semantic_tree_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import operator\n",
    "from utils.methods import length, unit, cosine, find_vecs, sort_disct, find_sim, find_insts\n",
    "from utils.tree import Tree\n",
    "import gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... model loaded ...\n"
     ]
    }
   ],
   "source": [
    "# Load Google's pre-trained Word2Vec model.\n",
    "\n",
    "model = gensim.models.KeyedVectors.load_word2vec_format('data/word2vec/GoogleNews-vectors-negative300.bin.gz', binary=True) \n",
    "print('... model loaded ...')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "support vectors:\n",
      "November\n",
      "December\n",
      "May\n",
      "September\n",
      "\n",
      "instances of the source:\n",
      "('October', 0.9762672386560065)\n",
      "('September', 0.96938921721851)\n",
      "('February', 0.9637033596820024)\n",
      "('November', 0.9633622069561678)\n",
      "('December', 0.9584515023448367)\n",
      "('August', 0.9554799903357435)\n",
      "('January', 0.9509219051885732)\n",
      "('March', 0.9468773748143877)\n",
      "('June', 0.9400733172495658)\n",
      "('May', 0.9341389748230425)\n",
      "('April', 0.9319802111775765)\n",
      "('July', 0.9183570070605671)\n",
      "\n",
      "support vectors:\n",
      "hand\n",
      "foot\n",
      "\n",
      "instances of the source:\n",
      "('hand', 0.7833214506427162)\n",
      "('foot', 0.7833213661261891)\n",
      "('finger', 0.4948128818839388)\n",
      "('wrist', 0.48082118778060884)\n",
      "\n",
      "support vectors:\n",
      "man\n",
      "queen\n",
      "\n",
      "instances of the source:\n",
      "('queen', 0.7637349490360181)\n",
      "('man', 0.7637349028094987)\n",
      "('woman', 0.7087423887679983)\n",
      "('girl', 0.6168551117777956)\n",
      "\n",
      "support vectors:\n",
      "car\n",
      "speed\n",
      "driver\n",
      "wheel\n",
      "\n",
      "instances of the source:\n",
      "('driver', 0.793975434377619)\n",
      "('wheel', 0.7766899449466298)\n",
      "('car', 0.7647040329710155)\n",
      "('drivers', 0.6399795637636363)\n",
      "('vehicle', 0.6388562160223561)\n",
      "('driving', 0.6153023953542638)\n",
      "('cars', 0.5820586233642618)\n",
      "('truck', 0.58159367932899)\n",
      "('speed', 0.5693787779941294)\n",
      "('speeding', 0.5551013391327169)\n",
      "('wheels', 0.5485091071861663)\n",
      "('motorcycle', 0.5370974263396799)\n",
      "('SUV', 0.5334396871750934)\n",
      "('tire', 0.5147110000138686)\n",
      "('bike', 0.5073546128208692)\n",
      "('passenger', 0.49583106927194764)\n",
      "('speeds', 0.48777471994958005)\n",
      "('tires', 0.4814904557618588)\n",
      "('Car', 0.47147747199465967)\n",
      "('motorists', 0.42754338286571975)\n",
      "('mph', 0.4171923595634153)\n",
      "('laps', 0.3650016797902377)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "########################################################################\n",
    "# 3.1.2 Categorization\n",
    "# Example 1.\n",
    "\n",
    "support_vector_words_months = ['November','December','May','September'] \n",
    "\n",
    "h_months = Tree(model, support_vector_words_months, None)\n",
    "h_months.find_insts()\n",
    "\n",
    "support_vector = ['hand', 'foot'] \n",
    "h_adult = Tree(model, support_vector, None)\n",
    "h_adult.find_insts()\n",
    "\n",
    "support_vector = ['man', 'queen'] \n",
    "h_adult = Tree(model, support_vector, None)\n",
    "h_adult.find_insts()\n",
    " \n",
    "support_vector = ['car','speed','driver','wheel'] \n",
    "h_adult = Tree(model, support_vector, None)\n",
    "h_adult.find_insts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Spain', 'France', 'Russia', 'Germany', 'USA']\n",
