In [2]:
import pickle
from gensim.models import Word2Vec
from gensim.models.word2vec import LineSentence
import numpy as np
import pandas as pd
import time
import gzip
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
In [3]:
%%time
import operator
import codecs
class MyDocuments(object):
def __init__(self, dirname):
self.dirname = dirname
def __iter__(self):
with gzip.open(self.dirname, 'rb') as f:
for line in f:
yield line.decode("utf-8").split('\t')[1].split()
CPU times: user 21.3 s, sys: 1.03 s, total: 22.3 s
Wall time: 24.2 s
In [61]:
%%time
def generate_w2v(corpus_path, word2vec_model_file):
print("starting epoche " + time.strftime("%H:%M:%S"))
# initiate the model and perform the first epoch of training
word2vec_model = Word2Vec(MyDocuments(corpus_path), size=100,
window=5, iter=5,
min_count=10, sg=1, workers=7)
word2vec_model.save(word2vec_model_file)
print("Finished epoche " + time.strftime("%H:%M:%S"))
print ("{} training epochs so far".format(word2vec_model.train_count))
print ("{:,} terms in the word2vec EOS vocabulary.".format(len(word2vec_model.wv.vocab)))
def load_w2v(word2vec_model_file):
# load the finished model from disk
word2vec_model = Word2Vec.load(word2vec_model_file)
word2vec_model.init_sims(replace=True)
return word2vec_model
CPU times: user 0 ns, sys: 0 ns, total: 0 ns
Wall time: 6.68 µs
In [62]:
%%time
# Load EOS processed corpus
corpus_path = 'data/eos/ngram/bigram_transformed_docs_%s.gz' % 'all'
print(corpus_path)
word2vec_model_file = 'data/eos/word2vec_model_all.model'
generate_w2v(corpus_path, word2vec_model_file)
2017-06-29 18:35:41,732 : INFO : collecting all words and their counts
2017-06-29 18:35:41,734 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types
data/eos/ngram/bigram_transformed_docs_all.gz
starting epoche 18:35:41
2017-06-29 18:35:42,578 : INFO : PROGRESS: at sentence #10000, processed 3121030 words, keeping 181409 word types
2017-06-29 18:35:43,399 : INFO : PROGRESS: at sentence #20000, processed 6246338 words, keeping 268855 word types
2017-06-29 18:35:44,260 : INFO : PROGRESS: at sentence #30000, processed 9444411 words, keeping 394971 word types
2017-06-29 18:35:45,118 : INFO : PROGRESS: at sentence #40000, processed 12668431 words, keeping 427671 word types
2017-06-29 18:35:45,935 : INFO : PROGRESS: at sentence #50000, processed 15833280 words, keeping 429247 word types
2017-06-29 18:35:46,730 : INFO : PROGRESS: at sentence #60000, processed 18929702 words, keeping 429877 word types
2017-06-29 18:35:47,606 : INFO : PROGRESS: at sentence #70000, processed 22160773 words, keeping 484261 word types
2017-06-29 18:35:48,510 : INFO : PROGRESS: at sentence #80000, processed 25390138 words, keeping 544776 word types
2017-06-29 18:35:49,335 : INFO : PROGRESS: at sentence #90000, processed 28427432 words, keeping 572959 word types
2017-06-29 18:35:50,108 : INFO : PROGRESS: at sentence #100000, processed 31354606 words, keeping 613450 word types
2017-06-29 18:35:50,887 : INFO : PROGRESS: at sentence #110000, processed 34297830 words, keeping 644826 word types
2017-06-29 18:35:51,663 : INFO : PROGRESS: at sentence #120000, processed 37236212 words, keeping 669737 word types
2017-06-29 18:35:52,477 : INFO : PROGRESS: at sentence #130000, processed 40288937 words, keeping 682793 word types
2017-06-29 18:35:53,370 : INFO : PROGRESS: at sentence #140000, processed 43549001 words, keeping 689432 word types
2017-06-29 18:35:54,265 : INFO : PROGRESS: at sentence #150000, processed 46781816 words, keeping 733751 word types
2017-06-29 18:35:55,117 : INFO : PROGRESS: at sentence #160000, processed 49991766 words, keeping 801465 word types
2017-06-29 18:35:55,973 : INFO : PROGRESS: at sentence #170000, processed 53194739 words, keeping 853616 word types
2017-06-29 18:35:56,837 : INFO : PROGRESS: at sentence #180000, processed 56453639 words, keeping 897638 word types
2017-06-29 18:35:57,685 : INFO : PROGRESS: at sentence #190000, processed 59652517 words, keeping 934721 word types
