notebook.community

Edit and run 





In [1]:



    


import pandas as pd
import pickle
import numpy as np

# Load the bar review dataset 
review = pd.read_pickle('../output/bar_reviews_cleaned_and_tokenized_SF.pickle')
review.head(2)



    


















    
Out[1]:










  
    

      
      business_id
      date
      review_id
      stars
      text
      type
      user_id
      votes_cool
      votes_funny
      votes_useful
      cleaned_tokenized
    

  
  
    

      10
      UsFtqoBl7naz8AVUBZMjQQ
      2013-11-08
      Di3exaUCFNw1V4kSNW5pgA
      5
      All the food is great here. But the best thing...
      review
      uK8tzraOp4M5u3uYrqIBXg
      0.0
      0.0
      0.0
      [[food, great], [best, thing, wing], [wing, si...
    

    

      11
      UsFtqoBl7naz8AVUBZMjQQ
      2014-03-29
      0Lua2-PbqEQMjD9r89-asw
      3
      We checked this place out this past Monday for...
      review
      I_47G-R2_egp7ME5u_ltew
      0.0
      0.0
      0.0
      [[checked, place, past, monday, wing, night], ...
    

  




















In [2]:



    


import gensim
from itertools import chain
import sys
sys.path.append('../vectorsearch/')
import nltk_helper
import doc2vec
from gensim.models.doc2vec import TaggedDocument


n_epochs = 10
n_docs = -1 # -1 for almost all of them...

# Collapse each review to a 1D list of words.
review_flatten = [list(chain.from_iterable(doc)) for doc in review.cleaned_tokenized[:n_docs]]

# docs = [TaggedDocument(words, ['SENT_%i'%index,])
#                              for index, words in enumerate(review_flatten)]

docs = [TaggedDocument(words, [review.review_id.iloc[index]])
                             for index, words in enumerate(review_flatten)]


# A list of words for each review  
sentences = [doc.words for doc in docs]

print '\nFirst Doc: \n-----------------\n', docs[0]



    


















    






path /data/insight_yelp/input/

First Doc: 
-----------------
TaggedDocument(['food', 'great', 'best', 'thing', u'wing', u'wing', 'simply', 'fantastic', 'wet', 'cajun', 'best', 'most', 'popular', 'also', 'like', 'seasoned', 'salt', u'wing', 'wing', 'night', 'monday', 'wednesday', 'night', '075', 'whole', u'wing', 'dining', 'area', 'nice', 'very', 'family', 'friendly', 'bar', 'very', 'nice', 'well', 'place', 'truly', 'yinzers', 'dream', 'pittsburgh', 'dad', 'would', 'love', 'place', 'nat', u'all-the-food', u'the-best-thing', u'their-wings', u'their-wings', u'wing-night', u'the-dining-area', u'the-bar', u'this-place', u'this-place'], [u'Di3exaUCFNw1V4kSNW5pgA'])

















In [ ]:



    


import copy 

model = doc2vec.Doc2Vec(min_count=3, window=6, size=100, sample=1e-4, negative=10, workers=12)
# Build the vocab from list of sentences.
model.build_vocab(docs) 
# Useful for training.  Get shuffled during training.



    


















    






























In [ ]:



    


from random import shuffle

for epoch in range(10):
    print '\rTraining Epoch %i, alpha %1.4f'%(epoch+1, model.alpha),
    #model.train(np.random.permutation(docs))
    shuffle(docs)
    model.train(docs)
    model.alpha -= 0.001 # decrease the learning rate
    model.min_alpha = model.alpha # fix the learning rate, no decay

model.init_sims(replace=True) 


# # Normalize the word vectors.
# vec_norms = np.sqrt(np.sum(model.syn0**2, axis=1))
# model.syn0 = (model.syn0/vec_norms[:, numpy.newaxis])
# # Normalize the doc vectors.
# vec_norms = np.sqrt(np.sum(model.docvecs.doctag_syn0**2, axis=1))
# model.docvecs.doctag_syn0 = (model.docvecs.doctag_syn0/vec_norms[:, numpy.newaxis])



