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Creating Word Vectors with word2vec

N.B.: use "git pull" anywhere in the nyc-ds-academy directory to update to latest notebooks

Load dependencies





In [1]:



    


import nltk
from nltk import word_tokenize, sent_tokenize
import gensim
from gensim.models.word2vec import Word2Vec
from sklearn.manifold import TSNE
import pandas as pd
from bokeh.io import output_notebook
from bokeh.plotting import show, figure
%matplotlib inline



    


















    






Using TensorFlow backend.

















In [2]:



    


nltk.download('punkt') # English-language sentence tokenizer (not all periods end sentences; not all sentences start with a capital letter)



    


















    






[nltk_data] Downloading package punkt to /home/jovyan/nltk_data...
[nltk_data]   Unzipping tokenizers/punkt.zip.










    
Out[2]:








True

Load data





In [3]:



    


nltk.download('gutenberg')



    


















    






[nltk_data] Downloading package gutenberg to /home/jovyan/nltk_data...
[nltk_data]   Unzipping corpora/gutenberg.zip.










    
Out[3]:








True

















In [4]:



    


from nltk.corpus import gutenberg



    











In [5]:



    


gutenberg.fileids()



    


















    
Out[5]:








['austen-emma.txt',
 'austen-persuasion.txt',
 'austen-sense.txt',
 'bible-kjv.txt',
 'blake-poems.txt',
 'bryant-stories.txt',
 'burgess-busterbrown.txt',
 'carroll-alice.txt',
 'chesterton-ball.txt',
 'chesterton-brown.txt',
 'chesterton-thursday.txt',
 'edgeworth-parents.txt',
 'melville-moby_dick.txt',
 'milton-paradise.txt',
 'shakespeare-caesar.txt',
 'shakespeare-hamlet.txt',
 'shakespeare-macbeth.txt',
 'whitman-leaves.txt']

















In [6]:



    


len(gutenberg.fileids())



    


















    
Out[6]:








18

Tokenize text





In [7]:



    


gberg_sent_tokens = sent_tokenize(gutenberg.raw())



    











In [8]:



    


gberg_sent_tokens[0:5]



    


















    
Out[8]:








['[Emma by Jane Austen 1816]\n\nVOLUME I\n\nCHAPTER I\n\n\nEmma Woodhouse, handsome, clever, and rich, with a comfortable home\nand happy disposition, seemed to unite some of the best blessings\nof existence; and had lived nearly twenty-one years in the world\nwith very little to distress or vex her.',
 "She was the youngest of the two daughters of a most affectionate,\nindulgent father; and had, in consequence of her sister's marriage,\nbeen mistress of his house from a very early period.",
 'Her mother\nhad died too long ago for her to have more than an indistinct\nremembrance of her caresses; and her place had been supplied\nby an excellent woman as governess, who had fallen little short\nof a mother in affection.',
 "Sixteen years had Miss Taylor been in Mr. Woodhouse's family,\nless as a governess than a friend, very fond of both daughters,\nbut particularly of Emma.",
 'Between _them_ it was more the intimacy\nof sisters.']

















In [9]:



    


gberg_sent_tokens[1]



    


















    
Out[9]:








"She was the youngest of the two daughters of a most affectionate,\nindulgent father; and had, in consequence of her sister's marriage,\nbeen mistress of his house from a very early period."

















In [10]:



    


word_tokenize(gberg_sent_tokens[1])



    


















    
Out[10]:








['She',
 'was',
 'the',
 'youngest',
 'of',
 'the',
 'two',
 'daughters',
 'of',
 'a',
 'most',
 'affectionate',
 ',',
 'indulgent',
 'father',
 ';',
 'and',
 'had',
 ',',
 'in',
 'consequence',
 'of',
 'her',
 'sister',
 "'s",
 'marriage',
 ',',
 'been',
 'mistress',
 'of',
 'his',
 'house',
 'from',
 'a',
 'very',
 'early',
 'period',
 '.']

