2017-06-29 18:35:58,528 : INFO : PROGRESS: at sentence #200000, processed 62816770 words, keeping 967536 word types
2017-06-29 18:35:59,383 : INFO : PROGRESS: at sentence #210000, processed 66020891 words, keeping 996829 word types
2017-06-29 18:36:00,246 : INFO : PROGRESS: at sentence #220000, processed 69242938 words, keeping 1026577 word types
2017-06-29 18:36:01,088 : INFO : PROGRESS: at sentence #230000, processed 72406754 words, keeping 1051366 word types
2017-06-29 18:36:01,976 : INFO : PROGRESS: at sentence #240000, processed 75638702 words, keeping 1055452 word types
2017-06-29 18:36:02,846 : INFO : PROGRESS: at sentence #250000, processed 78771323 words, keeping 1062677 word types
2017-06-29 18:36:03,544 : INFO : PROGRESS: at sentence #260000, processed 81361417 words, keeping 1081449 word types
2017-06-29 18:36:04,218 : INFO : PROGRESS: at sentence #270000, processed 83847827 words, keeping 1099302 word types
2017-06-29 18:36:04,892 : INFO : PROGRESS: at sentence #280000, processed 86323655 words, keeping 1115826 word types
2017-06-29 18:36:05,559 : INFO : PROGRESS: at sentence #290000, processed 88802739 words, keeping 1130695 word types
2017-06-29 18:36:06,237 : INFO : PROGRESS: at sentence #300000, processed 91297949 words, keeping 1144714 word types
2017-06-29 18:36:06,912 : INFO : PROGRESS: at sentence #310000, processed 93764487 words, keeping 1158463 word types
2017-06-29 18:36:07,583 : INFO : PROGRESS: at sentence #320000, processed 96249757 words, keeping 1170314 word types
2017-06-29 18:36:08,252 : INFO : PROGRESS: at sentence #330000, processed 98704821 words, keeping 1182350 word types
2017-06-29 18:36:08,927 : INFO : PROGRESS: at sentence #340000, processed 101174013 words, keeping 1193841 word types
2017-06-29 18:36:09,591 : INFO : PROGRESS: at sentence #350000, processed 103641958 words, keeping 1203681 word types
2017-06-29 18:36:10,369 : INFO : PROGRESS: at sentence #360000, processed 106534330 words, keeping 1204060 word types
2017-06-29 18:36:11,159 : INFO : PROGRESS: at sentence #370000, processed 109443172 words, keeping 1204060 word types
2017-06-29 18:36:11,942 : INFO : PROGRESS: at sentence #380000, processed 112343474 words, keeping 1204060 word types
2017-06-29 18:36:12,763 : INFO : PROGRESS: at sentence #390000, processed 115371521 words, keeping 1204316 word types
2017-06-29 18:36:13,593 : INFO : PROGRESS: at sentence #400000, processed 118360001 words, keeping 1205969 word types
2017-06-29 18:36:14,415 : INFO : PROGRESS: at sentence #410000, processed 121377292 words, keeping 1207211 word types
2017-06-29 18:36:15,245 : INFO : PROGRESS: at sentence #420000, processed 124502435 words, keeping 1207481 word types
2017-06-29 18:36:16,079 : INFO : PROGRESS: at sentence #430000, processed 127641667 words, keeping 1207749 word types
2017-06-29 18:36:16,951 : INFO : PROGRESS: at sentence #440000, processed 130837434 words, keeping 1208274 word types
2017-06-29 18:36:17,848 : INFO : PROGRESS: at sentence #450000, processed 134084773 words, keeping 1208778 word types
2017-06-29 18:36:18,731 : INFO : PROGRESS: at sentence #460000, processed 137319456 words, keeping 1208870 word types
2017-06-29 18:36:18,769 : INFO : collected 1208870 word types from a corpus of 137381164 raw words and 460187 sentences
2017-06-29 18:36:18,770 : INFO : Loading a fresh vocabulary
2017-06-29 18:36:20,211 : INFO : min_count=10 retains 440124 unique words (36% of original 1208870, drops 768746)
2017-06-29 18:36:20,212 : INFO : min_count=10 leaves 134926082 word corpus (98% of original 137381164, drops 2455082)
2017-06-29 18:36:21,085 : INFO : deleting the raw counts dictionary of 1208870 items
2017-06-29 18:36:21,153 : INFO : sample=0.001 downsamples 13 most-common words
2017-06-29 18:36:21,154 : INFO : downsampling leaves estimated 132753499 word corpus (98.4% of prior 134926082)
2017-06-29 18:36:21,154 : INFO : estimated required memory for 440124 words and 100 dimensions: 572161200 bytes