    


















    






Training Epoch 1, alpha 0.0250
























In [ ]:



    


model.save('../output/doc2vec_bars.model')



    



















In [101]:



    


# Can find similar documents..
print model.docvecs.most_similar(positive=docs[0][1]), '\n'

# Can find similar words...
print model.most_similar(positive=['beer']), '\n'

# Can find documents that are most similar to keywords.... 
print model.docvecs.most_similar(positive=[model['beer'], model['music']]), '\n'

# Can find words that are most common in documents
print " ".join(docs[0][0])
print model.most_similar(positive=[model.docvecs[docs[0][1][0]], ]), '\n'



    


















    






[(u'MNczjKfOZ8VD4Q9YzWNBbQ', 0.8875299692153931), (u'QcAMcIZgS4_gtU18GaWjiA', 0.8867901563644409), (u'DkqhyLlc7nkt7Du0RYfz3w', 0.8855961561203003), (u'yM4tNnuNuiH7MU54Ul128Q', 0.8844144344329834), (u'oyPN_upWhqekHRGIpKwp4g', 0.883427083492279), (u'_TlCk3xdz8RQCBQJAM55ZA', 0.8828277587890625), (u'gHKXQT4xVWohNGkCR6swog', 0.8823326230049133), (u'32dbp8Tkc1DvgTMZuXH7lQ', 0.8809983730316162), (u'JXILRnFAOBauVnDibjGrDA', 0.8803400993347168), (u'vXfyHC4vS6uyL7r2wBk4UA', 0.8800426125526428)] 

[('food', 0.9956748485565186), ('at-least', 0.9953977465629578), ('pittsburgh', 0.9950786828994751), ('not', 0.994979202747345), ('menu', 0.9948906898498535), ('dont', 0.9948253631591797), ('place', 0.9948084354400635), ('very', 0.9947399497032166), ('try', 0.9947223663330078), ('nice', 0.9946841597557068)] 

[(u'MsoASFxCmOOkOBOqZ80ngQ', 0.9963570833206177), (u'hnnT9vXu-m2PZeOKpqX-6Q', 0.9949337244033813), (u'c_OfFAXTywYkhsV4DgTpSw', 0.9945775270462036), (u'pKcQEu1QMRLWRuBBLqFU3A', 0.9941195249557495), (u'7idP8cvvVS7yKab8iTdqyw', 0.9940100908279419), (u'GdGz5w7Dx0CASRxkoktqvw', 0.9939402937889099), (u'uf61rPucuICXhSPXlZ1hIQ', 0.9938896894454956), (u'qnQoXQ5l3GkY_iFx-mO9PA', 0.9938743114471436), (u'39XuVN28H0eMmdvWHXaMTw', 0.9938485026359558), (u'7GAcFg4nSnf0OqeAXRxekw', 0.9937247037887573)] 

food great best thing wing wing simply fantastic wet cajun best most popular also like seasoned salt wing wing-night monday wednesday night 075 whole wing dining area nice very family friendly bar very nice well place truly yinzers dream pittsburgh dad would love place nat
[('website', 0.8903827667236328), ('literally', 0.8869487643241882), (u'wall', 0.8863758444786072), ('worse', 0.8859225511550903), ('toasted', 0.8841910362243652), (u'sport', 0.8837718963623047), ('playing', 0.883684515953064), ('sing', 0.8822931051254272), ('picture', 0.8821819424629211), ('stopping', 0.8820637464523315)] 








































In [136]:



    






    


















    






set([u'UsFtqoBl7naz8AVUBZMjQQ', u'mVHrayjG3uZ_RLHkLj-AMg'])
[-0.0038604951, -0.00059710076]

























In [104]:



    