In [11]:



    


word_tokenize(gberg_sent_tokens[1])[14]



    


















    
Out[11]:








'father'

















In [12]:



    


# a convenient method that handles newlines, as well as tokenizing sentences and words in one shot
gberg_sents = gutenberg.sents()



    











In [13]:



    


gberg_sents[0:5]



    


















    
Out[13]:








[['[', 'Emma', 'by', 'Jane', 'Austen', '1816', ']'],
 ['VOLUME', 'I'],
 ['CHAPTER', 'I'],
 ['Emma',
  'Woodhouse',
  ',',
  'handsome',
  ',',
  'clever',
  ',',
  'and',
  'rich',
  ',',
  'with',
  'a',
  'comfortable',
  'home',
  'and',
  'happy',
  'disposition',
  ',',
  'seemed',
  'to',
  'unite',
  'some',
  'of',
  'the',
  'best',
  'blessings',
  'of',
  'existence',
  ';',
  'and',
  'had',
  'lived',
  'nearly',
  'twenty',
  '-',
  'one',
  'years',
  'in',
  'the',
  'world',
  'with',
  'very',
  'little',
  'to',
  'distress',
  'or',
  'vex',
  'her',
  '.'],
 ['She',
  'was',
  'the',
  'youngest',
  'of',
  'the',
  'two',
  'daughters',
  'of',
  'a',
  'most',
  'affectionate',
  ',',
  'indulgent',
  'father',
  ';',
  'and',
  'had',
  ',',
  'in',
  'consequence',
  'of',
  'her',
  'sister',
  "'",
  's',
  'marriage',
  ',',
  'been',
  'mistress',
  'of',
  'his',
  'house',
  'from',
  'a',
  'very',
  'early',
  'period',
  '.']]

















In [14]:



    


gberg_sents[4]



    


















    
Out[14]:








['She',
 'was',
 'the',
 'youngest',
 'of',
 'the',
 'two',
 'daughters',
 'of',
 'a',
 'most',
 'affectionate',
 ',',
 'indulgent',
 'father',
 ';',
 'and',
 'had',
 ',',
 'in',
 'consequence',
 'of',
 'her',
 'sister',
 "'",
 's',
 'marriage',
 ',',
 'been',
 'mistress',
 'of',
 'his',
 'house',
 'from',
 'a',
 'very',
 'early',
 'period',
 '.']

















In [15]:



    


gberg_sents[4][14]



    


















    
Out[15]:








'father'

Run word2vec





In [17]:



    


# model = Word2Vec(sentences=gberg_sents, size=64, sg=1, window=10, min_count=5, seed=42, workers=8)



    











In [18]:



    


# model.save('../raw_gutenberg_model.w2v')

Explore model





In [19]:



    


# skip re-training the model with the next line:  
model = gensim.models.Word2Vec.load('../raw_gutenberg_model.w2v')



    











In [20]:



    


model['dog']



    


















    
Out[20]:








array([ 0.26904255, -0.1621359 ,  0.3750256 , -0.45720032,  0.11301365,
        0.38777879,  0.07985851, -0.41821676,  0.25089404,  0.33926705,
       -0.080161  , -0.41848078, -0.11926382,  0.05567036,  0.17746113,
        0.48711824, -0.07987826,  0.24794155,  0.51635629,  0.28091279,
       -0.02160198, -0.21664959, -0.16267581, -0.30657738, -0.05135779,
       -0.0717189 , -0.23059118,  0.39070779, -0.02148601, -0.02437739,
       -0.24497117, -0.21258108, -0.04940053,  0.47320694, -0.29593673,
       -0.3120383 , -0.16338396, -0.11775671,  0.09429431, -0.62936276,
        0.56831205, -0.04018871,  0.05976823,  0.29181743, -0.01939399,
        0.06972519, -0.29290241, -0.05240246,  0.26122624,  0.04284862,
       -0.10525419, -0.24352749,  0.34333584, -0.46437535,  0.81177765,
       -0.00473523, -0.38881841, -0.02673459, -0.40746167,  0.11519276,
        0.26032686,  0.12146576, -0.41793686,  0.24636635], dtype=float32)

















In [21]:



    


len(model['dog'])



    


















    
Out[21]:








64

















In [22]:



    


model.most_similar('dog') # distance



    


















    
Out[22]:








[('puppy', 0.8137584328651428),
 ('broth', 0.7907859683036804),
 ('cage', 0.7828431725502014),
 ('sweeper', 0.7751598358154297),
 ('pig', 0.7609682679176331),
 ('pet', 0.7605292797088623),
 ('boy', 0.7512097358703613),
 ('cow', 0.7502828240394592),
 ('fox', 0.745104968547821),
 ('Truck', 0.7432427406311035)]

















In [23]:



    


model.most_similar('think')



    


















    
Out[23]:








