In this notebook, we create word vectors from a corpus of public-domain books, a selection from Project Gutenberg.
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] Package punkt is already up-to-date!
Out[2]:
True
In [3]:
nltk.download('gutenberg')
[nltk_data] Downloading package gutenberg to /home/jovyan/nltk_data...
[nltk_data] Package gutenberg is already up-to-date!
Out[3]:
True
In [4]:
from nltk.corpus import gutenberg
In [6]:
len(gutenberg.fileids())
Out[6]:
18
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 [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'
In [16]:
# another convenient method that we don't immediately need:
gutenberg.words()
Out[16]:
['[', 'Emma', 'by', 'Jane', 'Austen', '1816', ']', ...]
In [17]:
# gutenberg.words() is analogous to the following line, which need not be run:
# word_tokenize(gutenberg.raw())
In [18]:
# our Gutenberg corpus is 2.6m words in length:
len(gutenberg.words())
Out[18]:
2621613
In [19]:
# model = Word2Vec(sentences=gberg_sents, size=64, sg=1, window=10, min_count=5, seed=42, workers=8)
In [20]:
# model.save('raw_gutenberg_model.w2v')
In [21]:
# skip re-training the model with the next line:
model = gensim.models.Word2Vec.load('raw_gutenberg_model.w2v')
In [22]:
model['dog']
Out[22]:
array([ -5.60053289e-01, -1.57095745e-01, -1.35954544e-01,
2.11573735e-01, -1.07136287e-01, 8.77310261e-02,
4.14559871e-01, 2.26700798e-01, 2.97906458e-01,
5.01078293e-02, 1.78846210e-01, 1.08067788e-01,
-4.35526311e-01, -4.25833821e-01, -2.53711820e-01,
7.52079263e-02, -5.83147258e-02, 3.74293029e-01,
-6.66874468e-01, 2.33097542e-02, 1.35770917e-01,
1.64983049e-01, -1.20256573e-01, 4.77150202e-01,
1.22489601e-01, 3.75590801e-01, 1.48467004e-01,
2.42270246e-01, 3.16664786e-03, -7.44531572e-01,
-6.36309106e-03, 4.52868968e-01, 2.24508166e-01,
2.70326473e-02, 1.53943673e-01, 2.01649368e-01,
-1.73006937e-01, -6.66332617e-02, 1.03040524e-01,
-1.66178823e-01, 2.01654643e-01, 2.97852069e-01,
-8.91307294e-02, -3.24958622e-01, 8.13202839e-03,
1.84750110e-02, -1.02419108e-01, 1.29145041e-01,
1.63626075e-01, -2.15422258e-01, -2.92296857e-01,
-5.65005839e-01, -1.61351681e-01, -2.04182118e-02,
-2.52595842e-01, 4.30920184e-01, -1.48654059e-01,
4.11218196e-01, 6.62949324e-01, -5.52654732e-04,
-2.41263226e-01, -7.45767877e-02, -4.33727890e-01,
6.22240841e-01], dtype=float32)
In [23]:
len(model['dog'])
Out[23]:
64
In [24]:
model.most_similar('dog') # distance
Out[24]:
[('puppy', 0.8146849870681763),
('sweeper', 0.7880833148956299),
('broth', 0.7820672988891602),
('cage', 0.7675053477287292),
('pig', 0.7646769285202026),
('pet', 0.7610172033309937),
('fox', 0.7553994059562683),
('Truck', 0.7543582320213318),
('Lightfoot', 0.742825984954834),
('cow', 0.7392480373382568)]
In [25]:
model.most_similar('think')
Out[25]:
[('suppose', 0.8497018814086914),
('manage', 0.8408315181732178),
('know', 0.8337839841842651),
('contradict', 0.8264023661613464),
('interfere', 0.8262845277786255),
('NOW', 0.8262041211128235),
('Mamma', 0.8196532726287842),
('believe', 0.8150398135185242),
('shouldn', 0.8124459981918335),
('Williams', 0.8068450689315796)]
In [26]:
model.most_similar('day')
Out[26]:
[('morning', 0.8098249435424805),
('night', 0.7679969072341919),
('month', 0.7410775423049927),
('evening', 0.7385483384132385),
('time', 0.7339625358581543),
('week', 0.7182490825653076),
('feasting', 0.7168370485305786),
('Saturday', 0.7018452286720276),
('Adar', 0.6979250311851501),
('morrow', 0.6971378922462463)]
In [27]:
model.most_similar('father')
Out[27]:
[('mother', 0.8728880882263184),
('brother', 0.8333652019500732),
('sister', 0.7919358015060425),
('wife', 0.7858221530914307),
('daughter', 0.7800713181495667),
('Amnon', 0.7543610334396362),
('Tamar', 0.7502950429916382),
('servant', 0.7387294769287109),
('uncle', 0.7327381372451782),
('curseth', 0.7289719581604004)]
In [28]:
model.doesnt_match("mother father daughter dog".split())
Out[28]:
'dog'
In [29]:
model.similarity('father', 'dog')
Out[29]:
0.49294278479463099
In [30]:
# close, but not quite; distinctly in female direction:
model.most_similar(positive=['father', 'woman'], negative=['man'])
Out[30]:
[('daughter', 0.7782288193702698),
