In [1]:
# Prakhar Pratyush
# er.prakhar2b@gmail.com
# IIT Roorkee
##############################

import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)

In [2]:
import gensim
from gensim.models.labeledword2vec import LabeledWord2Vec


2017-03-31 06:14:56,441 : INFO : 'pattern' package not found; tag filters are not available for English

In [3]:
from gensim.models.labeledword2vec import dbPedia
# Created a dbPedia class in labeledword2vec, taking inspiration from Text8Corpus
# Facebook (fastText) used dbPedia in their example script

In [4]:
corpus = dbPedia('dbpedia.train')

In [5]:
for a,b in corpus:
    print type(a)
    print type(b)
    print b
    print a
    break


<type 'list'>
<type 'list'>
['__label__5 ']
['steve', 'bolstad', 'was', 'a', 'democratic', 'party', 'member', 'of', 'the', 'montana', 'house', 'of', 'representatives', 'who', 'represented', 'district', '24', 'from', '2007-2008']

In [6]:
model = LabeledWord2Vec(corpus, size=100, loss='softmax', min_count=5, workers=4)


2017-03-31 06:14:56,768 : INFO : <gensim.models.labeledword2vec.dbPedia object at 0x7f048503cb50>
2017-03-31 06:15:49,138 : INFO : collecting all words and their counts
2017-03-31 06:15:49,140 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types
2017-03-31 06:15:50,629 : INFO : PROGRESS: at sentence #10000, processed 188053 words, keeping 32149 word types
2017-03-31 06:15:51,919 : INFO : PROGRESS: at sentence #20000, processed 376041 words, keeping 52979 word types
2017-03-31 06:15:53,238 : INFO : PROGRESS: at sentence #30000, processed 564090 words, keeping 71313 word types
2017-03-31 06:15:54,518 : INFO : PROGRESS: at sentence #40000, processed 755235 words, keeping 88110 word types
2017-03-31 06:15:55,468 : INFO : PROGRESS: at sentence #50000, processed 943522 words, keeping 103562 word types
2017-03-31 06:15:56,448 : INFO : PROGRESS: at sentence #60000, processed 1132999 words, keeping 118361 word types
2017-03-31 06:15:57,413 : INFO : PROGRESS: at sentence #70000, processed 1323887 words, keeping 132396 word types
2017-03-31 06:15:58,371 : INFO : PROGRESS: at sentence #80000, processed 1513492 words, keeping 145805 word types
2017-03-31 06:15:59,630 : INFO : PROGRESS: at sentence #90000, processed 1701927 words, keeping 158696 word types
2017-03-31 06:16:01,025 : INFO : PROGRESS: at sentence #100000, processed 1891528 words, keeping 170939 word types
2017-03-31 06:16:02,371 : INFO : PROGRESS: at sentence #110000, processed 2080178 words, keeping 182999 word types
2017-03-31 06:16:03,381 : INFO : PROGRESS: at sentence #120000, processed 2268354 words, keeping 194646 word types
2017-03-31 06:16:04,380 : INFO : PROGRESS: at sentence #130000, processed 2457985 words, keeping 206155 word types
2017-03-31 06:16:05,358 : INFO : PROGRESS: at sentence #140000, processed 2647213 words, keeping 217322 word types
2017-03-31 06:16:06,337 : INFO : PROGRESS: at sentence #150000, processed 2837461 words, keeping 228315 word types
2017-03-31 06:16:07,563 : INFO : PROGRESS: at sentence #160000, processed 3027524 words, keeping 239138 word types
2017-03-31 06:16:08,774 : INFO : PROGRESS: at sentence #170000, processed 3216760 words, keeping 249851 word types
2017-03-31 06:16:10,122 : INFO : PROGRESS: at sentence #180000, processed 3406467 words, keeping 260355 word types
2017-03-31 06:16:11,206 : INFO : PROGRESS: at sentence #190000, processed 3595371 words, keeping 270615 word types
2017-03-31 06:16:12,160 : INFO : PROGRESS: at sentence #200000, processed 3784952 words, keeping 280884 word types
2017-03-31 06:16:13,174 : INFO : PROGRESS: at sentence #210000, processed 3974164 words, keeping 290686 word types