      "support vectors:\n",
      "Spain\n",
      "France\n",
      "Russia\n",
      "Germany\n",
      "USA\n",
      "\n",
      "instances of the source:\n",
      "('Germany', 0.8207244343704916)\n",
      "('France', 0.7906501469634922)\n",
      "('Spain', 0.7694617964344214)\n",
      "('Italy', 0.7069019526103362)\n",
      "('Belgium', 0.6898427925536972)\n",
      "('Russia', 0.6897856545660481)\n",
      "('Europe', 0.6845493595446867)\n",
      "('Austria', 0.6771322686068174)\n",
      "('Switzerland', 0.6593649249589146)\n",
      "('Czech_Republic', 0.6567072064651759)\n",
      "('Netherlands', 0.6544353348100079)\n",
      "('Romania', 0.6523835433455439)\n",
      "('Hungary', 0.650733015275954)\n",
      "('Poland', 0.6496222789304603)\n",
      "('Argentina', 0.6487135047045006)\n",
      "('Croatia', 0.6249655467424288)\n",
      "('Portugal', 0.6197267377046075)\n",
      "('Britain', 0.6190645436412245)\n",
      "('USA', 0.6178472944775794)\n",
      "('Brazil', 0.6176541162405118)\n",
      "('Sweden', 0.6143366832749093)\n",
      "('Ukraine', 0.6017091777667769)\n",
      "('Finland', 0.6008335044182567)\n",
      "('European', 0.5919446803345122)\n",
      "('Denmark', 0.5882160960853273)\n",
      "('Japan', 0.5854512179479348)\n",
      "('Bulgaria', 0.5833336924872465)\n",
      "('Turkey', 0.5690052927857379)\n",
      "('Serbia', 0.560716004224345)\n",
      "('countries', 0.538414518751276)\n",
      "('South_Korea', 0.5380782385230463)\n",
      "('Chile', 0.5345694620150603)\n",
      "('Norway', 0.5296982438609128)\n",
      "('Greece', 0.5292112549950869)\n",
      "('European_Union', 0.5250652565905038)\n",
      "('Venezuela', 0.5190909112345321)\n",
      "('Germans', 0.5179700615506513)\n",
      "('Kazakhstan', 0.5125993325505294)\n",
      "('Azerbaijan', 0.5042828713652009)\n",
      "('Colombia', 0.49358434183204764)\n",
      "('EU', 0.49319324053491165)\n",
      "('Iceland', 0.4876312956457349)\n",
      "('Tunisia', 0.4739035529851875)\n",
      "('Korea', 0.4729509239303048)\n",
      "('Euro', 0.4656301405489708)\n",
      "('euro_zone', 0.44209608861657934)\n",
      "('Soviet_Union', 0.41330752654311104)\n",
      "('Sarkozy', 0.3938494429867749)\n",
      "\n",
      "instances of the branch: Spain\n",
      "Spain\n",
      "Barcelona\n",
      "Madrid\n",
      "\n",
      "instances of the branch: France\n",
      "French\n",
      "France\n",
      "Sarkozy\n",
      "\n",
      "instances of the branch: Russia\n",
      "Russia\n",
      "Moscow\n",
      "Ukraine\n",
      "Putin\n",
      "Kremlin\n",
      "\n",
      "instances of the branch: Germany\n",
      "Germany\n",
      "German\n",
      "Berlin\n",
      "Austria\n",
      "Frankfurt\n",
      "Germans\n",
      "\n",
      "instances of the branch: USA\n",
      "USA\n",
      "\n"
     ]
    }
   ],
   "source": [
    "########################################################################\n",
    "# 3.1.3 Meaning of semantic tree branches\n",
    "# Example 2.\n",
    "\n",
    "support_vector_adult = ['Spain', 'France', 'Russia', 'Germany', 'USA']\n",
    "print(support_vector_adult)\n",
    "h_adult = Tree(model, support_vector_adult, None)\n",
    "h_adult.find_insts()\n",
    "h_adult.branch_instances(0)\n",
    "h_adult.branch_instances(1)\n",
    "h_adult.branch_instances(2)\n",
    "h_adult.branch_instances(3)\n",
    "h_adult.branch_instances(4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "support vectors:\n",
      "walk\n",
      "walked\n",
      "\n",
      "instances of the source:\n",
      "('walked', 0.9173832544772685)\n",
      "('walk', 0.917383173678289)\n",
      "\n",
