, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.85, Distance Threshold: 2.2, Linkage: complete, ARI: 0.3471938930064422, NMI: 0.6888713594191093\n",
      "Alpha: 0.85, Distance Threshold: 2.2, Linkage: average, ARI: 0.4259990227219293, NMI: 0.6480149856070272\n",
      "Alpha: 0.85, Distance Threshold: 2.2, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.85, Distance Threshold: 2.25, Linkage: complete, ARI: 0.3471938930064422, NMI: 0.6888713594191093\n",
      "Alpha: 0.85, Distance Threshold: 2.25, Linkage: average, ARI: 0.4259990227219293, NMI: 0.6480149856070272\n",
      "Alpha: 0.85, Distance Threshold: 2.25, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.875, Distance Threshold: 1.3, Linkage: complete, ARI: 0.20860886807938614, NMI: 0.6787953084638949\n",
      "Alpha: 0.875, Distance Threshold: 1.3, Linkage: average, ARI: 0.3061624115915623, NMI: 0.7065640124101997\n",
      "Alpha: 0.875, Distance Threshold: 1.3, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.875, Distance Threshold: 1.4, Linkage: complete, ARI: 0.2503928696540315, NMI: 0.6926692361797485\n",
      "Alpha: 0.875, Distance Threshold: 1.4, Linkage: average, ARI: 0.36737309824804393, NMI: 0.7024761339079328\n",
      "Alpha: 0.875, Distance Threshold: 1.4, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.875, Distance Threshold: 1.5, Linkage: complete, ARI: 0.2799335412643168, NMI: 0.6999500683390717\n",
      "Alpha: 0.875, Distance Threshold: 1.5, Linkage: average, ARI: 0.4251520686992712, NMI: 0.7189807948452899\n",
      "Alpha: 0.875, Distance Threshold: 1.5, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.875, Distance Threshold: 1.6, Linkage: complete, ARI: 0.29571386257538945, NMI: 0.6860409967473233\n",
      "Alpha: 0.875, Distance Threshold: 1.6, Linkage: average, ARI: 0.4251520686992712, NMI: 0.7189807948452899\n",
      "Alpha: 0.875, Distance Threshold: 1.6, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.875, Distance Threshold: 1.7, Linkage: complete, ARI: 0.29571386257538945, NMI: 0.6860409967473233\n",
      "Alpha: 0.875, Distance Threshold: 1.7, Linkage: average, ARI: 0.5164996122806513, NMI: 0.7350861361365266\n",
      "Alpha: 0.875, Distance Threshold: 1.7, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.875, Distance Threshold: 1.8, Linkage: complete, ARI: 0.3259123641142869, NMI: 0.6858290332070568\n",
      "Alpha: 0.875, Distance Threshold: 1.8, Linkage: average, ARI: 0.5164996122806513, NMI: 0.7350861361365266\n",
      "Alpha: 0.875, Distance Threshold: 1.8, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.875, Distance Threshold: 1.9, Linkage: complete, ARI: 0.33027198330604324, NMI: 0.6821033601762366\n",
      "Alpha: 0.875, Distance Threshold: 1.9, Linkage: average, ARI: 0.5164996122806513, NMI: 0.7350861361365266\n",
      "Alpha: 0.875, Distance Threshold: 1.9, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.875, Distance Threshold: 2.0, Linkage: complete, ARI: 0.38338059558710413, NMI: 0.6965819005086128\n",
      "Alpha: 0.875, Distance Threshold: 2.0, Linkage: average, ARI: 0.5164996122806513, NMI: 0.7350861361365266\n",
      "Alpha: 0.875, Distance Threshold: 2.0, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.875, Distance Threshold: 2.1, Linkage: complete, ARI: 0.38338059558710413, NMI: 0.6965819005086128\n",
      "Alpha: 0.875, Distance Threshold: 2.1, Linkage: average, ARI: 0.40385820473499995, NMI: 0.6677017284895554\n",
      "Alpha: 0.875, Distance Threshold: 2.1, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.875, Distance Threshold: 2.15, Linkage: complete, ARI: 0.38338059558710413, NMI: 0.6965819005086128\n",