('sister', 0.777533769607544),
('mother', 0.7643921971321106),
('wife', 0.7588695287704468),
('husband', 0.7556524276733398),
('Tamar', 0.7055943012237549),
('Rachel', 0.7008661031723022),
('conceived', 0.6945146322250366),
('Sarah', 0.6943398714065552),
('brother', 0.6798239946365356)]
In [31]:
# more confident about this one:
model.most_similar(positive=['son', 'woman'], negative=['man'])
Out[31]:
[('daughter', 0.7534747123718262),
('Leah', 0.724845290184021),
('Hagar', 0.724432110786438),
('Rachel', 0.7239198684692383),
('conceived', 0.7235168218612671),
('Sarah', 0.7165275812149048),
('wife', 0.713229775428772),
('Rebekah', 0.6989840269088745),
('Caleb', 0.6943678259849548),
('Sarai', 0.6914916038513184)]
In [32]:
model.most_similar(positive=['husband', 'woman'], negative=['man'])
Out[32]:
[('wife', 0.708131730556488),
('sister', 0.6937975287437439),
('daughter', 0.68210768699646),
('child', 0.66499924659729),
('maid', 0.663989782333374),
('conceived', 0.6550735235214233),
('mother', 0.6408793926239014),
('nurse', 0.6267993450164795),
('elder', 0.6201719045639038),
('widow', 0.6188254952430725)]
In [33]:
model.most_similar(positive=['king', 'woman'], negative=['man'], topn=30)
Out[33]:
[('Rachel', 0.7381176352500916),
('Sarah', 0.7235167026519775),
('Laban', 0.7040327191352844),
('Leah', 0.703708291053772),
('Padanaram', 0.7029510736465454),
('Hagar', 0.6949448585510254),
('Rebekah', 0.6927774548530579),
('Pharaoh', 0.6861628293991089),
('Abram', 0.6841262578964233),
('Hamor', 0.6762512922286987),
('Shechem', 0.6723358631134033),
('daughter', 0.6721547842025757),
('damsel', 0.6675446033477783),
('Abimelech', 0.6638238430023193),
('Ephron', 0.6627167463302612),
('Bethuel', 0.6601616144180298),
('Esau', 0.6585861444473267),
('Solomon', 0.6583372354507446),
('Bilhah', 0.6578104496002197),
('household', 0.6573641300201416),
('Jerubbaal', 0.655084490776062),
('conceived', 0.6541304588317871),
('Judah', 0.652094304561615),
('Sarai', 0.649071991443634),
('Lot', 0.6466783881187439),
('Zilpah', 0.6465750932693481),
('birthright', 0.6449142694473267),
('queen', 0.644464910030365),
('David', 0.6373385190963745),
('Rahab', 0.6370617151260376)]
In [34]:
# impressive for such a small data set, without any cleaning, e.g., to lower case (covered next)
In [35]:
model.wv.vocab
Out[35]:
{'[': <gensim.models.keyedvectors.Vocab at 0x7fbfce6410f0>,
'Emma': <gensim.models.keyedvectors.Vocab at 0x7fbfce655da0>,
'by': <gensim.models.keyedvectors.Vocab at 0x7fbfce655b70>,
'Jane': <gensim.models.keyedvectors.Vocab at 0x7fbfce655a20>,
']': <gensim.models.keyedvectors.Vocab at 0x7fbfce655518>,
'I': <gensim.models.keyedvectors.Vocab at 0x7fbfce655550>,
'CHAPTER': <gensim.models.keyedvectors.Vocab at 0x7fbfce6558d0>,
'Woodhouse': <gensim.models.keyedvectors.Vocab at 0x7fbfce655f28>,
',': <gensim.models.keyedvectors.Vocab at 0x7fbfce655f98>,
'handsome': <gensim.models.keyedvectors.Vocab at 0x7fbfce655dd8>,
'clever': <gensim.models.keyedvectors.Vocab at 0x7fbfce655e80>,
'and': <gensim.models.keyedvectors.Vocab at 0x7fbfce655978>,
'rich': <gensim.models.keyedvectors.Vocab at 0x7fbfce655ef0>,
'with': <gensim.models.keyedvectors.Vocab at 0x7fbfce6559b0>,
'a': <gensim.models.keyedvectors.Vocab at 0x7fbfce655c88>,
'comfortable': <gensim.models.keyedvectors.Vocab at 0x7fbfce655828>,
'home': <gensim.models.keyedvectors.Vocab at 0x7fbfce655d68>,
'happy': <gensim.models.keyedvectors.Vocab at 0x7fbfce655588>,
'disposition': <gensim.models.keyedvectors.Vocab at 0x7fbfce6555c0>,
'seemed': <gensim.models.keyedvectors.Vocab at 0x7fbfce655630>,
'to': <gensim.models.keyedvectors.Vocab at 0x7fbfce6556a0>,
'unite': <gensim.models.keyedvectors.Vocab at 0x7fbfce655710>,
'some': <gensim.models.keyedvectors.Vocab at 0x7fbfce657048>,
'of': <gensim.models.keyedvectors.Vocab at 0x7fbfce6570b8>,
'the': <gensim.models.keyedvectors.Vocab at 0x7fbfce657128>,
'best': <gensim.models.keyedvectors.Vocab at 0x7fbfce657198>,
'blessings': <gensim.models.keyedvectors.Vocab at 0x7fbfce6571d0>,
'existence': <gensim.models.keyedvectors.Vocab at 0x7fbfce657208>,
';': <gensim.models.keyedvectors.Vocab at 0x7fbfce657240>,
'had': <gensim.models.keyedvectors.Vocab at 0x7fbfce6572b0>,