[('manage', 0.849516749382019),
 ('suppose', 0.8426423072814941),
 ('know', 0.8389058709144592),
 ('contradict', 0.8207963705062866),
 ('NOW', 0.8158916234970093),
 ('Mamma', 0.8147774934768677),
 ('interfere', 0.8047748804092407),
 ('imagine', 0.8041163682937622),
 ('anyhow', 0.8036106824874878),
 ('believe', 0.8022602796554565)]

















In [24]:



    


model.most_similar('day')



    


















    
Out[24]:








[('morning', 0.7969126105308533),
 ('night', 0.7783966064453125),
 ('time', 0.7478375434875488),
 ('month', 0.7393653392791748),
 ('week', 0.7346140742301941),
 ('evening', 0.704649806022644),
 ('feasting', 0.7016774415969849),
 ('Saturday', 0.6912297606468201),
 ('Adar', 0.6819364428520203),
 ('seventh', 0.675483226776123)]

















In [25]:



    


model.most_similar('father')



    


















    
Out[25]:








[('mother', 0.8784882426261902),
 ('brother', 0.8613675236701965),
 ('wife', 0.7934472560882568),
 ('sister', 0.7911010980606079),
 ('daughter', 0.785748302936554),
 ('Amnon', 0.776392936706543),
 ('Tamar', 0.7663865089416504),
 ('servant', 0.7563588619232178),
 ('uncle', 0.7395140528678894),
 ('bondwoman', 0.7374235391616821)]

















In [26]:



    


model.most_similar('broth')



    


















    
Out[26]:








[('poisoned', 0.8916468024253845),
 ('slice', 0.8743618130683899),
 ('basin', 0.8612580299377441),
 ('pepper', 0.8564265966415405),
 ('shell', 0.8563050031661987),
 ('shure', 0.8496631383895874),
 ('bun', 0.8453623056411743),
 ('Lightfoot', 0.8451728820800781),
 ('mandarin', 0.8446893095970154),
 ('cowslip', 0.8435923457145691)]

















In [27]:



    


# close, but not quite; distinctly in female direction: 
model.most_similar(positive=['father', 'woman'], negative=['man'])



    


















    
Out[27]:








[('sister', 0.7926232218742371),
 ('daughter', 0.7917730808258057),
 ('wife', 0.7815544605255127),
 ('husband', 0.7808158993721008),
 ('mother', 0.7753645181655884),
 ('brother', 0.7328757047653198),
 ('Tamar', 0.732427716255188),
 ('conceived', 0.717141330242157),
 ('Sarah', 0.7118659019470215),
 ('Rachel', 0.7106494903564453)]

















In [29]:



    


model.most_similar(positive=['king', 'woman'], negative=['man'], topn=50)



    


















    
Out[29]:








[('Rachel', 0.7507852911949158),
 ('Pharaoh', 0.7350609302520752),
 ('Sarah', 0.7345616817474365),
 ('Leah', 0.7254290580749512),
 ('Laban', 0.7232168912887573),
 ('Rebekah', 0.7184093594551086),
 ('Hagar', 0.7087153792381287),
 ('Padanaram', 0.7086805105209351),
 ('Abram', 0.7034566402435303),
 ('Bilhah', 0.6920945644378662),
 ('Solomon', 0.6878252029418945),
 ('Abimelech', 0.6848732233047485),
 ('Hamor', 0.6816117763519287),
 ('Esau', 0.6815595626831055),
 ('Zilpah', 0.6792290210723877),
 ('Jerubbaal', 0.6787564754486084),
 ('conceived', 0.6777492761611938),
 ('Onan', 0.674083411693573),
 ('daughter', 0.6740672588348389),
 ('Bethuel', 0.6726176142692566),
 ('Ephron', 0.6718195080757141),
 ('Sarai', 0.6691693067550659),
 ('damsel', 0.6690042018890381),
 ('Judah', 0.6685984134674072),
 ('Shechem', 0.6679830551147461),
 ('birthright', 0.6656962037086487),
 ('Lot', 0.6613132953643799),
 ('household', 0.6583735346794128),
 ('Mephibosheth', 0.6569991707801819),
 ('Babylon', 0.6507256031036377),
 ('queen', 0.6497037410736084),
 ('Heth', 0.6490903496742249),
 ('Tamar', 0.6490516662597656),
 ('Jerusalem', 0.6476879119873047),
 ('Hanun', 0.644147515296936),
 ('Samaria', 0.6416412591934204),
 ('kindred', 0.6413353681564331),
 ('Jethro', 0.6407186985015869),
 ('Benhadad', 0.640671968460083),
 ('Rahab', 0.6401610374450684),
 ('Gerar', 0.6396584510803223),
 ('Hittite', 0.6393617391586304),
 ('Canaan', 0.6370488405227661),
 ('David', 0.6362689733505249),
 ('Caleb', 0.6357195377349854),
 ('Esther', 0.6332159042358398),
 ('Jephunneh', 0.6326543688774109),
 ('concubine', 0.6308900713920593),
 ('Naboth', 0.6306082010269165),
 ('Asa', 0.6302342414855957)]

