      "instances of the branch: walk\n",
      "walk\n",
      "\n",
      "instances of the branch: walked\n",
      "ran\n",
      "walked\n",
      "threw\n",
      "drove\n",
      "sat\n",
      "fled\n",
      "grabbed\n",
      "crashed\n",
      "flew\n",
      "blew\n",
      "Announces\n",
      "slid\n",
      "exploded\n",
      "erupted\n",
      "wounding\n",
      "shook\n",
      "©\n",
      "marched\n",
      "collided\n",
      "shouted\n",
      "pronounced_dead\n",
      "stormed\n",
      "article_contains\n",
      "tore\n",
      "waved\n",
      "\n",
      "support vectors:\n",
      "well\n",
      "better\n",
      "best\n",
      "\n",
      "instances of the source:\n",
      "('better', 0.820290809011908)\n",
      "('best', 0.7942518850953415)\n",
      "('well', 0.7493072171584936)\n",
      "('good', 0.6923575467776534)\n",
      "('poorly', 0.5248129240309759)\n",
      "('differently', 0.5224479725968255)\n",
      "('quicker', 0.4980097905891259)\n",
      "('toughest', 0.4200598031254735)\n",
      "('report_abuse_button', 0.15180945501359164)\n",
      "\n",
      "instances of the branch: well\n",
      "well\n",
      "\n",
      "instances of the branch: better\n",
      "better\n",
      "worse\n",
      "easier\n",
      "stronger\n",
      "faster\n",
      "harder\n",
      "tougher\n",
      "safer\n",
      "quicker\n",
      "healthier\n",
      "\n",
      "instances of the branch: best\n",
      "best\n",
      "worst\n",
      "greatest\n",
      "strongest\n",
      "toughest\n",
      "finest\n",
      "hottest\n",
      "\n",
      "support vectors:\n",
      "dog\n",
      "dogs\n",
      "\n",
      "instances of the source:\n",
      "('dogs', 0.9664494336992696)\n",
      "('dog', 0.9664494250321362)\n",
      "\n",
      "instances of the branch: dog\n",
      "dog\n",
      "wallet\n",
      "\n",
      "instances of the branch: dogs\n",
      "animals\n",
      "dogs\n",
      "civilians\n",
      "trucks\n",
      "prisoners\n",
      "birds\n",
      "horses\n",
      "guns\n",
      "planes\n",
      "inmates\n",
      "boats\n",
      "helicopters\n",
      "wells\n",
      "3_pointers\n",
      "detainees\n",
      "foreigners\n",
      "shelters\n",
      "report_abuse_button\n",
      "tents\n",
      "\n"
     ]
    }
   ],
   "source": [
    "########################################################################\n",
    "# 3.1.4 Grammatical categories\n",
    "# Example 3\n",
    "   \n",
    "support_vector = ['walk', 'walked']\n",
    "h_months = Tree(model, support_vector, None)\n",
    "h_months.find_insts()\n",
    "h_months.branch_instances(0)\n",
    "h_months.branch_instances(1)\n",
    "\n",
    "support_vector = ['well', 'better', 'best']\n",
    "h_months = Tree(model, support_vector, None)\n",
    "h_months.find_insts()\n",
    "h_months.branch_instances(0)\n",
    "h_months.branch_instances(1)\n",
    "h_months.branch_instances(2)\n",
    "\n",
    "support_vector = ['dog', 'dogs']\n",
    "h_months = Tree(model, support_vector, None)\n",
    "h_months.find_insts()\n",
    "h_months.branch_instances(0)\n",
    "h_months.branch_instances(1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "man woman\n",
      "support vectors:\n",
      "man\n",
      "woman\n",
      "\n",
      "instances of the source:\n",
      "('woman', 0.9397875662903468)\n",
      "('man', 0.9397875350771672)\n",
      "\n",
      "instances of the branch: man\n",
      "man\n",
      "guys\n",
      "guy\n",
      "Man\n",
      "hero\n",
      "Sports_Writer\n",
      "winger\n",
      "genius\n",
      "\n",
      "man 2.3106172\n",
      "woman 2.655624\n",
      "mod branch woman\n",
      "instances of the source:\n",