      "Alpha: 0.875, Distance Threshold: 2.15, Linkage: average, ARI: 0.40385820473499995, NMI: 0.6677017284895554\n",
      "Alpha: 0.875, Distance Threshold: 2.15, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.875, Distance Threshold: 2.2, Linkage: complete, ARI: 0.38338059558710413, NMI: 0.6965819005086128\n",
      "Alpha: 0.875, Distance Threshold: 2.2, Linkage: average, ARI: 0.40385820473499995, NMI: 0.6677017284895554\n",
      "Alpha: 0.875, Distance Threshold: 2.2, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.875, Distance Threshold: 2.25, Linkage: complete, ARI: 0.36215102559830964, NMI: 0.6798155227619421\n",
      "Alpha: 0.875, Distance Threshold: 2.25, Linkage: average, ARI: 0.40385820473499995, NMI: 0.6677017284895554\n",
      "Alpha: 0.875, Distance Threshold: 2.25, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.9, Distance Threshold: 1.3, Linkage: complete, ARI: 0.2711317190005058, NMI: 0.6924448041891056\n",
      "Alpha: 0.9, Distance Threshold: 1.3, Linkage: average, ARI: 0.336692840221613, NMI: 0.6838491195669599\n",
      "Alpha: 0.9, Distance Threshold: 1.3, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.9, Distance Threshold: 1.4, Linkage: complete, ARI: 0.2721233829147444, NMI: 0.682151268330534\n",
      "Alpha: 0.9, Distance Threshold: 1.4, Linkage: average, ARI: 0.3977844881454412, NMI: 0.6994537003780076\n",
      "Alpha: 0.9, Distance Threshold: 1.4, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.9, Distance Threshold: 1.5, Linkage: complete, ARI: 0.2961308205007533, NMI: 0.6914905682786923\n",
      "Alpha: 0.9, Distance Threshold: 1.5, Linkage: average, ARI: 0.4509905924381417, NMI: 0.6857667813835173\n",
      "Alpha: 0.9, Distance Threshold: 1.5, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.9, Distance Threshold: 1.6, Linkage: complete, ARI: 0.3224925440631826, NMI: 0.6949753546098392\n",
      "Alpha: 0.9, Distance Threshold: 1.6, Linkage: average, ARI: 0.4509905924381417, NMI: 0.6857667813835173\n",
      "Alpha: 0.9, Distance Threshold: 1.6, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.9, Distance Threshold: 1.7, Linkage: complete, ARI: 0.3270231644240346, NMI: 0.6915050515769782\n",
      "Alpha: 0.9, Distance Threshold: 1.7, Linkage: average, ARI: 0.39816516478328806, NMI: 0.6455038893736583\n",
      "Alpha: 0.9, Distance Threshold: 1.7, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.9, Distance Threshold: 1.8, Linkage: complete, ARI: 0.3812055671980395, NMI: 0.7059065710835801\n",
      "Alpha: 0.9, Distance Threshold: 1.8, Linkage: average, ARI: 0.39816516478328806, NMI: 0.6455038893736583\n",
      "Alpha: 0.9, Distance Threshold: 1.8, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.9, Distance Threshold: 1.9, Linkage: complete, ARI: 0.41501840859732425, NMI: 0.7140100108652147\n",
      "Alpha: 0.9, Distance Threshold: 1.9, Linkage: average, ARI: 0.39816516478328806, NMI: 0.6455038893736583\n",
      "Alpha: 0.9, Distance Threshold: 1.9, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.9, Distance Threshold: 2.0, Linkage: complete, ARI: 0.39266255070264594, NMI: 0.6944554410506739\n",
      "Alpha: 0.9, Distance Threshold: 2.0, Linkage: average, ARI: 0.39816516478328806, NMI: 0.6455038893736583\n",
      "Alpha: 0.9, Distance Threshold: 2.0, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.9, Distance Threshold: 2.1, Linkage: complete, ARI: 0.39266255070264594, NMI: 0.6944554410506739\n",
      "Alpha: 0.9, Distance Threshold: 2.1, Linkage: average, ARI: 0.6301396294577164, NMI: 0.6887773698878411\n",
      "Alpha: 0.9, Distance Threshold: 2.1, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.9, Distance Threshold: 2.15, Linkage: complete, ARI: 0.39266255070264594, NMI: 0.6944554410506739\n",