In [ ]:



    


# impressive for such a small data set, without any cleaning, e.g., to lower case (covered next)

Reduce word vector dimensionality with t-SNE





In [30]:



    


model.wv.vocab



    


















    
Out[30]:








{'[': <gensim.models.keyedvectors.Vocab at 0x7f4c1ac84a58>,
 'Emma': <gensim.models.keyedvectors.Vocab at 0x7f4c1ac84278>,
 'by': <gensim.models.keyedvectors.Vocab at 0x7f4c1ac84978>,
 'Jane': <gensim.models.keyedvectors.Vocab at 0x7f4c1ac84550>,
 ']': <gensim.models.keyedvectors.Vocab at 0x7f4c1ac84860>,
 'I': <gensim.models.keyedvectors.Vocab at 0x7f4c1ac84940>,
 'CHAPTER': <gensim.models.keyedvectors.Vocab at 0x7f4c1bf62048>,
 'Woodhouse': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab57be0>,
 ',': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab57518>,
 'handsome': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab570b8>,
 'clever': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab57d68>,
 'and': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab57dd8>,
 'rich': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab57e48>,
 'with': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab579b0>,
 'a': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab574e0>,
 'comfortable': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab574a8>,
 'home': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab57240>,
 'happy': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab576d8>,
 'disposition': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab57ba8>,
 'seemed': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab57668>,
 'to': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab577f0>,
 'unite': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab575c0>,
 'some': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab57f98>,
 'of': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab57978>,
 'the': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab57f28>,
 'best': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab572b0>,
 'blessings': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab57780>,
 'existence': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab57b00>,
 ';': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab57748>,
 'had': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab57c50>,
 'lived': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab57438>,
 'nearly': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab572e8>,
 'twenty': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab57588>,
 '-': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab57f60>,
 'one': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832cf8>,
 'years': <gensim.models.keyedvectors.Vocab at 0x7f4c1b8329e8>,
 'in': <gensim.models.keyedvectors.Vocab at 0x7f4c1b8321d0>,
 'world': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832550>,
 'very': <gensim.models.keyedvectors.Vocab at 0x7f4c1b8326d8>,
 'little': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832518>,
 'distress': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832128>,
 'or': <gensim.models.keyedvectors.Vocab at 0x7f4c1b8327f0>,
 'vex': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832da0>,
 'her': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832278>,
 '.': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832e10>,
 'She': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832588>,
 'was': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832f98>,
 'youngest': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832a20>,
 'two': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832f60>,
 'daughters': <gensim.models.keyedvectors.Vocab at 0x7f4c1b8322e8>,
 'most': <gensim.models.keyedvectors.Vocab at 0x7f4c1b8329b0>,
 'affectionate': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832048>,
 'indulgent': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832a90>,
 'father': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832240>,
 'consequence': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832780>,