      "('spokeswoman', 1.3230135600306563)\n",
      "('pregnancy', 1.2458015066122183)\n",
      "('herself', 1.1384455826818505)\n",
      "('actress', 1.1368947430234801)\n",
      "('Women', 1.095780629464288)\n",
      "('Ms.', 1.0927896261158152)\n",
      "('pregnant', 1.0876568870214482)\n",
      "('Ms', 1.0431265764979043)\n",
      "('mothers', 1.0251475764965092)\n",
      "('she', 0.9910941598718414)\n",
      "('breast_cancer', 0.988529243000423)\n",
      "('gender', 0.9700311563896022)\n",
      "('babies', 0.9097078050478751)\n",
      "('woman', 0.9075840693912199)\n",
      "\n",
      "upward branch woman\n",
      "instances of the source:\n",
      "('woman', 2.495722339544731)\n",
      "('man', 2.1714892435286086)\n",
      "('girl', 2.0729242629009677)\n",
      "('Woman', 1.9518481599159394)\n",
      "('boy', 1.9088902769996612)\n",
      "('gunman', 1.9045658495286248)\n",
      "('teenager', 1.8680841919996838)\n",
      "('stabbed', 1.7433729040253876)\n",
      "('stabbing', 1.6903681454546338)\n",
      "('soldier', 1.6902164815162062)\n",
      "('lady', 1.6854382198232016)\n",
      "('victim', 1.5714800030505416)\n",
      "('girlfriend', 1.5692663711317243)\n",
      "('boyfriend', 1.5472894081951856)\n",
      "('robbery', 1.5378313991165606)\n",
      "('grandmother', 1.5322383554605756)\n",
      "('raped', 1.4761150193106405)\n",
      "('robbed', 1.4740343214277891)\n",
      "('Man', 1.4540661069346126)\n",
      "('Anyone', 1.4528891182395003)\n",
      "('Sgt', 1.4282848732509275)\n",
      "('priest', 1.427362170213306)\n",
      "('inmate', 1.42420022300444)\n",
      "('degree_murder', 1.3971916369238637)\n",
      "('firefighter', 1.3936030546929261)\n",
      "('mother', 1.3802556211167682)\n",
      "('Sports_Writer', 1.3781831660563233)\n",
      "('suspect', 1.3648474840452074)\n",
      "('Someone', 1.3635094024663197)\n",
      "('daughter', 1.3340344636448962)\n",
      "('prisoner', 1.3282599992614115)\n",
      "('businessman', 1.3195674048457564)\n",
      "('person', 1.3111187849146044)\n",
      "('slaying', 1.299934340709648)\n",
      "('politician', 1.2883306519483473)\n",
      "('cop', 1.2826200743910954)\n",
      "('actress', 1.2823199214618382)\n",
      "('detective', 1.2799824165685225)\n",
      "('teen', 1.2779628325771872)\n",
      "('men', 1.2762238697446673)\n",
      "('Entertainment_News', 1.2636855039510966)\n",
      "('clerk', 1.2612676208125304)\n",
      "('guy', 1.248093654133272)\n",
      "('worker', 1.2218307592478717)\n",
      "('neighbor', 1.215616354875134)\n",
      "('sexual_assault', 1.2147179194360855)\n",
      "('herself', 1.2018521117147616)\n",
      "('elderly', 1.1827500468421932)\n",
      "('Girl', 1.1815604774352935)\n",
      "('someone', 1.1779543041770482)\n",
      "('males', 1.1773927588053208)\n",
      "('male', 1.174003027042011)\n",
      "('husband', 1.1728327512313812)\n",
      "('knife', 1.1706002740686485)\n",
      "('lawmaker', 1.1662851862149934)\n",
      "('burglary', 1.1644184875495385)\n",
      "('detectives', 1.1622578442239813)\n",
      "('rape', 1.149241970029263)\n",
      "('stranger', 1.141448376827984)\n",
      "('homicide', 1.140325170313697)\n",
      "('dies', 1.1384086255804795)\n",
      "('son', 1.133972078988755)\n",
      "('grandson', 1.132778968610443)\n",
      "('Police', 1.1271934868317073)\n",
      "('child', 1.1265529220990245)\n",
      "('resident', 1.1261900671021088)\n",
      "('nurse', 1.1193084778312075)\n",
      "('women', 1.1143411553817093)\n",
      "('wife', 1.1091188797488238)\n",
      "('driver', 1.1037057968732868)\n",
      "('kid', 1.1028336797110792)\n",
      "('baby', 1.1008276357973)\n",
      "('drunk', 1.0887329525037481)\n",