2017-06-29 18:36:22,468 : INFO : resetting layer weights
2017-06-29 18:36:25,601 : INFO : training model with 7 workers on 440124 vocabulary and 100 features, using sg=1 hs=0 sample=0.001 negative=5 window=5
2017-06-29 18:36:26,604 : INFO : PROGRESS: at 0.09% examples, 632872 words/s, in_qsize 10, out_qsize 0
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2017-06-29 18:52:11,027 : INFO : worker thread finished; awaiting finish of 6 more threads
2017-06-29 18:52:11,038 : INFO : worker thread finished; awaiting finish of 5 more threads
2017-06-29 18:52:11,051 : INFO : worker thread finished; awaiting finish of 4 more threads
2017-06-29 18:52:11,069 : INFO : worker thread finished; awaiting finish of 3 more threads
2017-06-29 18:52:11,084 : INFO : worker thread finished; awaiting finish of 2 more threads
2017-06-29 18:52:11,094 : INFO : worker thread finished; awaiting finish of 1 more threads
2017-06-29 18:52:11,099 : INFO : worker thread finished; awaiting finish of 0 more threads
2017-06-29 18:52:11,099 : INFO : training on 686905820 raw words (663763491 effective words) took 945.5s, 702026 effective words/s
2017-06-29 18:52:11,100 : INFO : saving Word2Vec object under data/eos/word2vec_model_all.model, separately None
2017-06-29 18:52:11,101 : INFO : not storing attribute syn0norm
2017-06-29 18:52:11,101 : INFO : storing np array 'syn0' to data/eos/word2vec_model_all.model.wv.syn0.npy
2017-06-29 18:52:11,394 : INFO : storing np array 'syn1neg' to data/eos/word2vec_model_all.model.syn1neg.npy
2017-06-29 18:52:11,761 : INFO : not storing attribute cum_table
2017-06-29 18:52:12,581 : INFO : saved data/eos/word2vec_model_all.model
Finished epoche 18:52:12
1 training epochs so far
440,124 terms in the word2vec EOS vocabulary.
CPU times: user 1h 48min 19s, sys: 7.92 s, total: 1h 48min 27s
Wall time: 16min 31s
In [63]:
%%time
word2vec_model = load_w2v(word2vec_model_file)
2017-06-29 22:01:43,923 : INFO : loading Word2Vec object from data/eos/word2vec_model_all.model
2017-06-29 22:01:44,782 : INFO : loading wv recursively from data/eos/word2vec_model_all.model.wv.* with mmap=None
2017-06-29 22:01:44,784 : INFO : loading syn0 from data/eos/word2vec_model_all.model.wv.syn0.npy with mmap=None
2017-06-29 22:01:44,835 : INFO : setting ignored attribute syn0norm to None
2017-06-29 22:01:44,838 : INFO : loading syn1neg from data/eos/word2vec_model_all.model.syn1neg.npy with mmap=None
2017-06-29 22:01:44,879 : INFO : setting ignored attribute cum_table to None
2017-06-29 22:01:44,880 : INFO : loaded data/eos/word2vec_model_all.model
2017-06-29 22:01:45,658 : INFO : precomputing L2-norms of word weight vectors
CPU times: user 3.38 s, sys: 120 ms, total: 3.5 s
Wall time: 3.49 s
In [64]:
def get_related_terms(token, topn=10):
"""
look up the topn most similar terms to token
and print them as a formatted list
"""
for word, similarity in word2vec_model.most_similar(positive=[token], topn=topn):
print ("{:20} {}".format(word, round(similarity, 3)))
In [75]:
get_related_terms(u'economic')
bilateral_interaction 0.734
b315bffa80685f5c 0.725
economy 0.719
abu_dhabidepartment 0.716
topilin 0.705
socio_economic 0.698
problem_interrelate 0.696
dawei_special 0.694
massively_deepen 0.694
latvian_presidency 0.692
In [66]:
get_related_terms(u'terror')
terrorist 0.802
via_facebook,[186 0.743
moi_karzakan 0.74
joe_pozell 0.733
terrorism’ 0.73
ibda 0.708
terrorist_organization 0.703
behave_unjust 0.7
thus_neutralize 0.699
milita 0.698
In [67]:
get_related_terms(u'baghdad')
iraqi 0.788
erbil 0.75
tikrit 0.748
anbar_province 0.731
arbil 0.712
anbar 0.711
kurds 0.71
kurdish 0.71
irbil 0.707
shi'ite_militia 0.706
In [68]:
get_related_terms(u'government')
authority 0.707
spokesman_bülent 0.661
twitter_@gregatthetrib 0.646
sharon_chepchirchir 0.646
unremitting 0.643
kant_celebration 0.64
president_abdrabbo 0.638
kompass 0.634
kurtulmuş_justice 0.633
governments’ 0.627
In [69]:
get_related_terms(u'maliki')
nouri_al 0.772
nakedly_sectarian 0.7
dispassionate_observer 0.699
favor_shiites 0.697
yam_matir 0.684
secessionist_aspiration 0.675
baathists 0.663
pregent 0.663
nuri_al 0.66
sectarian_tone 0.659
In [70]:
get_related_terms(u'russia')
russian 0.822
moscow 0.803
james_slavyanski 0.788
ukraine 0.777
moldova_georgia 0.764
lukashenko 0.757
belarussian 0.754
crimea 0.743
putin 0.742
rossiiskaya_gazeta 0.738
In [71]:
get_related_terms(u'health')
mohs 0.758
marleen_temmerman 0.751
central_labcentral 0.744
care_corporationprimary 0.738
dustin_duncan 0.736
saleha_complementary 0.726
joins_allegheny 0.726
adolescent_reproductive 0.725
survey_2008/09 0.72
dosimetrist 0.72
In [72]:
def word_algebra(add=[], subtract=[], topn=1):
"""
combine the vectors associated with the words provided
in add= and subtract=, look up the topn most similar
terms to the combined vector, and print the result(s)
"""
answers = word2vec_model.most_similar(positive=add, negative=subtract, topn=topn)
for term, similarity in answers:
print (term)
In [73]:
word_algebra(add=[u'sanction', u'syria'], topn=5)
curbing
sanction_impose
vladimir_chizhov
young_tajiks
russia
In [74]:
word_algebra(add=[u'economy', u'syria'], subtract=[u'asad'], topn=5)
economic
reckless_borrowing
recession
discontent_domestically
economic_growth
In [45]:
from sklearn.manifold import TSNE
In [46]:
# build a list of the terms, integer indices,
# and term counts from the word2vec model vocabulary
ordered_vocab = [(term, voc.index, voc.count)
for term, voc in word2vec_model.wv.vocab.items()]
# sort by the term counts, so the most common terms appear first
ordered_vocab = sorted(ordered_vocab, key=lambda tup: -tup[2])
# # unzip the terms, integer indices, and counts into separate lists
ordered_terms, term_indices, term_counts = zip(*ordered_vocab)
# # create a DataFrame with the food2vec vectors as data,
# # and the terms as row labels
word_vectors = pd.DataFrame(word2vec_model.wv.syn0norm[term_indices, :],
index=ordered_terms)
word_vectors
Out[46]:
0
1
2
3
4
5
6
7
8
9
...
90
91
92
93
94
95
96
97
98
99
say
-0.007175
0.080072
0.187092
0.055488
0.072249
0.112369
-0.107836
0.025000
0.024303
-0.082460
...
0.062865
-0.056757
0.230199
-0.031412
0.044573
0.007744
-0.136562
0.040573
0.050124
0.042578
istanbul
-0.036135
-0.015310
0.012111
0.015122
-0.180252
0.021894
-0.136873
-0.024661
0.058580
0.017153
...
0.182467
0.024780
-0.060922
-0.111213
-0.213065
0.114228
-0.037462
0.097068
0.024201
-0.113160
turkey
-0.127895
-0.050788
0.027706
0.013913
-0.095069
0.160910
-0.187430
0.106380
0.148729
0.015136
...
0.110936
-0.003229
0.051678
0.027611
-0.023469
0.156915
-0.261858
-0.079094
-0.203174
0.066948
will
0.026403
0.027060
0.144830
-0.107578
0.230813
-0.003298
-0.129703
0.029644
0.103363
-0.141433
...
0.006640
0.081921
0.105053
-0.017269
-0.124522
0.011551
0.012229
-0.087602
-0.247321
0.042051
year
0.035458
0.050516
0.003135
-0.016043
0.009039
0.092902
-0.217881
0.175122
0.065804
0.135953
...
-0.003752
-0.058563
0.076364
-0.081817
-0.034305
0.090204
-0.178993
-0.109481
-0.125844
-0.151508
turkish
0.049734
-0.085024
0.077718
0.060094
-0.141939
-0.011339
-0.177154
0.130250
0.001267
-0.054835
...
-0.013175
0.109169
0.064726
0.029496
0.082840
0.146370
-0.238414
0.068515
-0.092492
-0.134425
country
-0.172656
0.012565
0.096140
0.053815
0.065204
0.061690
-0.055476
-0.091359
0.053572
0.032707
...
0.035081
0.212101
0.076298
-0.029617
-0.074905
0.235771
-0.187836
-0.044306
0.002797
0.021783
also
0.047957
0.077703
0.071980
0.029916
0.096666
0.091557
-0.205554
-0.027032
0.047752
-0.031062
...
0.120670
-0.098722
0.163418
0.063945
0.104717
0.193466
-0.004166
0.165809
0.002308
-0.031382
report
-0.104857
-0.016275
-0.003608
-0.017703
-0.012306
0.118620
-0.117260
0.130901
0.242188
0.102539
...
0.060643
0.036640
0.058216
-0.055098
0.177434
-0.162351
-0.064648
0.004424
0.016056
0.135426
one
0.151683
0.058006
-0.066712
-0.003021
-0.066153
0.086485
-0.095669
0.087823
-0.015020
-0.040060
...
-0.011489
0.059597
0.096439
0.071556
-0.166881
-0.037777
-0.226817
-0.043788
0.003687
-0.103469
2014
-0.030080
0.055416
-0.133711
0.004351
-0.185522
0.031183
-0.276503
0.047110
0.160626
-0.069662
...
0.044102
-0.128110
0.004502
-0.075051
0.041970
0.146633
-0.006539
-0.026572
-0.128157
0.121942
two
-0.026772
0.003788
-0.109658
0.008365
-0.042905
0.035049
-0.126304
0.206089
0.128266
0.023004
...
0.048841
0.056053
0.083886
0.050222
-0.042958
-0.031473
-0.108313
0.018518
-0.024018
-0.190881
government
-0.145427
-0.051571
0.219745
-0.003305
0.040333
0.075364
-0.265159
0.014209
-0.016310
-0.115460
...
-0.078892
0.193475
-0.026426
-0.095946
0.001627
0.096821
-0.257144
0.012606
-0.021041
-0.040259
make
0.025160
0.000560
0.052905
-0.174010
-0.063678
0.136204
-0.159934
0.029702
0.000086
0.013303
...
0.110044
0.026002
0.004055
-0.025552
0.125093
0.088956
-0.154505
0.011635
0.007645
0.072734
people
-0.091974
0.059478
0.023189
0.005488
0.066259
0.173893
-0.087576
-0.014043
0.018769
0.050336
...
0.117576
-0.072245
0.232986
0.046452
0.120781
0.127227
-0.123195
-0.025581
-0.189127
-0.103314
kill
-0.081740
-0.005447
0.018472
0.133303
0.076641
0.017512
0.003887
0.072128
-0.067231
0.120541
...
0.021604
0.032971
-0.007676
-0.096835
0.186345
0.246221
0.042532
0.157333
-0.020955
-0.029153
al
-0.062980
-0.043839
0.100663
0.084139
0.211841
0.088168
-0.130800
0.173299
-0.072751
-0.087818
...
0.049914
0.067122
-0.208092
0.018652
-0.136524
0.188310
0.009492
0.033778
-0.073370
0.186508
take
0.125899
0.047834
0.017645
-0.058374
0.024626
0.016181
-0.004367
0.081018
0.031041
-0.108640
...
0.128632
0.043423
-0.004227
-0.001084
0.125422
0.163071
0.015512
-0.048648
-0.062239
0.006467
die
-0.159162
-0.138721
-0.034697
-0.072290
0.101603
-0.081048
-0.129726
0.012542
0.125210
0.123141
...
0.108098
0.158572
0.061611
-0.027249
-0.053886
0.227250
-0.019474
-0.035400
-0.074131
-0.054260
release
-0.023052
-0.155157
-0.049735
-0.057213
0.039720
0.183115
-0.259950
0.041212
-0.055865
0.148899
...
-0.058897
0.022607
0.216316
0.017283
0.046077
0.159625
0.078825
0.111966
0.032277
0.072520
use
0.017919
0.025233
0.126670
-0.063967
0.035568
0.045540
-0.263706
-0.013657
0.112049
-0.098960
...
0.118023
-0.090493
-0.004000
-0.077968
0.042727
0.097545
-0.095687
0.002204
0.014303
0.048503
iraq
-0.177109
-0.030952
-0.100538
0.076755
0.012067
0.190583
-0.127527
0.088391
-0.052124
-0.047211
...
-0.064984
0.065739
0.109719
0.057872
0.102734
-0.025163
-0.156460
0.073485
-0.078457
0.159067
group
0.091615
0.082531
0.036483
0.118741
-0.029250
0.071506
-0.245084
0.055297
0.076326
0.024575
...
-0.000394
0.089990
-0.001203
-0.111241
-0.076937
0.297324
-0.141974
-0.052840
0.117221
-0.135965
official
-0.033435
-0.020764
0.059590
-0.133348
0.047668
-0.095917
-0.216422
0.135215
0.114657
-0.007935
...
-0.112652
0.113300
0.108791
0.020373
0.089630
0.096546
-0.195533
0.109615
0.082232
0.015213
force
-0.051302
-0.045385
0.107835
0.048159
-0.026071
0.264495
-0.195866
-0.093140
0.110722
0.028148
...
0.014564
0.057751
0.006171
-0.166616
0.025653
0.122743
-0.209469
-0.162730
0.141524
-0.089900
day
-0.131548
0.093529
-0.111130
-0.135834
0.247161
0.038947
-0.113639
0.149191
0.079939
0.143661
...
-0.094659
0.018357
-0.044588
0.029464
-0.163479
0.113914
-0.045937
0.076741
-0.028915
-0.208017
attack
0.185713
0.037096
0.008169
0.062516
-0.077554
0.113981
-0.064820
0.005807
0.183394
0.030960
...
0.025447
-0.025554
0.051743
0.151645
0.086723
0.087277
-0.172123
0.121331
0.061347
0.040250
syria
0.103525
0.064497
-0.035089
0.176433
0.047972
-0.012873
0.012375
0.056772
-0.004923
-0.033192
...
0.090903
0.062227
0.114431
-0.135324
0.013285
0.167706
-0.262002
0.039680
-0.027336
0.101377
do_not
0.082974
0.077247
0.096322
0.034978
0.006877
0.044896
-0.081274
0.056609
-0.022484
-0.068219
...
0.068384
0.021245
0.089881
0.006565
-0.033715
0.106782
-0.028112
-0.032525
0.009773
-0.124098
islamic_state
-0.036371
-0.035310
-0.105348
-0.027342
-0.003000
0.111463
-0.134029
0.107310
-0.034985
0.013620
...
-0.010920
0.184687
0.219387
0.017219
0.117617
0.085950
-0.248726
0.115783
0.012890
0.103741
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
radiological
-0.084782
0.041526
0.068895
0.036198
-0.063713
0.075111
-0.198610
0.005856
0.109810
0.040258
...
-0.011182
-0.064215
0.050694
-0.070121
-0.059937
0.037450
-0.214015
0.004756
-0.041275
0.085901
nila
0.035991
-0.107168
0.026531
0.198637
0.029604
0.105270
0.055029
-0.020060
-0.009003
0.062965
...
-0.000530
-0.020153
-0.154255
-0.086591
-0.015745
-0.032141
0.008581
-0.010209
0.256607
0.050626
negotiation_chapter
-0.008010
-0.073908
0.029553
0.011573
0.085848
0.028672
-0.275935
0.063770
-0.003759
-0.198865
...
0.090730
-0.010783
0.139204
-0.061647
-0.035058
0.078966
-0.281204
-0.035101
-0.016322
0.075476
characterise
-0.040591
0.031566
0.058624
0.003455
-0.070116
0.010291
-0.257051
0.028046
0.096200
-0.110702
...
0.058111
-0.077298
0.071149
0.061803
-0.044124
0.061356
-0.150908
-0.110006
-0.019197
0.043273
convention_centre
-0.076523
0.079009
0.067047
0.041559
0.074451
0.161365
-0.260513
0.144277
0.023541
-0.017976
...
-0.089587
-0.045589
0.027428
-0.103061
-0.233285
0.076913
-0.036519
-0.003506
-0.044973
0.096993
stateless
-0.089152
-0.072662
0.014591
0.028800
0.043733
0.107848
-0.214299
0.188058
-0.013078
0.084558
...
-0.047340
-0.006329
0.046638
0.024514
-0.039203
0.122089
-0.033374
0.046774
-0.118084
-0.059382
frum
0.064065
-0.140256
0.066531
-0.027723
0.014495
-0.067955
-0.143580
0.186778
-0.052442
0.158476
...
0.075205
-0.098216
-0.011724
-0.064961
0.014589
-0.001467
0.038112
-0.036390
0.050133
0.017617
gadi_eisenkot
-0.105180
-0.087652
0.028327
0.011917
0.201628
0.095602
-0.093805
0.013001
-0.192993
-0.078910
...
-0.032476
0.023486
-0.035944
-0.073868
-0.087510
0.011451
-0.013085
-0.013065
0.105397
0.116020
kendrys_morales
0.110837
0.053714
-0.019298
-0.059844
-0.081511
0.025084
-0.147927
0.158957
0.070729
0.122787
...
0.052918
-0.087024
-0.087512
-0.155308
-0.086053
0.103220
0.014308
-0.047109
0.068438
-0.027221
mma
0.033996
0.003546
0.051872
0.039446
-0.074688
0.103353
-0.192643
0.061597
0.054602
-0.031072
...
-0.016004
0.134954
-0.012546
-0.115611
-0.047051
0.139867
-0.097583
-0.114987
-0.046257
0.075630
kenyan_soil
-0.049342
-0.007951
0.067945
0.029778
0.037426
-0.000637
-0.140602
0.016032
0.068200
0.007649
...
0.061027
-0.004911
-0.045834
-0.059228
0.060767
0.179019
-0.210576
0.133889
0.009799
-0.033391
whitewash
0.071349
-0.005215
0.086441
0.035942
-0.016645
0.071071
-0.174353
0.107367
0.005705
0.106472
...
0.042988
-0.107267
-0.082758
-0.022811
-0.015384
0.025012
-0.055352
0.018313
0.020940
0.047287
جرى
-0.017494
-0.115955
-0.002550
0.082988
-0.001978
0.053215
0.045140
-0.021179
0.068354
0.144360
...
0.027456
-0.005594
-0.140885
-0.105079
0.031540
-0.021886
-0.061626
0.130866
0.140234
0.014992
viitor
-0.020487
-0.086695
-0.084366
0.106825
0.138843
0.072887
0.019655
0.026785
-0.080689
-0.019568
...
-0.014826
-0.097046
-0.175593
-0.109115
-0.018416
0.076325
-0.014690
-0.017890
0.191626
0.084841
یکی_از
-0.016198
-0.117230
-0.006378
0.076278
0.005628
0.070740
0.027225
-0.033409
0.064749
0.140332
...
0.017420
0.012542
-0.148604
-0.090342
0.035444
-0.017842
-0.091683
0.132762
0.117434
0.012450
seismic
-0.009738
0.087583
0.074315
0.015184
0.078458
0.008590
-0.211312
-0.040134
0.062717
-0.071309
...
0.118312
0.098898
0.042536
-0.127569
0.071416
0.049085
-0.230165
-0.011284
-0.237609
-0.076771
cultural_heritage
-0.016243
-0.066360
0.108507
0.081441
-0.033847
0.050664
-0.255676
-0.058795
0.007172
-0.082351
...
-0.018048
0.114866
0.044173
0.028428
-0.027717
0.100919
-0.211773
-0.023836
-0.024271
-0.014209
vehemently_deny
-0.152459
-0.058369
0.229668
-0.051347
0.042601
0.053049
-0.191734
0.139923
0.113826
-0.098778
...
0.038797
0.001190
0.105058
-0.016964
0.122767
0.103827
-0.273587
0.032335
0.055161
0.054546
freitas
-0.028204
0.044932
0.143755
-0.070631
0.001071
0.083913
-0.242675
0.044169
0.116076
-0.139206
...
-0.045438
0.097773
0.166708
0.003426
0.055148
0.136190
-0.138701
-0.006041
-0.061990
0.039757
innovations
-0.044536
0.122042
-0.012181
0.026768
0.059509
0.122086
-0.257757
0.043464
0.076866
-0.036596
...
-0.009206
0.018004
0.030874
-0.064752
-0.079282
0.076300
-0.074904
-0.046451
-0.091227
0.155878
recepţionat_ultima
-0.032241
-0.136126
-0.002674
0.089382
0.049482
0.088732
0.041105
-0.031576
0.011431
0.061555
...
0.008107
-0.035632
-0.173227
-0.077640
-0.001278
0.016278
-0.057675
0.070640
0.195741
0.064583
interconnection
-0.013654
0.015849
0.076451
0.027196
0.131361
0.135050
-0.233376
0.055546
0.006966
-0.210511
...
0.040737
0.069296
0.100108
-0.062001
-0.102580
0.120195
-0.168350
0.002609
-0.009078
0.031324
11,000
-0.119646
0.059653
0.057799
0.157357
0.091652
0.044199
-0.226970
0.062433
0.030276
0.019085
...
0.043802
0.019122
0.131802
-0.064577
0.097394
0.193952
-0.091352
-0.020455
-0.121985
0.043296
72_virgins
0.090460
-0.037127
0.108155
-0.086298
0.051622
0.035858
-0.177113
0.112910
-0.051355
-0.076957
...
0.008502
-0.081238
-0.121879
-0.041552
-0.009791
0.089377
-0.071848
-0.122618
0.065926
-0.086120
cu_dizabilităţi
-0.021470
-0.098537
-0.048823
0.107724
0.126946
0.080129
0.035164
0.030936
-0.065588
-0.028827
...
0.006019
-0.076314
-0.187371
-0.110369
-0.009560
0.077484
-0.034071
0.001794
0.183278
0.063151
firmei
-0.041851
-0.111150
-0.083625
0.085216
0.150097
0.102897
-0.008617
0.073665
-0.103816
0.014733
...
-0.032736
-0.116372
-0.142631
-0.114572
-0.013362
0.084834
-0.000931
0.004978
0.140077
0.112357
fertility_problem
-0.060376
-0.032244
0.003570
-0.005015
-0.074345
0.117210
-0.212842
0.174380
-0.056630
-0.048692
...
0.012243
-0.009356
0.067061
-0.041214
-0.026261
0.091471
-0.100773
-0.034682
0.021516
0.006716
edgar
0.009079
-0.017819
0.103082
-0.179491
0.028402
-0.058034
-0.170711
0.143317
-0.087344
0.112935
...
-0.040657
-0.075904
-0.058598
0.008331
-0.167875
-0.026716
-0.041317
0.046712
0.083367
-0.035156
die_hälfte
-0.069399
-0.129514
0.049512
-0.035836
0.113498
0.011286
-0.024487
0.001885
0.056699
0.196907
...
0.008573
0.070969
-0.142175
0.019000
-0.092665
0.044966
0.008121
-0.025543
-0.014726
0.104025
حسین
-0.022731
-0.108423
-0.026839
0.067004
0.012225
0.061044
0.031032
-0.021430
0.060067
0.154672
...
0.031105
0.011349
-0.152482
-0.095261
0.016626
-0.003496
-0.082888
0.113917
0.111015
0.011661
25455 rows × 100 columns
In [47]:
# tsne_input = word_vectors.drop(spacy.en.English.Defaults.stop_words, errors=u'ignore')
tsne_input = word_vectors.head(5000)
In [48]:
tsne_input.head()
Out[48]:
0
1
2
3
4
5
6
7
8
9
...
90
91
92
93
94
95
96
97
98
99
say
-0.007175
0.080072
0.187092
0.055488
0.072249
0.112369
-0.107836
0.025000
0.024303
-0.082460
...
0.062865
-0.056757
0.230199
-0.031412
0.044573
0.007744
-0.136562
0.040573
0.050124
0.042578
istanbul
-0.036135
-0.015310
0.012111
0.015122
-0.180252
0.021894
-0.136873
-0.024661
0.058580
0.017153
...
0.182467
0.024780
-0.060922
-0.111213
-0.213065
0.114228
-0.037462
0.097068
0.024201
-0.113160
turkey
-0.127895
-0.050788
0.027706
0.013913
-0.095069
0.160910
-0.187430
0.106380
0.148729
0.015136
...
0.110936
-0.003229
0.051678
0.027611
-0.023469
0.156915
-0.261858
-0.079094
-0.203174
0.066948
will
0.026403
0.027060
0.144830
-0.107578
0.230813
-0.003298
-0.129703
0.029644
0.103363
-0.141433
...
0.006640
0.081921
0.105053
-0.017269
-0.124522
0.011551
0.012229
-0.087602
-0.247321
0.042051
year
0.035458
0.050516
0.003135
-0.016043
0.009039
0.092902
-0.217881
0.175122
0.065804
0.135953
...
-0.003752
-0.058563
0.076364
-0.081817
-0.034305
0.090204
-0.178993
-0.109481
-0.125844
-0.151508
5 rows × 100 columns
In [49]:
tsne_filepath = 'data/eos/tsne/tsne_model'
tsne_vectors_filepath = 'data/eos/tsne/tsne_vectors.npy'
In [50]:
%%time
if 1 == 1:
tsne = TSNE()
tsne_vectors = tsne.fit_transform(tsne_input.values)
with open(tsne_filepath, 'wb') as f:
pickle.dump(tsne, f)
print('done...')
pd.np.save(tsne_vectors_filepath, tsne_vectors)
# with open(tsne_filepath) as f:
# tsne = pickle.load(f)
# tsne_vectors = pd.np.load(tsne_vectors_filepath)
tsne_vectors = pd.DataFrame(tsne_vectors,
index=pd.Index(tsne_input.index),
columns=[u'x_coord', u'y_coord'])
done...
CPU times: user 51.2 s, sys: 3.04 s, total: 54.2 s
Wall time: 53.6 s
In [51]:
tsne_vectors.head()
Out[51]:
x_coord
y_coord
say
0.790907
2.216428
istanbul
1.657240
9.019597
turkey
-5.158473
7.759974
will
-5.248469
-3.632023
year
-5.759462
6.261718
In [52]:
tsne_vectors[u'word'] = tsne_vectors.index
In [54]:
# add our DataFrame as a ColumnDataSource for Bokeh
plot_data = ColumnDataSource(tsne_vectors)
# create the plot and configure the
# title, dimensions, and tools
tsne_plot = figure(title=u't-SNE Word Embeddings',
plot_width = 800,
plot_height = 800,
tools= (u'pan, wheel_zoom, box_zoom,'
u'box_select, resize, reset'),
active_scroll=u'wheel_zoom')
# add a hover tool to display words on roll-over
tsne_plot.add_tools( HoverTool(tooltips = u'@word') )
# draw the words as circles on the plot
tsne_plot.circle(u'x_coord', u'y_coord', source=plot_data,
color=u'blue', line_alpha=0.2, fill_alpha=0.1,
size=10, hover_line_color=u'black')
# configure visual elements of the plot
tsne_plot.title.text_font_size = value(u'16pt')
tsne_plot.xaxis.visible = False
tsne_plot.yaxis.visible = False
tsne_plot.grid.grid_line_color = None
tsne_plot.outline_line_color = None
# engage!
show(tsne_plot);
In [50]:
# Another view
# X = word2vec_model[word2vec_model.wv.vocab]
# tsne = TSNE(n_components=2)
# X_tsne = tsne.fit_transform(X)
# plt.scatter(X_tsne[:, 0], X_tsne[:, 1])
# plt.show()
In [ ]:
Content source: YorkUIRLab/eosdb
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