def test_constructor_with_file_wikicorpus(self):
#load tf-idf corpus
tfidf_corpus = MmCorpus('/media/sdc1/test_dump/result/test_tfidf_corpus.mm')
#load lda corpus
#lda_corpus = MmCorpus('/media/sdc1/test_dump/result/test_lda_corpus.mm')
#load dictionary
id2token = Dictionary.load("/media/sdc1/test_dump/result/test_wordids.dict")
#load article titles
document_titles = DocumentTitles.load("/media/sdc1/test_dump/result/test_articles.txt")
#train esa model
esa_model = EsaModel(tfidf_corpus, num_clusters = 15,
document_titles = document_titles,
num_features = len(id2token))
print esa_model
esa_model.save('/media/sdc1/test_dump/result/wiki_esa.model')
tmp_esa = EsaModel.load('/media/sdc1/test_dump/result/wiki_esa.model')
print tmp_esa
"""LDA Model creation"""
#build lda model
lda = models.LdaModel(corpus=mm_tfidf, id2word=id2token,
num_topics=NUM_TOPICS, update_every=1,
chunksize=10000,
passes=2)
#save trained model
lda.save(options.prefix + '_lda.model')
#save corpus as lda vectors in matrix market format
corpora.MmCorpus.serialize(options.prefix + '_lda_corpus.mm', lda[mm_tfidf],
progress_cnt=10000)
#init lda-corpus reader
mm_lda = corpora.MmCorpus(options.prefix + '_lda_corpus.mm')
"""ESA Model creation"""
#document titles
article_titles = DocumentTitles.load(options.prefix + "_articles.txt")
#build esa model
esa = EsaModel(mm_lda, num_clusters=10000, document_titles=article_titles,
num_features=NUM_TOPICS)
esa.save(options.prefix + "_esa_on_lda.model")
logger.info("finished transforming")