      "('athlete', 1.086019610864741)\n",
      "('abducted', 1.085166635405109)\n",
      "('doctor', 1.0834319316047232)\n",
      "('actor', 1.0823183081466807)\n",
      "('death_toll', 1.0779984452756837)\n",
      "('teenagers', 1.0662395725222746)\n",
      "('right_hander', 1.0631081993150917)\n",
      "('pastor', 1.061420982979293)\n",
      "('father', 1.0599745196708354)\n",
      "('daughters', 1.0598561485904452)\n",
      "('uncle', 1.0596891298806639)\n",
      "('friend', 1.0592317792457828)\n",
      "('hander', 1.0586900632321448)\n",
      "('farmer', 1.057319794029682)\n",
      "('senator', 1.0540893417514035)\n",
      "('musician', 1.051083039881191)\n",
      "('murder', 1.048859481631966)\n",
      "('arrested', 1.0476900606529655)\n",
      "('dog', 1.046752713504764)\n",
      "('infant', 1.0446631788707226)\n",
      "('suspects', 1.0442939995301603)\n",
      "('citizen', 1.0378472342434735)\n",
      "('police', 1.03719128195993)\n",
      "('defendant', 1.034286680888404)\n",
      "('She', 1.0295656224162688)\n",
      "('singer', 1.0268115869479995)\n",
      "('Witnesses', 1.02608924218883)\n",
      "('somebody', 1.022812998210098)\n",
      "('congressman', 1.0225306034142796)\n",
      "('girls', 1.020447449861474)\n",
      "('unidentified', 1.0203310489683999)\n",
      "('female', 1.0197434596333212)\n",
      "('pregnant', 1.0115903508901567)\n",
      "('Firefighters', 1.0096015025923195)\n",
      "('fighter', 1.008731565779271)\n",
      "('cat', 1.0072614716725692)\n",
      "('Nobody', 1.0068435985138398)\n",
      "('pope', 1.0060637945104614)\n",
      "('ladies', 1.0038285700773366)\n",
      "('autopsy', 1.0029878284978535)\n",
      "('mom', 0.9984522953904311)\n",
      "('punched', 0.9976401812265238)\n",
      "('shooter', 0.9960304430999086)\n",
      "('kidnapped', 0.9932109195518649)\n",
      "('apartment', 0.9859567759970915)\n",
      "('jailed', 0.9839949735892061)\n",
      "('God', 0.9834880455268532)\n",
      "('cousin', 0.9826549998388888)\n",
      "('diplomat', 0.9809063697695074)\n",
      "('striker', 0.9808566380124729)\n",
      "('County_Jail', 0.9805148407999961)\n",
      "('hospitalized', 0.9794307651621172)\n",
      "('photographer', 0.9788677426084885)\n",
      "('suspicious', 0.976561865419068)\n",
      "('boys', 0.975817000760747)\n",
      "('County_Sheriff', 0.9755832130587235)\n",
      "('suburb', 0.975542701296588)\n",
      "('grandfather', 0.9715126711126204)\n",
      "('her', 0.9703159824750482)\n",
      "('sexual', 0.968976493356398)\n",
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      "('substance', 0.5023893455516149)\n",
      "('Dog', 0.5021408425814522)\n",
      "('Bible', 0.5021158671695566)\n",
      "('Michelle', 0.5019884096246898)\n",
      "('losing_streak', 0.5018953369504999)\n",
      "('civil_rights', 0.5014484018877511)\n",
      "('pulled', 0.5010575219947268)\n",
      "('basketball', 0.5010266609918245)\n",
      "('##th_century', 0.5004452763830451)\n",
      "('presidency', 0.5004417293599888)\n",
      "('monster', 0.5002936466598124)\n",
      "('unbeaten', 0.5002865387262189)\n",
      "('Margaret', 0.5002412086657343)\n",
      "('armed', 0.4995643493964932)\n",
      "('soccer', 0.49951679520219283)\n",
      "('north', 0.4994825722533276)\n",
      "('Jets', 0.4987780624264998)\n",
      "('love', 0.49866428972037374)\n",
      "('victory', 0.497993264316461)\n",
      "('White_Sox', 0.4977415657273474)\n",
      "('PGA_Tour', 0.4974675025555317)\n",
      "('supervisor', 0.49696470308179524)\n",
      "('unknown', 0.49680901716763914)\n",
      "('remembers', 0.4966243070531976)\n",
      "('George_W._Bush', 0.4964010446549063)\n",
      "('minister', 0.49631274284829574)\n",