'lived': <gensim.models.keyedvectors.Vocab at 0x7fbfce657320>,
'nearly': <gensim.models.keyedvectors.Vocab at 0x7fbfce657390>,
'twenty': <gensim.models.keyedvectors.Vocab at 0x7fbfce657400>,
'-': <gensim.models.keyedvectors.Vocab at 0x7fbfce657438>,
'one': <gensim.models.keyedvectors.Vocab at 0x7fbfce6574a8>,
'years': <gensim.models.keyedvectors.Vocab at 0x7fbfce657518>,
'in': <gensim.models.keyedvectors.Vocab at 0x7fbfce657588>,
'world': <gensim.models.keyedvectors.Vocab at 0x7fbfce6575f8>,
'very': <gensim.models.keyedvectors.Vocab at 0x7fbfce657668>,
'little': <gensim.models.keyedvectors.Vocab at 0x7fbfce6576d8>,
'distress': <gensim.models.keyedvectors.Vocab at 0x7fbfce657710>,
'or': <gensim.models.keyedvectors.Vocab at 0x7fbfce657780>,
'vex': <gensim.models.keyedvectors.Vocab at 0x7fbfce6577f0>,
'her': <gensim.models.keyedvectors.Vocab at 0x7fbfce657860>,
'.': <gensim.models.keyedvectors.Vocab at 0x7fbfce657898>,
'She': <gensim.models.keyedvectors.Vocab at 0x7fbfce657908>,
'was': <gensim.models.keyedvectors.Vocab at 0x7fbfce657978>,
'youngest': <gensim.models.keyedvectors.Vocab at 0x7fbfce6579b0>,
'two': <gensim.models.keyedvectors.Vocab at 0x7fbfce657a20>,
'daughters': <gensim.models.keyedvectors.Vocab at 0x7fbfce657a58>,
'most': <gensim.models.keyedvectors.Vocab at 0x7fbfce657ac8>,
'affectionate': <gensim.models.keyedvectors.Vocab at 0x7fbfce657b00>,
'indulgent': <gensim.models.keyedvectors.Vocab at 0x7fbfce657b38>,
'father': <gensim.models.keyedvectors.Vocab at 0x7fbfce657ba8>,
'consequence': <gensim.models.keyedvectors.Vocab at 0x7fbfce657be0>,
'sister': <gensim.models.keyedvectors.Vocab at 0x7fbfce657c50>,
"'": <gensim.models.keyedvectors.Vocab at 0x7fbfce657c88>,
's': <gensim.models.keyedvectors.Vocab at 0x7fbfce657cc0>,
'marriage': <gensim.models.keyedvectors.Vocab at 0x7fbfce657cf8>,
'been': <gensim.models.keyedvectors.Vocab at 0x7fbfce657d68>,
'mistress': <gensim.models.keyedvectors.Vocab at 0x7fbfce657da0>,
'his': <gensim.models.keyedvectors.Vocab at 0x7fbfce657e10>,
'house': <gensim.models.keyedvectors.Vocab at 0x7fbfce657e80>,
'from': <gensim.models.keyedvectors.Vocab at 0x7fbfce657ef0>,
'early': <gensim.models.keyedvectors.Vocab at 0x7fbfce657f60>,
'period': <gensim.models.keyedvectors.Vocab at 0x7fbfce657fd0>,
'Her': <gensim.models.keyedvectors.Vocab at 0x7fbfce663080>,
'mother': <gensim.models.keyedvectors.Vocab at 0x7fbfce6630f0>,
'died': <gensim.models.keyedvectors.Vocab at 0x7fbfce663160>,
'too': <gensim.models.keyedvectors.Vocab at 0x7fbfce6631d0>,
'long': <gensim.models.keyedvectors.Vocab at 0x7fbfce663240>,
'ago': <gensim.models.keyedvectors.Vocab at 0x7fbfce6632b0>,
'for': <gensim.models.keyedvectors.Vocab at 0x7fbfce663320>,
'have': <gensim.models.keyedvectors.Vocab at 0x7fbfce663390>,
'more': <gensim.models.keyedvectors.Vocab at 0x7fbfce663400>,
'than': <gensim.models.keyedvectors.Vocab at 0x7fbfce663470>,
'an': <gensim.models.keyedvectors.Vocab at 0x7fbfce6634e0>,
'remembrance': <gensim.models.keyedvectors.Vocab at 0x7fbfce663518>,
'caresses': <gensim.models.keyedvectors.Vocab at 0x7fbfce663550>,
'place': <gensim.models.keyedvectors.Vocab at 0x7fbfce6635c0>,
'supplied': <gensim.models.keyedvectors.Vocab at 0x7fbfce6635f8>,
'excellent': <gensim.models.keyedvectors.Vocab at 0x7fbfce663630>,
'woman': <gensim.models.keyedvectors.Vocab at 0x7fbfce6636a0>,
'as': <gensim.models.keyedvectors.Vocab at 0x7fbfce663710>,
'governess': <gensim.models.keyedvectors.Vocab at 0x7fbfce663748>,
'who': <gensim.models.keyedvectors.Vocab at 0x7fbfce6637b8>,
'fallen': <gensim.models.keyedvectors.Vocab at 0x7fbfce663828>,
'short': <gensim.models.keyedvectors.Vocab at 0x7fbfce663898>,
'affection': <gensim.models.keyedvectors.Vocab at 0x7fbfce6638d0>,
'Sixteen': <gensim.models.keyedvectors.Vocab at 0x7fbfce663940>,
'Miss': <gensim.models.keyedvectors.Vocab at 0x7fbfce6639b0>,
'Taylor': <gensim.models.keyedvectors.Vocab at 0x7fbfce663a20>,
'Mr': <gensim.models.keyedvectors.Vocab at 0x7fbfce663a90>,
'family': <gensim.models.keyedvectors.Vocab at 0x7fbfce663b00>,
'less': <gensim.models.keyedvectors.Vocab at 0x7fbfce663b70>,
'friend': <gensim.models.keyedvectors.Vocab at 0x7fbfce663be0>,