 'sister': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832ba8>,
 "'": <gensim.models.keyedvectors.Vocab at 0x7f4c1b832860>,
 's': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832be0>,
 'marriage': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832c18>,
 'been': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832208>,
 'mistress': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832ef0>,
 'his': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832358>,
 'house': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832438>,
 'from': <gensim.models.keyedvectors.Vocab at 0x7f4c1b8327b8>,
 'early': <gensim.models.keyedvectors.Vocab at 0x7f4c1b8324a8>,
 'period': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832b70>,
 'Her': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832d68>,
 'mother': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832320>,
 'died': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832198>,
 'too': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832e80>,
 'long': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832d30>,
 'ago': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832b38>,
 'for': <gensim.models.keyedvectors.Vocab at 0x7f4c1b832400>,
 'have': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab6b828>,
 'more': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab6ba90>,
 'than': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab6bd30>,
 'an': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab6bf60>,
 'remembrance': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab6bc50>,
 'caresses': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab6b9b0>,
 'place': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab6b6a0>,
 'supplied': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab6bdd8>,
 'excellent': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab6b160>,
 'woman': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab6b208>,
 'as': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab6bac8>,
 'governess': <gensim.models.keyedvectors.Vocab at 0x7f4c1ab6b3c8>,
 'who': <gensim.models.keyedvectors.Vocab at 0x7f4c1ac88198>,
 'fallen': <gensim.models.keyedvectors.Vocab at 0x7f4c1ac88978>,
 'short': <gensim.models.keyedvectors.Vocab at 0x7f4c1ac88860>,
 'affection': <gensim.models.keyedvectors.Vocab at 0x7f4c1ac880f0>,
 'Sixteen': <gensim.models.keyedvectors.Vocab at 0x7f4c1ac88a20>,
 'Miss': <gensim.models.keyedvectors.Vocab at 0x7f4c1ac88e10>,
 'Taylor': <gensim.models.keyedvectors.Vocab at 0x7f4c1ac88f98>,
 'Mr': <gensim.models.keyedvectors.Vocab at 0x7f4c1ac886d8>,
 'family': <gensim.models.keyedvectors.Vocab at 0x7f4c1ac88ef0>,
 'less': <gensim.models.keyedvectors.Vocab at 0x7f4c1ac88780>,
 'friend': <gensim.models.keyedvectors.Vocab at 0x7f4c1ac88f60>,
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 'pray': <gensim.models.keyedvectors.Vocab at 0x7f4c1bef1438>,
 'silly': <gensim.models.keyedvectors.Vocab at 0x7f4c1a94c550>,
 'break': <gensim.models.keyedvectors.Vocab at 0x7f4c1a94c470>,
 'circle': <gensim.models.keyedvectors.Vocab at 0x7f4c1a94c080>,
 'grievously': <gensim.models.keyedvectors.Vocab at 0x7f4c1a94c828>,
 'Only': <gensim.models.keyedvectors.Vocab at 0x7f4c1a94c4e0>,
 'Elton': <gensim.models.keyedvectors.Vocab at 0x7f4c1a94cd30>,
 ',--': <gensim.models.keyedvectors.Vocab at 0x7f4c1a94c2e8>,
 'look': <gensim.models.keyedvectors.Vocab at 0x7f4c1a94cb70>,
 'whole': <gensim.models.keyedvectors.Vocab at 0x7f4c1a94cda0>,
 'fitted': <gensim.models.keyedvectors.Vocab at 0x7f4c1a94cac8>,
 'comfortably': <gensim.models.keyedvectors.Vocab at 0x7f4c1a94c710>,
 'shame': <gensim.models.keyedvectors.Vocab at 0x7f4c1a94c1d0>,
 'longer': <gensim.models.keyedvectors.Vocab at 0x7f4c1a94c780>,
 'joining': <gensim.models.keyedvectors.Vocab at 0x7f4c1a94c4a8>,
 'hands': <gensim.models.keyedvectors.Vocab at 0x7f4c1a94ccc0>,
 'same': <gensim.models.keyedvectors.Vocab at 0x7f4c1a94cd68>,
 'service': <gensim.models.keyedvectors.Vocab at 0x7f4c1a94c668>,