      "('Xinhua', 0.49608348399904534)\n",
      "('Saturday', 0.49568251311250755)\n",
      "('truth', 0.4956225424374416)\n",
      "('Township', 0.4954179361253575)\n",
      "('south', 0.49537042716640034)\n",
      "('near', 0.4953573415547401)\n",
      "('Mickelson', 0.4949004289058212)\n",
      "('was', 0.49459107046448475)\n",
      "('Capt.', 0.49433955075127906)\n",
      "('nose', 0.49421993173958484)\n",
      "('Wal_Mart', 0.49368105305881665)\n",
      "('Republican', 0.4935489991971325)\n",
      "('Devils', 0.49331786546921647)\n",
      "('Olmert', 0.49316580198425347)\n",
      "('memorial', 0.4930529062877747)\n",
      "('investigation', 0.492581180380182)\n",
      "('Harry_Potter', 0.49247959954304754)\n",
      "('Christian', 0.4922501000993764)\n",
      "('winner', 0.4921574953904121)\n",
      "('bridge', 0.4920952826789437)\n",
      "('peaceful', 0.4919079959503952)\n",
      "('neighborhood', 0.49183782041136687)\n",
      "('Sen.', 0.49150970496042434)\n",
      "('Lions', 0.49111549187354314)\n",
      "\n",
      "regular branch woman\n",
      "instances of the branch: woman\n",
      "her\n",
      "she\n",
      "She\n",
      "women\n",
      "woman\n",
      "child\n",
      "mother\n",
      "daughter\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "girl\n",
      "baby\n",
      "spokeswoman\n",
      "female\n",
      "Women\n",
      "herself\n",
      "Ms.\n",
      "actress\n",
      "abortion\n",
      "Ms\n",
      "pregnant\n",
      "Miss\n",
      "daughters\n",
      "Mrs\n",
      "lady\n",
      "babies\n",
      "breast_cancer\n",
      "mothers\n",
      "nurse\n",
      "grandmother\n",
      "couples\n",
      "gender\n",
      "pregnancy\n",
      "Woman\n",
      "report_abuse_button\n",
      "infant\n",
      "raped\n",
      "breast\n",
      "widow\n",
      "\n"
     ]
    }
   ],
   "source": [
    "########################################################################\n",
    "# A.2.1 Antonomy \n",
    "# Example 5\n",
    "\n",
    "# unsymetric antonomy\n",
    "print('man woman')\n",
    "support_vector_adult = ['man', 'woman'] \n",
    "h_adult = Tree(model, support_vector_adult, None)\n",
    "h_adult.find_insts()\n",
    "h_adult.branch_instances(0)\n",
    "\n",
    "print('man',length(model['man']))\n",
    "print('woman',length(model['woman']))\n",
    "\n",
    "# symmetric antonomy\n",
    "print('mod branch woman')\n",
    "unit_mail = unit(h_adult.branches[0])\n",
    "opposite_mail = np.dot(-unit_mail, h_adult.branches[1])*-unit_mail\n",
    "find_insts(model, opposite_mail)\n",
    "\n",
    "print('upward branch woman')\n",
    "find_insts(model,  h_adult.branches[1]-opposite_mail)\n",
    "\n",
    "# regular branch\n",
    "print('regular branch woman')\n",
    "h_adult.branch_instances(1)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "words similar to chair\n",
      "('chair', 3.0518358)\n",
      "('chairs', 2.4021974)\n",
      "('Chair', 2.2853484)\n",
      "('chairperson', 1.9232249)\n",
      "('chairwoman', 1.9139162)\n",
      "('chairman', 1.8803706)\n",
      "('Vice_Chair', 1.7232699)\n",
      "('Co_Chair', 1.6692963)\n",
      "('chairing', 1.6576846)\n",
      "('Chairs', 1.6466459)\n",
      "\n",
      "support vectors:\n",
      "director\n",
      "chairman\n",
      "head\n",
      "executive\n",
      "president\n",
      "speaker\n",
      "\n",
      "instances of the source:\n",
      "('chairman', 0.824242959710922)\n",
      "('executive', 0.811630502155094)\n",
      "('president', 0.8114869095626118)\n",
      "('director', 0.8066601614459316)\n",
      "('vice_president', 0.7496077721147711)\n",
      "('chief_executive', 0.6886873734192565)\n",