'fond': <gensim.models.keyedvectors.Vocab at 0x7fbfce663c50>,
'both': <gensim.models.keyedvectors.Vocab at 0x7fbfce663cc0>,
'but': <gensim.models.keyedvectors.Vocab at 0x7fbfce663d30>,
'particularly': <gensim.models.keyedvectors.Vocab at 0x7fbfce663d68>,
'Between': <gensim.models.keyedvectors.Vocab at 0x7fbfce663dd8>,
'it': <gensim.models.keyedvectors.Vocab at 0x7fbfce663e48>,
'intimacy': <gensim.models.keyedvectors.Vocab at 0x7fbfce663e80>,
'sisters': <gensim.models.keyedvectors.Vocab at 0x7fbfce663ef0>,
'Even': <gensim.models.keyedvectors.Vocab at 0x7fbfce663f60>,
'before': <gensim.models.keyedvectors.Vocab at 0x7fbfce663fd0>,
'ceased': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703080>,
'hold': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7030f0>,
'office': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703160>,
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'entering': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc710>,
'militia': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc780>,
'county': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc7f0>,
'embodied': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc828>,
'Captain': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc898>,
'general': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc908>,
'favourite': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc940>,
'military': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc978>,
'introduced': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc9b0>,
'Churchill': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc9e8>,
'Yorkshire': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dca20>,
'fell': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dca90>,
'love': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dcb00>,
'surprized': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dcb38>,
'except': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dcba8>,
'full': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dcc18>,
'pride': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dcc88>,
'importance': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dccc0>,
'connexion': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dccf8>,
'offend': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dcd68>,
'command': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dcdd8>,
'bore': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dce48>,
'proportion': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dce80>,
'estate': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dcef0>,
'took': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dcf60>,
'infinite': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dcf98>,
'mortification': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dcfd0>,
'threw': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de080>,
'due': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de0f0>,
'decorum': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de160>,
'unsuitable': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de198>,
'produce': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de208>,
'ought': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de278>,
'whose': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de2e8>,
'warm': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de358>,
'sweet': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de3c8>,
'return': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de438>,
'goodness': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de470>,
'spirit': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de4e0>,
'resolution': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de518>,
'pursue': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de588>,
'refrain': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de5f8>,
'unreasonable': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de630>,
'anger': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de6a0>,
'missing': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de710>,
'former': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de780>,
'income': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de7f0>,