# print model.docvecs['KUinHkKyGhznElgIzx0yIw']*2
# print get_mean_doc_vector(['KUinHkKyGhznElgIzx0yIw', 'KUinHkKyGhznElgIzx0yIw'], model)

for rev in review.review_id.iloc[:10]:
    print rev
    print np.dot(model.docvecs[rev], model.docvecs[rev])



    


















    






Di3exaUCFNw1V4kSNW5pgA
0.0042853
0Lua2-PbqEQMjD9r89-asw
0.0122367
7N9j5YbBHBW6qguE5DAeyA
0.00195272
mjCJR33jvUNt41iJCxDU_g
0.00155523
6w6gMZ3iBLGcUM4RBIuifQ
0.0221827
jVVv_DA5mCDB6mediuwHAw
0.00289149
3Es8GsjkssusYgeU6_ZVpQ
0.0126018
KAkcn7oQP1xX8KsZ-XmktA
0.00472332
BZNJkkP0bXnwQ2-sCqat2Q
0.014506
VDTIbR3G5_IPkpXbo2MutA
0.00847885

























In [1]:



    


for word, sim in model.most_similar('beer'):
    print np.dot(model[word], model[word])



    


















    







NameErrorTraceback (most recent call last)
<ipython-input-1-3f7c4f3ce9aa> in <module>()
----> 1 for word, sim in model.most_similar('beer'):
      2     print np.dot(model[word], model[word])

NameError: name 'model' is not defined
























In [78]:



    


for key in model.vocab.keys():
    model[key]



    


















    
Out[78]:








{'fawn': <gensim.models.word2vec.Vocab at 0x7f994aeb0bd0>,
 'raining': <gensim.models.word2vec.Vocab at 0x7f996d984e90>,
 'bypassed': <gensim.models.word2vec.Vocab at 0x7f996d2de450>,
 'cussed': <gensim.models.word2vec.Vocab at 0x7f99be5c1650>,
 'blackend': <gensim.models.word2vec.Vocab at 0x7f99be5c1e50>,
 '5-diamond': <gensim.models.word2vec.Vocab at 0x7f99be5c1290>,
 'yellow': <gensim.models.word2vec.Vocab at 0x7f99be5c1c50>,
 'four': <gensim.models.word2vec.Vocab at 0x7f99be5c12d0>,
 'prefix': <gensim.models.word2vec.Vocab at 0x7f99be5c1150>,
 'deelish': <gensim.models.word2vec.Vocab at 0x7f99656b1d50>,
 'hanging': <gensim.models.word2vec.Vocab at 0x7f99be5c1d90>,
 'bistroid': <gensim.models.word2vec.Vocab at 0x7f99be5c1a50>,
 'woody': <gensim.models.word2vec.Vocab at 0x7f99be5c1c90>,
 'aggression': <gensim.models.word2vec.Vocab at 0x7f99be5c1950>,
 'conjure': <gensim.models.word2vec.Vocab at 0x7f99be5c1dd0>,
 'frou-frou': <gensim.models.word2vec.Vocab at 0x7f99be5c1f10>,
 'crooned': <gensim.models.word2vec.Vocab at 0x7f9965209ad0>,
 'frisee': <gensim.models.word2vec.Vocab at 0x7f996d5f00d0>,
 'fiddling': <gensim.models.word2vec.Vocab at 0x7f996576b590>,
 'eligible': <gensim.models.word2vec.Vocab at 0x7f99be5c1210>,
 'electricity': <gensim.models.word2vec.Vocab at 0x7f99be5c1590>,
 'mid-week': <gensim.models.word2vec.Vocab at 0x7f99be5c1f90>,
 'scold': <gensim.models.word2vec.Vocab at 0x7f99be5c1550>,
 'unanswered': <gensim.models.word2vec.Vocab at 0x7f996d4ca150>,
 'gab': <gensim.models.word2vec.Vocab at 0x7f9965646a10>,
 'originality': <gensim.models.word2vec.Vocab at 0x7f996d4ca310>,
 'opener': <gensim.models.word2vec.Vocab at 0x7f996d125890>,
 'prix-fixe': <gensim.models.word2vec.Vocab at 0x7f996d4ca790>,
 u'crooner': <gensim.models.word2vec.Vocab at 0x7f996d4ca510>,
 'lore': <gensim.models.word2vec.Vocab at 0x7f996d4ca610>,
 'lord': <gensim.models.word2vec.Vocab at 0x7f996d4ca690>,
 'immature': <gensim.models.word2vec.Vocab at 0x7f996d4ca090>,
 'inclement': <gensim.models.word2vec.Vocab at 0x7f996d5a1cd0>,
 'swivel': <gensim.models.word2vec.Vocab at 0x7f996d4ca750>,
 'hormone': <gensim.models.word2vec.Vocab at 0x7f996d4ca290>,
 'shielding': <gensim.models.word2vec.Vocab at 0x7f99653c5950>,
 'hostest': <gensim.models.word2vec.Vocab at 0x7f996d425750>,
 'deli': <gensim.models.word2vec.Vocab at 0x7f996d4ca650>,
 'diehard': <gensim.models.word2vec.Vocab at 0x7f99656d2450>,
 'regional': <gensim.models.word2vec.Vocab at 0x7f996d4ca5d0>,
 'costume': <gensim.models.word2vec.Vocab at 0x7f996d44f390>,
 u'dell': <gensim.models.word2vec.Vocab at 0x7f996d4ca590>,
 'like-say': <gensim.models.word2vec.Vocab at 0x7f996d4ca550>,
 'fattiness': <gensim.models.word2vec.Vocab at 0x7f996d4ca4d0>,
 u'hdtv': <gensim.models.word2vec.Vocab at 0x7f996d4ca450>,
 'tantalizing': <gensim.models.word2vec.Vocab at 0x7f996d4ca410>,
 'leisurely': <gensim.models.word2vec.Vocab at 0x7f996d4ca390>,
 'low-lighting': <gensim.models.word2vec.Vocab at 0x7f996d4ca350>,
 'fur': <gensim.models.word2vec.Vocab at 0x7f99657108d0>,
 'stabbed': <gensim.models.word2vec.Vocab at 0x7f996d4ca250>,
 'roofie': <gensim.models.word2vec.Vocab at 0x7f99656c7390>,