      "Alpha: 0.9, Distance Threshold: 2.15, Linkage: average, ARI: 0.6301396294577164, NMI: 0.6887773698878411\n",
      "Alpha: 0.9, Distance Threshold: 2.15, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.9, Distance Threshold: 2.2, Linkage: complete, ARI: 0.39266255070264594, NMI: 0.6944554410506739\n",
      "Alpha: 0.9, Distance Threshold: 2.2, Linkage: average, ARI: 0.49598517603458925, NMI: 0.6138298594611744\n",
      "Alpha: 0.9, Distance Threshold: 2.2, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.9, Distance Threshold: 2.25, Linkage: complete, ARI: 0.3531907080966405, NMI: 0.6635812126664354\n",
      "Alpha: 0.9, Distance Threshold: 2.25, Linkage: average, ARI: 0.49598517603458925, NMI: 0.6138298594611744\n",
      "Alpha: 0.9, Distance Threshold: 2.25, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.925, Distance Threshold: 1.3, Linkage: complete, ARI: 0.26293296488329004, NMI: 0.680659529551878\n",
      "Alpha: 0.925, Distance Threshold: 1.3, Linkage: average, ARI: 0.42202331580529556, NMI: 0.7078100909297982\n",
      "Alpha: 0.925, Distance Threshold: 1.3, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.925, Distance Threshold: 1.4, Linkage: complete, ARI: 0.32567887734645523, NMI: 0.6898902603651709\n",
      "Alpha: 0.925, Distance Threshold: 1.4, Linkage: average, ARI: 0.5145101790067479, NMI: 0.7305470448712914\n",
      "Alpha: 0.925, Distance Threshold: 1.4, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.925, Distance Threshold: 1.5, Linkage: complete, ARI: 0.33356761377657135, NMI: 0.6834876435030474\n",
      "Alpha: 0.925, Distance Threshold: 1.5, Linkage: average, ARI: 0.5085065214993935, NMI: 0.7207836563431856\n",
      "Alpha: 0.925, Distance Threshold: 1.5, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.925, Distance Threshold: 1.6, Linkage: complete, ARI: 0.3618513248272035, NMI: 0.6912623226547686\n",
      "Alpha: 0.925, Distance Threshold: 1.6, Linkage: average, ARI: 0.3970311862675211, NMI: 0.6524632613745401\n",
      "Alpha: 0.925, Distance Threshold: 1.6, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.925, Distance Threshold: 1.7, Linkage: complete, ARI: 0.4200048313237827, NMI: 0.7074799520106645\n",
      "Alpha: 0.925, Distance Threshold: 1.7, Linkage: average, ARI: 0.3970311862675211, NMI: 0.6524632613745401\n",
      "Alpha: 0.925, Distance Threshold: 1.7, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.925, Distance Threshold: 1.8, Linkage: complete, ARI: 0.5145101790067479, NMI: 0.7305470448712914\n",
      "Alpha: 0.925, Distance Threshold: 1.8, Linkage: average, ARI: 0.5109757643248856, NMI: 0.6149220380906675\n",
      "Alpha: 0.925, Distance Threshold: 1.8, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.925, Distance Threshold: 1.9, Linkage: complete, ARI: 0.5145101790067479, NMI: 0.7305470448712914\n",
      "Alpha: 0.925, Distance Threshold: 1.9, Linkage: average, ARI: 0.5109757643248856, NMI: 0.6149220380906675\n",
      "Alpha: 0.925, Distance Threshold: 1.9, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.925, Distance Threshold: 2.0, Linkage: complete, ARI: 0.5085065214993935, NMI: 0.7207836563431856\n",
      "Alpha: 0.925, Distance Threshold: 2.0, Linkage: average, ARI: 0.5109757643248856, NMI: 0.6149220380906675\n",
      "Alpha: 0.925, Distance Threshold: 2.0, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.925, Distance Threshold: 2.1, Linkage: complete, ARI: 0.5085065214993935, NMI: 0.7207836563431856\n",
      "Alpha: 0.925, Distance Threshold: 2.1, Linkage: average, ARI: 0.5109757643248856, NMI: 0.6149220380906675\n",
