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
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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)]
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'
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 [ ]:
Content source: prakhar2b/Weekend-Projects
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