 'bringing': <gensim.models.word2vec.Vocab at 0x7f996d4ca210>,
 'number-1': <gensim.models.word2vec.Vocab at 0x7f996d4ca1d0>,
 'soba': <gensim.models.word2vec.Vocab at 0x7f9965646b90>,
 'tcby': <gensim.models.word2vec.Vocab at 0x7f996d4ca0d0>,
 'disturb': <gensim.models.word2vec.Vocab at 0x7f996d4ca050>,
 'internally': <gensim.models.word2vec.Vocab at 0x7f99be5d6210>,
 u'prize': <gensim.models.word2vec.Vocab at 0x7f99be5d6550>,
 'broiler': <gensim.models.word2vec.Vocab at 0x7f99be5d6410>,
 'obstruction': <gensim.models.word2vec.Vocab at 0x7f99654994d0>,
 'wooden': <gensim.models.word2vec.Vocab at 0x7f99be5d64d0>,
 'clientele': <gensim.models.word2vec.Vocab at 0x7f99be5d6510>,
 'aside-from': <gensim.models.word2vec.Vocab at 0x7f99be5d6050>,
 'voyeur': <gensim.models.word2vec.Vocab at 0x7f99be5d63d0>,
 'wednesday': <gensim.models.word2vec.Vocab at 0x7f99be5d65d0>,
 'piling': <gensim.models.word2vec.Vocab at 0x7f99be5d62d0>,
 'broiled': <gensim.models.word2vec.Vocab at 0x7f99be5d6450>,
 'stars-': <gensim.models.word2vec.Vocab at 0x7f99be5d6390>,
 'crotch': <gensim.models.word2vec.Vocab at 0x7f99be5d6310>,
 'succession': <gensim.models.word2vec.Vocab at 0x7f99be5d6290>,
 'stereotypical': <gensim.models.word2vec.Vocab at 0x7f99be5d6190>,
 'path': <gensim.models.word2vec.Vocab at 0x7f996587b550>,
 'merengue': <gensim.models.word2vec.Vocab at 0x7f996d300fd0>,
 u'fritter': <gensim.models.word2vec.Vocab at 0x7f99649b3950>,
 'wellmy': <gensim.models.word2vec.Vocab at 0x7f99657cae90>,
 'glassy': <gensim.models.word2vec.Vocab at 0x7f99be5d61d0>,
 'whether-or-not': <gensim.models.word2vec.Vocab at 0x7f996d9365d0>,
 'nigh': <gensim.models.word2vec.Vocab at 0x7f996d9369d0>,
 'tired': <gensim.models.word2vec.Vocab at 0x7f996d936b10>,
 'miller': <gensim.models.word2vec.Vocab at 0x7f996d936490>,
 'cordially': <gensim.models.word2vec.Vocab at 0x7f996d9364d0>,
 'preface': <gensim.models.word2vec.Vocab at 0x7f996d936790>,
 'bacon': <gensim.models.word2vec.Vocab at 0x7f996d936450>,
 'pulse': <gensim.models.word2vec.Vocab at 0x7f996d936c10>,
 'elegant': <gensim.models.word2vec.Vocab at 0x7f9965206210>,
 'second': <gensim.models.word2vec.Vocab at 0x7f996d9367d0>,
 'crisply': <gensim.models.word2vec.Vocab at 0x7f996d936410>,
 '275': <gensim.models.word2vec.Vocab at 0x7f996d936d10>,
 'perfectnot': <gensim.models.word2vec.Vocab at 0x7f996d125f10>,
 'sailed': <gensim.models.word2vec.Vocab at 0x7f996d9368d0>,
 'scraped': <gensim.models.word2vec.Vocab at 0x7f996d936290>,
 'snuggled': <gensim.models.word2vec.Vocab at 0x7f996d936fd0>,
 'theory': <gensim.models.word2vec.Vocab at 0x7f99657cafd0>,
 'blouse': <gensim.models.word2vec.Vocab at 0x7f996d936b50>,
 'hilariously': <gensim.models.word2vec.Vocab at 0x7f996d2feb90>,
 'vegetarianvegan': <gensim.models.word2vec.Vocab at 0x7f996d936a50>,
 'thunder': <gensim.models.word2vec.Vocab at 0x7f996d936c90>,
 'cooking': <gensim.models.word2vec.Vocab at 0x7f996d936f50>,
 'pittsburghers': <gensim.models.word2vec.Vocab at 0x7f996d936e50>,
 'roadhouse': <gensim.models.word2vec.Vocab at 0x7f996d936d90>,
 'up-side': <gensim.models.word2vec.Vocab at 0x7f996d936d50>,
 'negated': <gensim.models.word2vec.Vocab at 0x7f996d936bd0>,
 'marching': <gensim.models.word2vec.Vocab at 0x7f996d936b90>,