      "('Executive_Director', 0.6797398696994327)\n",
      "('managing_director', 0.6657381076417622)\n",
      "('chief', 0.6600173476653418)\n",
      "('head', 0.6511749202501224)\n",
      "('chair', 0.6466832897856418)\n",
      "('Chairman', 0.6463830475232717)\n",
      "('CEO', 0.6447943926014384)\n",
      "('Vice_President', 0.598830861716218)\n",
      "('Director', 0.5968860293473758)\n",
      "('VP', 0.5906316697042124)\n",
      "('Chair', 0.5903537539692406)\n",
      "('secretary', 0.5835946329778882)\n",
      "('founder', 0.5835017314809737)\n",
      "('coordinator', 0.5789210567843724)\n",
      "('administrator', 0.570757982733203)\n",
      "('manager', 0.5683932672829819)\n",
      "('treasurer', 0.5612155004052366)\n",
      "('co_founder', 0.5565098322167534)\n",
      "('deputy', 0.5547168420310928)\n",
      "('officer', 0.5472029751912861)\n",
      "('dean', 0.5370263024221033)\n",
      "('adviser', 0.5267857214250007)\n",
      "('Chief_Executive', 0.5246258705041813)\n",
      "('consultant', 0.5116949564986345)\n",
      "('Managing_Director', 0.5114325723803207)\n",
      "('vice', 0.5095032172047562)\n",
      "('speaker', 0.5067146332969943)\n",
      "('spokesman', 0.49834750803727884)\n",
      "('General_Manager', 0.4947865672380056)\n",
      "('chaired', 0.4824441247669543)\n",
      "('commissioner', 0.47604406621266954)\n",
      "('Chief_Executive_Officer', 0.47390796425149334)\n",
      "('spokesperson', 0.4663476980196008)\n",
      "('CFO', 0.45379321285537216)\n",
      "('helm', 0.4523964724913507)\n",
      "('Senior_Vice', 0.45001628403817345)\n",
      "('counsel', 0.45000488557144447)\n",
      "('advisor', 0.4426735506458552)\n",
      "('professor', 0.4295904267774594)\n",
      "('resigned', 0.4254422425382298)\n",
      "('ambassador', 0.42337549832239896)\n",
      "('economist', 0.4143168511673078)\n",
      "('superintendent', 0.4131523194445568)\n",
      "('editor', 0.4129174125089084)\n",
      "('analyst', 0.38127648472153297)\n",
      "('spokeswoman', 0.36026879124388866)\n",
      "\n",
      "words similar to chair - manager\n",
      "('chair', 2.1273554566784165)\n",
      "('chairs', 1.801668496948298)\n",
      "('Chair', 1.4413952573540967)\n",
      "('Chairs', 1.3042280549078633)\n",
      "('sofa', 1.2063301987299262)\n",
      "('couch', 1.1714460174402044)\n",
      "('recliner', 1.117024307693365)\n",
      "('stool', 1.1106778294535742)\n",
      "('rocking_chair', 1.1032550416787743)\n",
      "('chairwoman', 1.0149541587719868)\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<list_reverseiterator at 0x7f35e6fd3278>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "########################################################################\n",
    "# A.2.2 Polisemy\n",
    "# Example 6\n",
    "\n",
    "\n",
    "print('words similar to chair')\n",
    "find_sim(model, model['chair'], 10)\n",
    "\n",
    "support_vector_manager = ['director', 'chairman','head','executive','president', 'speaker']\n",
    "cat_manager = Tree(model, support_vector_manager, None)\n",
    "cat_manager.find_insts()\n",
    "\n",
    "print('words similar to chair - manager') \n",
    "find_sim(model, model['chair']-cat_manager.root, 10)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "support vectors:\n",
      "February\n",
      "October\n",
      "December\n",
      "June\n",
      "August\n",
      "July\n",
      "November\n",
      "September\n",
      "April\n",
      "March\n",
      "January\n",
      "May\n",
      "\n",
      "instances of the source:\n",
      "('October', 0.9783126195039022)\n",
      "('February', 0.971259553630031)\n",
      "('August', 0.9686985545183694)\n",
      "('March', 0.9638293096998848)\n",