 'shew': <gensim.models.keyedvectors.Vocab at 0x7f4c1a94cf98>,
 'attention': <gensim.models.keyedvectors.Vocab at 0x7f4c1a94ca20>,
 'ask': <gensim.models.keyedvectors.Vocab at 0x7f4c1a94c320>,
 'dare': <gensim.models.keyedvectors.Vocab at 0x7f4c1a94c400>,
 'With': <gensim.models.keyedvectors.Vocab at 0x7f4c1a94cdd8>,
 'laughing': <gensim.models.keyedvectors.Vocab at 0x7f4c1a94c0b8>,
 'fish': <gensim.models.keyedvectors.Vocab at 0x7f4c1a94c358>,
 'chicken': <gensim.models.keyedvectors.Vocab at 0x7f4c1a94ceb8>,
 'chuse': <gensim.models.keyedvectors.Vocab at 0x7f4c1a94c3c8>,
 'Depend': <gensim.models.keyedvectors.Vocab at 0x7f4c1a94c048>,
 'six': <gensim.models.keyedvectors.Vocab at 0x7f4c1b90ceb8>,
 'care': <gensim.models.keyedvectors.Vocab at 0x7f4c1b90c780>,
 'II': <gensim.models.keyedvectors.Vocab at 0x7f4c1b90c630>,
 'native': <gensim.models.keyedvectors.Vocab at 0x7f4c1b90c208>,
 'born': <gensim.models.keyedvectors.Vocab at 0x7f4c1b90c978>,
 'respectable': <gensim.models.keyedvectors.Vocab at 0x7f4c1b90c7b8>,
 'generations': <gensim.models.keyedvectors.Vocab at 0x7f4c1b90c438>,
 'rising': <gensim.models.keyedvectors.Vocab at 0x7f4c1b90c828>,
 'gentility': <gensim.models.keyedvectors.Vocab at 0x7f4c1b90cb38>,
 'property': <gensim.models.keyedvectors.Vocab at 0x7f4c1b90c518>,
 'received': <gensim.models.keyedvectors.Vocab at 0x7f4c1b90c748>,
 'education': <gensim.models.keyedvectors.Vocab at 0x7f4c1b90c128>,
 'succeeding': <gensim.models.keyedvectors.Vocab at 0x7f4c1b90cb00>,
 'small': <gensim.models.keyedvectors.Vocab at 0x7f4c1b90ce10>,
 'become': <gensim.models.keyedvectors.Vocab at 0x7f4c1b90c9e8>,
 'indisposed': <gensim.models.keyedvectors.Vocab at 0x7f4c1b90c668>,
 'homely': <gensim.models.keyedvectors.Vocab at 0x7f4c1b90c160>,
 'pursuits': <gensim.models.keyedvectors.Vocab at 0x7f4c1b90cdd8>,
 'brothers': <gensim.models.keyedvectors.Vocab at 0x7f4c1be13208>,
 'engaged': <gensim.models.keyedvectors.Vocab at 0x7f4c1be13e10>,
 'satisfied': <gensim.models.keyedvectors.Vocab at 0x7f4c1be13e80>,
 'active': <gensim.models.keyedvectors.Vocab at 0x7f4c1be13a20>,
 'social': <gensim.models.keyedvectors.Vocab at 0x7f4c1be13978>,
 'entering': <gensim.models.keyedvectors.Vocab at 0x7f4c1be13a90>,
 'militia': <gensim.models.keyedvectors.Vocab at 0x7f4c1be13a58>,
 'county': <gensim.models.keyedvectors.Vocab at 0x7f4c1be13d30>,
 'embodied': <gensim.models.keyedvectors.Vocab at 0x7f4c1be136a0>,
 'Captain': <gensim.models.keyedvectors.Vocab at 0x7f4c1be13048>,
 'general': <gensim.models.keyedvectors.Vocab at 0x7f4c1be136d8>,
 'favourite': <gensim.models.keyedvectors.Vocab at 0x7f4c1be13b70>,
 'military': <gensim.models.keyedvectors.Vocab at 0x7f4c1be13828>,
 'introduced': <gensim.models.keyedvectors.Vocab at 0x7f4c1be13e48>,
 'Churchill': <gensim.models.keyedvectors.Vocab at 0x7f4c1be13780>,
 'Yorkshire': <gensim.models.keyedvectors.Vocab at 0x7f4c1be13160>,
 'fell': <gensim.models.keyedvectors.Vocab at 0x7f4c1be13c50>,
 'love': <gensim.models.keyedvectors.Vocab at 0x7f4c1be13dd8>,
 'surprized': <gensim.models.keyedvectors.Vocab at 0x7f4c1be13320>,
 'except': <gensim.models.keyedvectors.Vocab at 0x7f4c1be13eb8>,
 'full': <gensim.models.keyedvectors.Vocab at 0x7f4c1be130f0>,
 'pride': <gensim.models.keyedvectors.Vocab at 0x7f4c1be13630>,
 'importance': <gensim.models.keyedvectors.Vocab at 0x7f4c1be13cf8>,
 'connexion': <gensim.models.keyedvectors.Vocab at 0x7f4c1be13710>,
 'offend': <gensim.models.keyedvectors.Vocab at 0x7f4c1be13b38>,
 'command': <gensim.models.keyedvectors.Vocab at 0x7f4c1be134e0>,
 'bore': <gensim.models.keyedvectors.Vocab at 0x7f4c1be13358>,
 'proportion': <gensim.models.keyedvectors.Vocab at 0x7f4c1be139e8>,
 'estate': <gensim.models.keyedvectors.Vocab at 0x7f4c1be13390>,
 'took': <gensim.models.keyedvectors.Vocab at 0x7f4c1be135c0>,
 'infinite': <gensim.models.keyedvectors.Vocab at 0x7f4c1be13ac8>,
 'mortification': <gensim.models.keyedvectors.Vocab at 0x7f4c1be13c88>,
 'threw': <gensim.models.keyedvectors.Vocab at 0x7f4c1be13748>,