'still': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de860>,
'comparison': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de898>,
'Enscombe': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de8d0>,
'cease': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de940>,
'once': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de9b0>,
'considered': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de9e8>,
'especially': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dea20>,
'Churchills': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dea58>,
'amazing': <gensim.models.keyedvectors.Vocab at 0x7fbfce9deac8>,
'worst': <gensim.models.keyedvectors.Vocab at 0x7fbfce9deb38>,
'bargain': <gensim.models.keyedvectors.Vocab at 0x7fbfce9deba8>,
'poorer': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dec18>,
'child': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dec88>,
'From': <gensim.models.keyedvectors.Vocab at 0x7fbfce9decf8>,
'expense': <gensim.models.keyedvectors.Vocab at 0x7fbfce9ded68>,
'relieved': <gensim.models.keyedvectors.Vocab at 0x7fbfce9deda0>,
'boy': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dee10>,
'additional': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dee48>,
'softening': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dee80>,
'lingering': <gensim.models.keyedvectors.Vocab at 0x7fbfce9deeb8>,
'illness': <gensim.models.keyedvectors.Vocab at 0x7fbfce9def28>,
'reconciliation': <gensim.models.keyedvectors.Vocab at 0x7fbfce9def60>,
'kindred': <gensim.models.keyedvectors.Vocab at 0x7fbfce9defd0>,
'offered': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b7080>,
'charge': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b70f0>,
'Frank': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b7160>,
'decease': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b71d0>,
'scruples': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b7208>,
'reluctance': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b7240>,
'supposed': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b7278>,
'overcome': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b72b0>,
'considerations': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b72e8>,
'wealth': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b7358>,
'seek': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b73c8>,
'improve': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b7438>,
'complete': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b7470>,
'became': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b74e0>,
'desirable': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b7518>,
'quitted': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b7588>,
'trade': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b75f8>,
'established': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b7630>,
'favourable': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b7668>,
...}
In [36]:
len(model.wv.vocab)
Out[36]:
17011
In [37]:
X = model[model.wv.vocab]
In [38]:
tsne = TSNE(n_components=2, n_iter=1000) # 200 is minimum iter; default is 1000
In [39]:
X_2d = tsne.fit_transform(X)
In [40]:
X_2d[0:5]
Out[40]:
array([[ 3.93104606, -3.94591487],
[ 3.72858522, -3.21834929],
[ 3.11308347, -3.13188365],
[ 5.1765412 , 2.32805661],
[ 3.92752917, -3.95470727]])
In [41]:
# create DataFrame for storing results and plotting
coords_df = pd.DataFrame(X_2d, columns=['x','y'])
coords_df['token'] = model.wv.vocab.keys()
In [42]:
coords_df.head()
Out[42]:
x
y
token
0
3.931046
-3.945915
[
1
3.728585
-3.218349
Emma
2
3.113083
-3.131884
by
3
5.176541
2.328057
Jane
4
3.927529
-3.954707
]
In [43]:
# coords_df.to_csv('raw_gutenberg_tsne.csv', index=False)
In [44]:
coords_df = pd.read_csv('raw_gutenberg_tsne.csv')
In [45]:
_ = coords_df.plot.scatter('x', 'y', figsize=(12,12), marker='.', s=10, alpha=0.2)
In [47]:
subset_df = coords_df.sample(n=5000)
In [48]:
p = figure(plot_width=800, plot_height=800)
_ = p.text(x=subset_df.x, y=subset_df.y, text=subset_df.token)
In [49]:
show(p)
In [ ]:
Content source: the-deep-learners/TensorFlow-LiveLessons
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