      "('September', 0.9603501127078066)\n",
      "('June', 0.9600558294060397)\n",
      "('April', 0.9585525296205124)\n",
      "('January', 0.9551238379726366)\n",
      "('July', 0.9475423806442835)\n",
      "('May', 0.9471191411241837)\n",
      "('November', 0.9457843919721464)\n",
      "('December', 0.942204245169785)\n",
      "\n",
      "vectors similart to month\n",
      "('months', 2.401113)\n",
      "('weeks', 2.0805054)\n",
      "('years', 1.8744059)\n",
      "('days', 1.8518896)\n",
      "('month', 1.6351727)\n",
      "('decades', 1.4139879)\n",
      "('year', 1.3904314)\n",
      "('hours', 1.3581333)\n",
      "('week', 1.3386409)\n",
      "('Months', 1.3271725)\n",
      "\n",
      "vectors similart to months\n",
      "('month', 2.153279)\n",
      "('week', 1.8012607)\n",
      "('year', 1.6479722)\n",
      "('months', 1.4663965)\n",
      "('weeks', 1.4055755)\n",
      "('August', 1.3277794)\n",
      "('February', 1.321259)\n",
      "('October', 1.3167071)\n",
      "('January', 1.3116376)\n",
      "('day', 1.2929561)\n",
      "\n",
      "len_months root: 2.084506092691942\n",
      "len_months: 2.401113\n",
      "\n",
      "len_months sim vectors:\n",
      "('October', 2.0392986159132978)\n",
      "('February', 2.0245964571270556)\n",
      "('August', 2.019258038875418)\n",
      "('March', 2.0091080683844784)\n",
      "('September', 2.001855661056816)\n",
      "('June', 2.001242225721305)\n",
      "('April', 1.998108588159231)\n",
      "('January', 1.990961459529272)\n",
      "('July', 1.9751578655368363)\n",
      "('May', 1.97427562017852)\n",
      "\n",
      "instances of the source:\n",
      "\n",
      "lack months sim vectors:\n",
      "('months', 1.5772621484899498)\n",
      "('weeks', 1.3983946408602463)\n",
      "('years', 1.3511791373036943)\n",
      "('days', 1.1662393111886429)\n",
      "('decades', 1.1424464698413435)\n",
      "('quarters', 1.0378955791025484)\n",
      "('hundred_thousand', 0.8766869371306687)\n",
      "('hours', 0.8739754311915912)\n",
      "('hundred', 0.8611555991937275)\n",
      "('twenty', 0.8565192600121038)\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<list_reverseiterator at 0x7f3701fcd550>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "########################################################################\n",
    "# A.2.3 Hypernomy\n",
    "# Example 7\n",
    "\n",
    "# hypernym lack between months and months\n",
    "support_vector_months = ['February', 'October', 'December', 'June', 'August', 'July', 'November', \n",
    "                       'September', 'April', 'March', 'January', 'May']\n",
    "h_months = Tree(model, support_vector_months, None)\n",
    "h_months.find_insts()\n",
    "\n",
    "print('vectors similart to month')\n",
    "find_sim(model, model['months'], 10)\n",
    "\n",
    "print('vectors similart to months')\n",
    "find_sim(model, model['month'], 10)\n",
    "\n",
    "\n",
    "len_months_abstr = length(h_months.root)\n",
    "print('len_months root: '+ str(len_months_abstr))\n",
    "len_months = length(model['months'])\n",
    "print('len_months: '+ str(len_months))\n",
    "print('')\n",
    "\n",
    "print('len_months sim vectors:')\n",
    "find_sim(model, h_months.root, 10)\n",
    "\n",
    "lack = model['months'] - h_months.root\n",
    "h_months_lack = Tree(model, ['month_lack'], [lack])\n",
    "h_months_lack.find_insts()\n",
    "\n",
    "print('lack months sim vectors:')\n",
    "find_sim(model, lack, 10)"
   ]
  },
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   "cell_type": "code",
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   "cell_type": "code",
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