 'due': <gensim.models.keyedvectors.Vocab at 0x7f4c1be137f0>,
 'decorum': <gensim.models.keyedvectors.Vocab at 0x7f4c1be13da0>,
 'unsuitable': <gensim.models.keyedvectors.Vocab at 0x7f4c1be134a8>,
 'produce': <gensim.models.keyedvectors.Vocab at 0x7f4c1be8a860>,
 'ought': <gensim.models.keyedvectors.Vocab at 0x7f4c1be8ab38>,
 'whose': <gensim.models.keyedvectors.Vocab at 0x7f4c1be8ac88>,
 'warm': <gensim.models.keyedvectors.Vocab at 0x7f4c1be8aef0>,
 'sweet': <gensim.models.keyedvectors.Vocab at 0x7f4c1be8ab00>,
 'return': <gensim.models.keyedvectors.Vocab at 0x7f4c1be8ac50>,
 'goodness': <gensim.models.keyedvectors.Vocab at 0x7f4c1be8a080>,
 'spirit': <gensim.models.keyedvectors.Vocab at 0x7f4c1be8a9e8>,
 'resolution': <gensim.models.keyedvectors.Vocab at 0x7f4c1be8a7b8>,
 'pursue': <gensim.models.keyedvectors.Vocab at 0x7f4c1be8ad68>,
 'refrain': <gensim.models.keyedvectors.Vocab at 0x7f4c1be8a1d0>,
 'unreasonable': <gensim.models.keyedvectors.Vocab at 0x7f4c1be8a5f8>,
 'anger': <gensim.models.keyedvectors.Vocab at 0x7f4c1be8a6a0>,
 'missing': <gensim.models.keyedvectors.Vocab at 0x7f4c1be8a2b0>,
 'former': <gensim.models.keyedvectors.Vocab at 0x7f4c1be8ac18>,
 'income': <gensim.models.keyedvectors.Vocab at 0x7f4c1be8afd0>,
 'still': <gensim.models.keyedvectors.Vocab at 0x7f4c1be8aa58>,
 'comparison': <gensim.models.keyedvectors.Vocab at 0x7f4c1be8a550>,
 'Enscombe': <gensim.models.keyedvectors.Vocab at 0x7f4c1be8aa20>,
 'cease': <gensim.models.keyedvectors.Vocab at 0x7f4c1be8ada0>,
 'once': <gensim.models.keyedvectors.Vocab at 0x7f4c1be8aeb8>,
 'considered': <gensim.models.keyedvectors.Vocab at 0x7f4c1be8a710>,
 'especially': <gensim.models.keyedvectors.Vocab at 0x7f4c1be8a8d0>,
 'Churchills': <gensim.models.keyedvectors.Vocab at 0x7f4c1be8acc0>,
 'amazing': <gensim.models.keyedvectors.Vocab at 0x7f4c1be8a7f0>,
 'worst': <gensim.models.keyedvectors.Vocab at 0x7f4c1be8a278>,
 'bargain': <gensim.models.keyedvectors.Vocab at 0x7f4c1be8a9b0>,
 'poorer': <gensim.models.keyedvectors.Vocab at 0x7f4c1ac58828>,
 'child': <gensim.models.keyedvectors.Vocab at 0x7f4c1ac58518>,
 'From': <gensim.models.keyedvectors.Vocab at 0x7f4c1ac58ef0>,
 'expense': <gensim.models.keyedvectors.Vocab at 0x7f4c1beeae48>,
 'relieved': <gensim.models.keyedvectors.Vocab at 0x7f4c1beeadd8>,
 'boy': <gensim.models.keyedvectors.Vocab at 0x7f4c1beeae80>,
 'additional': <gensim.models.keyedvectors.Vocab at 0x7f4c1beea4e0>,
 'softening': <gensim.models.keyedvectors.Vocab at 0x7f4c1beea198>,
 'lingering': <gensim.models.keyedvectors.Vocab at 0x7f4c1beea358>,
 'illness': <gensim.models.keyedvectors.Vocab at 0x7f4c1beea438>,
 'reconciliation': <gensim.models.keyedvectors.Vocab at 0x7f4c1beea0b8>,
 'kindred': <gensim.models.keyedvectors.Vocab at 0x7f4c1beea2e8>,
 'offered': <gensim.models.keyedvectors.Vocab at 0x7f4c1beea2b0>,
 'charge': <gensim.models.keyedvectors.Vocab at 0x7f4c1beea1d0>,
 'Frank': <gensim.models.keyedvectors.Vocab at 0x7f4c1beea940>,
 'decease': <gensim.models.keyedvectors.Vocab at 0x7f4c1beea320>,
 'scruples': <gensim.models.keyedvectors.Vocab at 0x7f4c1beeacf8>,
 'reluctance': <gensim.models.keyedvectors.Vocab at 0x7f4c1beea908>,
 'supposed': <gensim.models.keyedvectors.Vocab at 0x7f4c1beea470>,
 'overcome': <gensim.models.keyedvectors.Vocab at 0x7f4c1beeac18>,
 'considerations': <gensim.models.keyedvectors.Vocab at 0x7f4c1beeaf60>,
 'wealth': <gensim.models.keyedvectors.Vocab at 0x7f4c1beeac88>,
 'seek': <gensim.models.keyedvectors.Vocab at 0x7f4c1beea3c8>,
 'improve': <gensim.models.keyedvectors.Vocab at 0x7f4c1beea400>,
 'complete': <gensim.models.keyedvectors.Vocab at 0x7f4c1beeabe0>,
 'became': <gensim.models.keyedvectors.Vocab at 0x7f4c1beeaba8>,
 'desirable': <gensim.models.keyedvectors.Vocab at 0x7f4c1beea518>,
 'quitted': <gensim.models.keyedvectors.Vocab at 0x7f4c1beeab38>,
 'trade': <gensim.models.keyedvectors.Vocab at 0x7f4c1beea4a8>,
 'established': <gensim.models.keyedvectors.Vocab at 0x7f4c1beea898>,
 'favourable': <gensim.models.keyedvectors.Vocab at 0x7f4c1beea0f0>,
 ...}

