2017-03-31 06:16:14,167 : INFO : PROGRESS: at sentence #220000, processed 4163363 words, keeping 300451 word types
2017-03-31 06:16:15,367 : INFO : PROGRESS: at sentence #230000, processed 4352247 words, keeping 310159 word types
2017-03-31 06:16:16,655 : INFO : PROGRESS: at sentence #240000, processed 4540647 words, keeping 319627 word types
2017-03-31 06:16:18,017 : INFO : PROGRESS: at sentence #250000, processed 4731558 words, keeping 329120 word types
2017-03-31 06:16:19,135 : INFO : PROGRESS: at sentence #260000, processed 4922296 words, keeping 338304 word types
2017-03-31 06:16:20,103 : INFO : PROGRESS: at sentence #270000, processed 5113022 words, keeping 347392 word types
2017-03-31 06:16:21,082 : INFO : PROGRESS: at sentence #280000, processed 5302146 words, keeping 356422 word types
2017-03-31 06:16:22,045 : INFO : PROGRESS: at sentence #290000, processed 5493256 words, keeping 365422 word types
2017-03-31 06:16:23,169 : INFO : PROGRESS: at sentence #300000, processed 5682498 words, keeping 374398 word types
2017-03-31 06:16:24,413 : INFO : PROGRESS: at sentence #310000, processed 5870322 words, keeping 382752 word types
2017-03-31 06:16:25,737 : INFO : PROGRESS: at sentence #320000, processed 6060402 words, keeping 391376 word types
2017-03-31 06:16:26,946 : INFO : PROGRESS: at sentence #330000, processed 6251665 words, keeping 399951 word types
2017-03-31 06:16:27,906 : INFO : PROGRESS: at sentence #340000, processed 6438666 words, keeping 408171 word types
2017-03-31 06:16:28,863 : INFO : PROGRESS: at sentence #350000, processed 6627339 words, keeping 416662 word types
2017-03-31 06:16:29,818 : INFO : PROGRESS: at sentence #360000, processed 6818060 words, keeping 425071 word types
2017-03-31 06:16:30,857 : INFO : PROGRESS: at sentence #370000, processed 7008225 words, keeping 433665 word types
2017-03-31 06:16:32,194 : INFO : PROGRESS: at sentence #380000, processed 7198624 words, keeping 441930 word types
2017-03-31 06:16:33,491 : INFO : PROGRESS: at sentence #390000, processed 7387784 words, keeping 449987 word types
2017-03-31 06:16:34,778 : INFO : PROGRESS: at sentence #400000, processed 7578357 words, keeping 458186 word types
2017-03-31 06:16:35,804 : INFO : PROGRESS: at sentence #410000, processed 7768991 words, keeping 466368 word types
2017-03-31 06:16:36,781 : INFO : PROGRESS: at sentence #420000, processed 7957318 words, keeping 474237 word types
2017-03-31 06:16:37,775 : INFO : PROGRESS: at sentence #430000, processed 8146365 words, keeping 482167 word types
2017-03-31 06:16:38,836 : INFO : PROGRESS: at sentence #440000, processed 8338135 words, keeping 490073 word types
2017-03-31 06:16:40,190 : INFO : PROGRESS: at sentence #450000, processed 8527500 words, keeping 497874 word types
2017-03-31 06:16:41,563 : INFO : PROGRESS: at sentence #460000, processed 8717840 words, keeping 505684 word types
2017-03-31 06:16:42,829 : INFO : PROGRESS: at sentence #470000, processed 8907287 words, keeping 513597 word types
2017-03-31 06:16:43,891 : INFO : PROGRESS: at sentence #480000, processed 9097564 words, keeping 521236 word types
2017-03-31 06:16:44,982 : INFO : PROGRESS: at sentence #490000, processed 9287110 words, keeping 528858 word types
2017-03-31 06:16:46,009 : INFO : PROGRESS: at sentence #500000, processed 9475965 words, keeping 536299 word types
2017-03-31 06:16:47,292 : INFO : PROGRESS: at sentence #510000, processed 9665045 words, keeping 543775 word types
2017-03-31 06:16:48,661 : INFO : PROGRESS: at sentence #520000, processed 9853500 words, keeping 551204 word types