 u'groupie': <gensim.models.word2vec.Vocab at 0x7f996d936a90>,
 'rent-out': <gensim.models.word2vec.Vocab at 0x7f996575a150>,
 'pressed': <gensim.models.word2vec.Vocab at 0x7f996d936a10>,
 'attention-to-detail': <gensim.models.word2vec.Vocab at 0x7f996d936950>,
 u'crouch': <gensim.models.word2vec.Vocab at 0x7f996d936910>,
 'incomprehensible': <gensim.models.word2vec.Vocab at 0x7f996585a0d0>,
 'shocked': <gensim.models.word2vec.Vocab at 0x7f996d621b90>,
 'donalds': <gensim.models.word2vec.Vocab at 0x7f996d936810>,
 u'herb': <gensim.models.word2vec.Vocab at 0x7f99658218d0>,
 'depend-on': <gensim.models.word2vec.Vocab at 0x7f9965662dd0>,
 'interrupting': <gensim.models.word2vec.Vocab at 0x7f996d936710>,
 'jasmine': <gensim.models.word2vec.Vocab at 0x7f996d9366d0>,
 'swag': <gensim.models.word2vec.Vocab at 0x7f996d5fce10>,
 u'here': <gensim.models.word2vec.Vocab at 0x7f996d936650>,
 u'herd': <gensim.models.word2vec.Vocab at 0x7f996d936610>,
 'reported': <gensim.models.word2vec.Vocab at 0x7f996d936590>,
 'ching': <gensim.models.word2vec.Vocab at 0x7f996d936550>,
 'tobin': <gensim.models.word2vec.Vocab at 0x7f99658a9dd0>,
 'china': <gensim.models.word2vec.Vocab at 0x7f996d936250>,
 'hulk': <gensim.models.word2vec.Vocab at 0x7f99657cf190>,
 'dorm': <gensim.models.word2vec.Vocab at 0x7f996d936210>,
 'affiliated': <gensim.models.word2vec.Vocab at 0x7f996d936190>,
 'doro': <gensim.models.word2vec.Vocab at 0x7f996d9360d0>,
 'tatum': <gensim.models.word2vec.Vocab at 0x7f996d936090>,
 'dominic': <gensim.models.word2vec.Vocab at 0x7f996d936350>,
 'dork': <gensim.models.word2vec.Vocab at 0x7f996d936ad0>,
 u'buddy': <gensim.models.word2vec.Vocab at 0x7f9965499810>,
 'natured': <gensim.models.word2vec.Vocab at 0x7f996d936310>,
 'gobut': <gensim.models.word2vec.Vocab at 0x7f996d4254d0>,
 'substance': <gensim.models.word2vec.Vocab at 0x7f996d6a99d0>,
 'uplifting': <gensim.models.word2vec.Vocab at 0x7f996d6a9910>,
 'sloshed': <gensim.models.word2vec.Vocab at 0x7f99657cf1d0>,
 'lychee': <gensim.models.word2vec.Vocab at 0x7f9965214d50>,
 'elaborate': <gensim.models.word2vec.Vocab at 0x7f996d6a9f50>,
 'climbed': <gensim.models.word2vec.Vocab at 0x7f996d6a9dd0>,
 'oasis': <gensim.models.word2vec.Vocab at 0x7f99657cf210>,
 'oxymoron': <gensim.models.word2vec.Vocab at 0x7f996d6a9650>,
 'snowing': <gensim.models.word2vec.Vocab at 0x7f996d6a9c10>,
 'kidd': <gensim.models.word2vec.Vocab at 0x7f996d6a9790>,
 'transfixed': <gensim.models.word2vec.Vocab at 0x7f996d6a9490>,
 'dimness': <gensim.models.word2vec.Vocab at 0x7f996d6a9550>,
 'military': <gensim.models.word2vec.Vocab at 0x7f996d6a9610>,
 'pullin': <gensim.models.word2vec.Vocab at 0x7f996d6a9290>,
 'spotty': <gensim.models.word2vec.Vocab at 0x7f996d6a94d0>,
 'delicacy': <gensim.models.word2vec.Vocab at 0x7f9965886cd0>,
 'cancellation': <gensim.models.word2vec.Vocab at 0x7f996d6a9ad0>,
 u'mainstay': <gensim.models.word2vec.Vocab at 0x7f99657487d0>,
 u'patrick': <gensim.models.word2vec.Vocab at 0x7f9965781910>,
 'criticism': <gensim.models.word2vec.Vocab at 0x7f996d6a9cd0>,
 'appropriately': <gensim.models.word2vec.Vocab at 0x7f996d6a9c90>,
 u'roadrunner': <gensim.models.word2vec.Vocab at 0x7f996d6a9fd0>,
 'attracted': <gensim.models.word2vec.Vocab at 0x7f99657cac10>,
 'projection': <gensim.models.word2vec.Vocab at 0x7f996d6a9f90>,
 'magically': <gensim.models.word2vec.Vocab at 0x7f996d6a9e90>,
 'owed': <gensim.models.word2vec.Vocab at 0x7f996d6a9e50>,
 'remodeled': <gensim.models.word2vec.Vocab at 0x7f9965499950>,
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 'disguise': <gensim.models.word2vec.Vocab at 0x7f996d7465d0>,
 'exclaiming': <gensim.models.word2vec.Vocab at 0x7f996d746690>,
 'any-longer': <gensim.models.word2vec.Vocab at 0x7f996d7466d0>,
 'rotates': <gensim.models.word2vec.Vocab at 0x7f996d746710>,
 'rushing': <gensim.models.word2vec.Vocab at 0x7f996d746750>,
 'amusingly': <gensim.models.word2vec.Vocab at 0x7f996d746790>,
 'previous': <gensim.models.word2vec.Vocab at 0x7f996d7467d0>,
 'hah': <gensim.models.word2vec.Vocab at 0x7f996d746810>,
 'alum': <gensim.models.word2vec.Vocab at 0x7f996d746850>,
 'handshake': <gensim.models.word2vec.Vocab at 0x7f996d746890>,
 'enters': <gensim.models.word2vec.Vocab at 0x7f996d7468d0>,
 'chihuly': <gensim.models.word2vec.Vocab at 0x7f99657d7a10>,
 u'han': <gensim.models.word2vec.Vocab at 0x7f996d746990>,
 'out-of-town': <gensim.models.word2vec.Vocab at 0x7f996d746a10>,
 'old-fashioned': <gensim.models.word2vec.Vocab at 0x7f996d746a90>,
 'emphasis': <gensim.models.word2vec.Vocab at 0x7f996d367690>,
 'pub-like': <gensim.models.word2vec.Vocab at 0x7f996d746b50>,
 'haz': <gensim.models.word2vec.Vocab at 0x7f996d746bd0>,
 'richie': <gensim.models.word2vec.Vocab at 0x7f996d746c10>,
 'easy': <gensim.models.word2vec.Vocab at 0x7f996d746c50>,
 'prison': <gensim.models.word2vec.Vocab at 0x7f996d746c90>,
 u'enchilada': <gensim.models.word2vec.Vocab at 0x7f996d746cd0>,
 'bnb': <gensim.models.word2vec.Vocab at 0x7f996d2be9d0>,
 'har': <gensim.models.word2vec.Vocab at 0x7f996d746d50>,
 'east': <gensim.models.word2vec.Vocab at 0x7f996d746d90>,
 'hat': <gensim.models.word2vec.Vocab at 0x7f996d746dd0>,
 '20-25': <gensim.models.word2vec.Vocab at 0x7f996d746e10>,
 'prententious': <gensim.models.word2vec.Vocab at 0x7f996d746e90>,
 'casually': <gensim.models.word2vec.Vocab at 0x7f996d746ed0>,
 'quirk': <gensim.models.word2vec.Vocab at 0x7f996d746f10>,
 'survival': <gensim.models.word2vec.Vocab at 0x7f996d746f90>,
 'daytona': <gensim.models.word2vec.Vocab at 0x7f996d143850>,
 u'obscenity': <gensim.models.word2vec.Vocab at 0x7f996d746fd0>,
 '1150': <gensim.models.word2vec.Vocab at 0x7f99c7d46910>,
 'sad-face': <gensim.models.word2vec.Vocab at 0x7f996d75f090>,
 'gristle': <gensim.models.word2vec.Vocab at 0x7f9965848c10>,
 'possibly': <gensim.models.word2vec.Vocab at 0x7f996d75f0d0>,
 'otherworldly': <gensim.models.word2vec.Vocab at 0x7f996d75f150>,
 'indicative': <gensim.models.word2vec.Vocab at 0x7f996d75f190>,
 'birth': <gensim.models.word2vec.Vocab at 0x7f996d75f1d0>,
 'sorority': <gensim.models.word2vec.Vocab at 0x7f996575a8d0>,
 'unlikely': <gensim.models.word2vec.Vocab at 0x7f996d45ff10>,
 'imposed': <gensim.models.word2vec.Vocab at 0x7f996d300310>,
 'shadow': <gensim.models.word2vec.Vocab at 0x7f996d75f290>,
 'unique': <gensim.models.word2vec.Vocab at 0x7f996d75f2d0>,
 ...}

























In [107]:



    


model.syn0.shape



    


















    
Out[107]:








(7626, 100)

























In [108]:



    


model.syn0.shape



    


















    
Out[108]:








(7626, 100)

























In [109]:



    






    



















In [110]:



    


model['beer']



    


















    
Out[110]:








array([-0.11914355, -0.00848406, -0.25816774,  0.05004114, -0.13797277,
       -0.07304802,  0.07144317, -0.00289828,  0.00988765, -0.0521464 ,
       -0.02484666, -0.05874219,  0.16049536, -0.00942174,  0.15264362,
        0.0524993 ,  0.01947534, -0.09520859, -0.04392029, -0.17615482,
       -0.19804238, -0.09196329, -0.0707287 ,  0.17948456,  0.03011344,
       -0.13420026, -0.07682815, -0.06674536, -0.06478921,  0.05593799,
       -0.22613293,  0.01537087,  0.05933586, -0.07667404,  0.08785174,
       -0.04805563, -0.10572113,  0.01903196,  0.06023778, -0.15284617,
        0.00767768,  0.07501449, -0.01899237,  0.11723676, -0.00583145,
       -0.04982071,  0.04778826, -0.11280042,  0.0237955 ,  0.08502073,
       -0.04627986, -0.0168891 , -0.06349624, -0.01229718, -0.02730742,
        0.14085445, -0.0117617 , -0.00330494, -0.06652286,  0.0688545 ,
        0.09140737,  0.06150964,  0.1517535 , -0.10411895,  0.29116744,
       -0.10556895, -0.12878124,  0.05843589,  0.13523191, -0.03235988,
       -0.05662379, -0.02573927,  0.11005671,  0.06786436, -0.08925769,
        0.10860933,  0.05802899, -0.10399682,  0.10902008,  0.11846616,
        0.02740206,  0.0593122 , -0.01810796, -0.04139902,  0.07716142,
       -0.03177017,  0.01237178, -0.21982144,  0.19788279, -0.06397597,
       -0.14181791,  0.00949997, -0.09941574, -0.09559454, -0.01750667,
        0.15215348,  0.11657657,  0.16803506, -0.04015172,  0.02486112], dtype=float32)

























In [112]:



    


model.most_similar(['beer'])



    


















    
Out[112]:








[('food', 0.9956748485565186),
 ('at-least', 0.9953977465629578),
 ('pittsburgh', 0.9950786828994751),
 ('not', 0.994979202747345),
 ('menu', 0.9948906898498535),
 ('dont', 0.9948253631591797),
 ('place', 0.9948084354400635),
 ('very', 0.9947399497032166),
 ('try', 0.9947223663330078),
 ('nice', 0.9946841597557068)]

























In [ ]:



    






    
















    


    

        

            

            

                Content source: erccarls/vectorsearch
            

            
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