In [31]:



    


len(model.wv.vocab)



    


















    
Out[31]:








17011

















In [ ]:



    


# X = model[model.wv.vocab]



    











In [ ]:



    


# tsne = TSNE(n_components=2, n_iter=1000) # 200 is minimum iter; default is 1000



    











In [ ]:



    


# X_2d = tsne.fit_transform(X)



    











In [ ]:



    


# coords_df = pd.DataFrame(X_2d, columns=['x','y'])
# coords_df['token'] = model.wv.vocab.keys()



    











In [ ]:



    


# coords_df.to_csv('../raw_gutenberg_tsne.csv', index=False)

Visualize 2D representation of word vectors





In [32]:



    


coords_df = pd.read_csv('./raw_gutenberg_tsne.csv')



    











In [34]:



    


coords_df.head()



    


















    
Out[34]:










  
    

      
      x
      y
      token
    

  
  
    

      0
      4.736166
      0.330797
      [
    

    

      1
      2.382989
      -4.162857
      Emma
    

    

      2
      -5.468009
      -2.095312
      by
    

    

      3
      2.030853
      -4.465032
      Jane
    

    

      4
      4.746364
      0.328226
      ]
    

  




















In [33]:



    


_ = coords_df.plot.scatter('x', 'y', figsize=(12,12), marker='.', s=10, alpha=0.2)



    


















    
























In [35]:



    


output_notebook() # output bokeh plots inline in notebook



    


















    







    

        
        Loading BokehJS ...
    










    



























In [36]:



    


subset_df = coords_df.sample(n=5000)



    











In [37]:



    


p = figure(plot_width=800, plot_height=800)
_ = p.text(x=subset_df.x, y=subset_df.y, text=subset_df.token)



    











In [38]:



    


show(p)



    


















    









    












    



























In [ ]:

Content source: the-deep-learners/nyc-ds-academy

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