2017-03-31 06:16:50,068 : INFO : PROGRESS: at sentence #530000, processed 10040466 words, keeping 558451 word types
2017-03-31 06:16:51,185 : INFO : PROGRESS: at sentence #540000, processed 10229112 words, keeping 565838 word types
2017-03-31 06:16:52,152 : INFO : PROGRESS: at sentence #550000, processed 10419325 words, keeping 573103 word types
2017-03-31 06:16:53,106 : INFO : collected 580362 word types from a corpus of 10608497 raw words and 560000 sentences
2017-03-31 06:16:53,107 : INFO : Loading a fresh vocabulary
2017-03-31 06:16:53,922 : INFO : min_count=5 retains 73083 unique words (12% of original 580362, drops 507279)
2017-03-31 06:16:53,923 : INFO : min_count=5 leaves 9923105 word corpus (93% of original 10608497, drops 685392)
2017-03-31 06:16:54,290 : INFO : deleting the raw counts dictionary of 580362 items
2017-03-31 06:16:54,341 : INFO : sample=0.001 downsamples 33 most-common words
2017-03-31 06:16:54,342 : INFO : downsampling leaves estimated 7215777 word corpus (72.7% of prior 9923105)
2017-03-31 06:16:54,345 : INFO : estimated required memory for 73083 words and 100 dimensions: 65774700 bytes
2017-03-31 06:16:54,509 : INFO : resetting layer weights
2017-03-31 06:16:56,746 : INFO : Building the vocabulary of labels (words of the output layer)
2017-03-31 06:16:56,747 : INFO : collecting all words and their counts
2017-03-31 06:16:56,749 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types
2017-03-31 06:16:56,754 : INFO : collected 14 word types from a corpus of 14 raw words and 1 sentences
2017-03-31 06:16:56,756 : INFO : Loading a fresh vocabulary
2017-03-31 06:16:56,757 : INFO : min_count=0 retains 14 unique words (100% of original 14, drops 0)
2017-03-31 06:16:56,758 : INFO : min_count=0 leaves 14 word corpus (100% of original 14, drops 0)
2017-03-31 06:16:56,760 : INFO : deleting the raw counts dictionary of 14 items
2017-03-31 06:16:56,761 : INFO : sample=0 downsamples 0 most-common words
2017-03-31 06:16:56,763 : INFO : downsampling leaves estimated 14 word corpus (100.0% of prior 14)
2017-03-31 06:16:56,764 : INFO : estimated required memory for 14 words and 100 dimensions: 12600 bytes
2017-03-31 06:16:56,766 : INFO : resetting layer weights
/home/prakhar/anaconda2/lib/python2.7/site-packages/gensim/models/labeledword2vec.py:387: UserWarning: C extension not loaded for Word2Vec, training will be slow. Install a C compiler and reinstall gensim for fast training.
  warnings.warn("C extension not loaded for Word2Vec, training will be slow. "
2017-03-31 06:16:56,768 : INFO : training model with 4 workers on 73083 vocabulary and 100 features, using sg=0 hs=0 sample=0.001 negative=0 window=9223372036854775807
2017-03-31 06:16:56,769 : INFO : expecting 560000 sentences, matching count from corpus used for vocabulary survey
2017-03-31 06:16:58,968 : INFO : PROGRESS: at 0.02% examples, 3097 words/s, in_qsize 7, out_qsize 0
2017-03-31 06:17:00,187 : INFO : PROGRESS: at 0.10% examples, 9964 words/s, in_qsize 7, out_qsize 0
2017-03-31 06:17:01,403 : INFO : PROGRESS: at 0.17% examples, 13219 words/s, in_qsize 7, out_qsize 0
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2017-03-31 06:57:56,178 : INFO : worker thread finished; awaiting finish of 3 more threads
2017-03-31 06:57:56,229 : INFO : worker thread finished; awaiting finish of 2 more threads
2017-03-31 06:57:56,361 : INFO : worker thread finished; awaiting finish of 1 more threads
2017-03-31 06:57:56,412 : INFO : worker thread finished; awaiting finish of 0 more threads
2017-03-31 06:57:56,414 : INFO : training on 53042485 raw words (36077904 effective words) took 2459.6s, 14668 effective words/s