      "Alpha: 0.925, Distance Threshold: 2.1, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.925, Distance Threshold: 2.15, Linkage: complete, ARI: 0.5085065214993935, NMI: 0.7207836563431856\n",
      "Alpha: 0.925, Distance Threshold: 2.15, Linkage: average, ARI: 0.5109757643248856, NMI: 0.6149220380906675\n",
      "Alpha: 0.925, Distance Threshold: 2.15, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.925, Distance Threshold: 2.2, Linkage: complete, ARI: 0.5085065214993935, NMI: 0.7207836563431856\n",
      "Alpha: 0.925, Distance Threshold: 2.2, Linkage: average, ARI: 0.5109757643248856, NMI: 0.6149220380906675\n",
      "Alpha: 0.925, Distance Threshold: 2.2, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.925, Distance Threshold: 2.25, Linkage: complete, ARI: 0.5085065214993935, NMI: 0.7207836563431856\n",
      "Alpha: 0.925, Distance Threshold: 2.25, Linkage: average, ARI: 0.5109757643248856, NMI: 0.6149220380906675\n",
      "Alpha: 0.925, Distance Threshold: 2.25, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 0.95, Distance Threshold: 1.3, Linkage: complete, ARI: 0.29732637485437124, NMI: 0.6706636505534975\n",
      "Alpha: 0.95, Distance Threshold: 1.3, Linkage: average, ARI: 0.4193713904699725, NMI: 0.6602772286019769\n",
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      "Alpha: 0.95, Distance Threshold: 1.4, Linkage: complete, ARI: 0.35201208854796673, NMI: 0.6691659371071301\n",
      "Alpha: 0.95, Distance Threshold: 1.4, Linkage: average, ARI: 0.4193713904699725, NMI: 0.6602772286019769\n",
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      "Alpha: 0.95, Distance Threshold: 1.5, Linkage: complete, ARI: 0.46579869522206613, NMI: 0.7014313715237067\n",
      "Alpha: 0.95, Distance Threshold: 1.5, Linkage: average, ARI: 0.49598517603458925, NMI: 0.6138298594611744\n",
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      "Alpha: 0.95, Distance Threshold: 1.6, Linkage: complete, ARI: 0.46722626771823755, NMI: 0.6968129332512717\n",
      "Alpha: 0.95, Distance Threshold: 1.6, Linkage: average, ARI: 0.49598517603458925, NMI: 0.6138298594611744\n",
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      "Alpha: 0.95, Distance Threshold: 1.7, Linkage: complete, ARI: 0.46722626771823755, NMI: 0.6968129332512717\n",
      "Alpha: 0.95, Distance Threshold: 1.7, Linkage: average, ARI: 0.49598517603458925, NMI: 0.6138298594611744\n",
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      "Alpha: 0.95, Distance Threshold: 1.8, Linkage: complete, ARI: 0.39477345988929263, NMI: 0.645124705123334\n",
      "Alpha: 0.95, Distance Threshold: 1.8, Linkage: average, ARI: 0.49598517603458925, NMI: 0.6138298594611744\n",
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      "Alpha: 0.95, Distance Threshold: 1.9, Linkage: complete, ARI: 0.39477345988929263, NMI: 0.645124705123334\n",
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      "Alpha: 0.95, Distance Threshold: 2.0, Linkage: complete, ARI: 0.39477345988929263, NMI: 0.645124705123334\n",
      "Alpha: 0.95, Distance Threshold: 2.0, Linkage: average, ARI: 0.49598517603458925, NMI: 0.6138298594611744\n",
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      "Alpha: 0.95, Distance Threshold: 2.1, Linkage: complete, ARI: 0.39477345988929263, NMI: 0.645124705123334\n",
      "Alpha: 0.95, Distance Threshold: 2.1, Linkage: average, ARI: 0.24820016810612613, NMI: 0.4319339072407677\n",
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      "Alpha: 0.95, Distance Threshold: 2.15, Linkage: average, ARI: 0.24820016810612613, NMI: 0.4319339072407677\n",
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      "Alpha: 0.95, Distance Threshold: 2.2, Linkage: complete, ARI: 0.6301396294577164, NMI: 0.6887773698878411\n",
      "Alpha: 0.95, Distance Threshold: 2.2, Linkage: average, ARI: 0.24820016810612613, NMI: 0.4319339072407677\n",
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      "Alpha: 1.2, Distance Threshold: 1.4, Linkage: average, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 1.4, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 1.5, Linkage: complete, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 1.5, Linkage: average, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 1.5, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 1.6, Linkage: complete, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 1.6, Linkage: average, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 1.6, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 1.7, Linkage: complete, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 1.7, Linkage: average, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 1.7, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 1.8, Linkage: complete, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 1.8, Linkage: average, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 1.8, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 1.9, Linkage: complete, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 1.9, Linkage: average, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 1.9, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 2.0, Linkage: complete, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 2.0, Linkage: average, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 2.0, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 2.1, Linkage: complete, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 2.1, Linkage: average, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 2.1, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 2.15, Linkage: complete, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 2.15, Linkage: average, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 2.15, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 2.2, Linkage: complete, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 2.2, Linkage: average, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 2.2, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 2.25, Linkage: complete, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 2.25, Linkage: average, ARI: 0.0, NMI: 0.0\n",
      "Alpha: 1.2, Distance Threshold: 2.25, Linkage: single, ARI: 0.0, NMI: 0.0\n",
      "Best Parameters: Alpha: 0.9, Distance Threshold: 2.1, Linkage: average, Best ARI: 0.6301396294577164, Best NMI: 0.6887773698878411\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pickle\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from sklearn.metrics import adjusted_rand_score, normalized_mutual_info_score\n",
    "from scipy.spatial.distance import pdist, squareform\n",
    "\n",
    "# Load embeddings from file\n",
    "embeddings_file = '../data/embeddings.pkl'\n",
    "with open(embeddings_file, 'rb') as f:\n",
    "    embeddings = pickle.load(f)\n",
    "\n",
    "# Define custom distance function\n",
    "def custom_distance(embedding1: np.ndarray, embedding2: np.ndarray, alpha: float = 0.2) -> float:\n",
    "    page_num1, emb1 = int(embedding1[0]), embedding1[1:]\n",
    "    page_num2, emb2 = int(embedding2[0]), embedding2[1:]\n",
    "    embedding_distance = np.linalg.norm(emb1 - emb2)\n",
    "    page_distance = abs(page_num1 - page_num2)\n",
    "    return float(alpha * embedding_distance + (1 - alpha) * page_distance)\n",
    "\n",
    "# Define ideal clusters\n",
    "ideal_clusters = [\n",
    "    (0, 0), (1, 8), (9, 11), (12, 21), (22, 29), (30, 36), (37, 38), (39, 43), (44, 45), (46, 47), (48, 48), (49, 74)\n",
    "]\n",
    "ideal_labels = np.zeros(len(embeddings), dtype=int)\n",
    "for i, (start, end) in enumerate(ideal_clusters):\n",
    "    ideal_labels[start:end+1] = i\n",
    "\n",
    "# Function to perform clustering and evaluate\n",
    "def evaluate_clustering(embeddings, alpha, distance_threshold, linkage='single'):\n",
    "    page_numbers = np.arange(len(embeddings)).reshape(-1, 1)\n",
    "    embeddings_with_pages = np.hstack((page_numbers, np.array(embeddings)))\n",
    "    \n",
    "    # Compute the custom distance matrix\n",
    "    distance_matrix = squareform(pdist(embeddings_with_pages, lambda x, y: custom_distance(x, y, alpha)))\n",
    "    \n",
    "    clustering = AgglomerativeClustering(n_clusters=None, distance_threshold=distance_threshold, metric='precomputed', linkage=linkage)\n",
    "    labels = clustering.fit_predict(distance_matrix)\n",
    "    \n",
    "    # Evaluate using Adjusted Rand Index (ARI)\n",
    "    ari = adjusted_rand_score(ideal_labels, labels)\n",
    "    # Evaluate using Normalized Mutual Information (NMI)\n",
    "    nmi = normalized_mutual_info_score(ideal_labels, labels)\n",
    "    \n",
    "    return ari, nmi\n",
    "\n",
    "# Grid search over alpha, distance_threshold, and linkage\n",
    "alpha_values = [0.75, 0.8, 0.825, 0.85, 0.875, 0.9, 0.925, 0.95, 1.0, 1.1, 1.2]\n",
    "distance_threshold_values = [1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.15, 2.2, 2.25]\n",
    "linkage_values = ['complete', 'average', 'single']\n",
    "best_ari = -1\n",
    "best_nmi = -1\n",
    "best_params = None\n",
    "\n",
    "for alpha in alpha_values:\n",
    "    for distance_threshold in distance_threshold_values:\n",
    "        for linkage in linkage_values:\n",
    "            ari, nmi = evaluate_clustering(embeddings, alpha, distance_threshold, linkage)\n",
    "            print(f'Alpha: {alpha}, Distance Threshold: {distance_threshold}, Linkage: {linkage}, ARI: {ari}, NMI: {nmi}')\n",
    "            if ari > best_ari:\n",
    "                best_ari = ari\n",
    "                best_nmi = nmi\n",
    "                best_params = (alpha, distance_threshold, linkage)\n",
    "\n",
    "print(f'Best Parameters: Alpha: {best_params[0]}, Distance Threshold: {best_params[1]}, Linkage: {best_params[2]}, Best ARI: {best_ari}, Best NMI: {best_nmi}')\n",
    "\n",
    "# Visualize the best clustering\n",
    "best_alpha, best_distance_threshold, best_linkage = best_params\n",
    "page_numbers = np.arange(len(embeddings)).reshape(-1, 1)\n",
    "embeddings_with_pages = np.hstack((page_numbers, np.array(embeddings)))\n",
    "distance_matrix = squareform(pdist(embeddings_with_pages, lambda x, y: custom_distance(x, y, best_alpha)))\n",
    "clustering = AgglomerativeClustering(n_clusters=None, distance_threshold=best_distance_threshold, metric='precomputed', linkage=best_linkage)\n",
    "labels = clustering.fit_predict(distance_matrix)\n",
    "\n",
    "# Dimensionality reduction using t-SNE\n",
    "tsne = TSNE(n_components=2, metric='precomputed', init='random', random_state=42)\n",
    "embeddings_2d = tsne.fit_transform(distance_matrix)\n",
    "\n",
    "# Plot the clusters\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.scatterplot(x=embeddings_2d[:, 0], y=embeddings_2d[:, 1], hue=labels, palette='viridis', s=100, alpha=0.7)\n",
    "plt.title('t-SNE Visualization of Clusters')\n",
    "plt.xlabel('t-SNE Component 1')\n",
    "plt.ylabel('t-SNE Component 2')\n",
    "plt.legend(title='Cluster')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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