In [7]:
model.predict(('this', 'is', 'a', 'document'))


Out[7]:
[('__label__13 ', 0.0019447299),
 ('__label__11 ', 0.068571143),
 ('__label__7 ', 0.0039946274),
 ('__label__6 ', 0.012618431),
 ('__label__3 ', 0.0065235742),
 ('__label__9 ', 0.032118198),
 ('__label__2 ', 0.25102019),
 ('__label__1 ', 0.0344299),
 ('__label__10 ', 0.065702207),
 ('__label__4 ', 0.011481027),
 ('__label__14 ', 0.10591118),
 ('__label__12 ', 0.31658924),
 ('__label__5 ', 0.036398347),
 ('__label__8 ', 0.052697256)]

In [9]:
model.predict(('steve', 'bolstad', 'was', 'a', 'democratic', 'party', 'member', 'of', 'the', 'montana', 'house', 'of', 'representatives', 'who', 'represented', 'district', '24', 'from', '2007-2008'))


Out[9]:
[('__label__13 ', 2.7748533e-07),
 ('__label__11 ', 1.449238e-07),
 ('__label__7 ', 1.2371956e-06),
 ('__label__6 ', 2.0912228e-08),
 ('__label__3 ', 2.7629557e-07),
 ('__label__9 ', 6.3651845e-10),
 ('__label__2 ', 8.5066205e-07),
 ('__label__1 ', 5.7213396e-07),
 ('__label__10 ', 1.9056859e-08),
 ('__label__4 ', 1.3083488e-07),
 ('__label__14 ', 5.1655525e-07),
 ('__label__12 ', 7.1353284e-08),
 ('__label__5 ', 0.99999636),
 ('__label__8 ', 3.0999328e-09)]

we can see that label 5 has highest score. It's not showing in proper order as of now. The code needs massive improvement.

However, just look at the accuracy :)


In [10]:
test_corpus = dbPedia('dbpedia.test')

In [11]:
model.predict(test_corpus)


-------------------------------------------------------------------
TypeError                         Traceback (most recent call last)
<ipython-input-11-b9a369391230> in <module>()
----> 1 model.predict(test_corpus)

/home/prakhar/anaconda2/lib/python2.7/site-packages/gensim/models/labeledword2vec.pyc in predict(self, document)
    433     def predict(self, document):
    434         """Return the probabilistic scores of the relatedness of each label with this document"""
--> 435         return score_document_labeled_cbow(self, document=document)
    436 
    437     def score(self, **kwargs):

/home/prakhar/anaconda2/lib/python2.7/site-packages/gensim/models/labeledword2vec.pyc in score_document_labeled_cbow(model, document, labels, work, neu1)
    116 def score_document_labeled_cbow(model, document, labels=None, work=None, neu1=None):
    117 
--> 118         word_vocabs = [model.wv.vocab[w] for w in document if w in model.wv.vocab]
    119 
    120         if labels is not None:

TypeError: unhashable type: 'list'

OK, as of now, this part is not written. Only sentences as bag of words is supported as of now, not entire corpus.

Facebook fastText result on dbpedia (softmax)

Read 32M words Number of words: 803537 (794259 in labeledw2v) Number of labels: 14 Progress: 100.0% words/sec/thread: 1310320 lr: 0.000000 loss: 0.087406 eta: 0h0m 4m (actual - within 1 minute)

prakhar@AwesomeDude:~/fastText$ ./fasttext test model.bin dbpedia.test P@1: 0.983 R@1: 0.983 Number of examples: 70000.


In [ ]: