def __init__(self, prefix):
Extractor.__init__(self)
logger.info(
"Load dictionary, tfidf model, lda model and esa model with prefix %s" % prefix)
self.dictionary = corpora.Dictionary.load(prefix + "_wordids.dict")
self.tfidf_model = models.TfidfModel.load(prefix + "_tfidf.model")
# self.lda_model = models.LdaModel.load(prefix + "_lda.model")
self.esa_model = EsaModel.load(prefix + "_esa1000_on_tfidf.model")
def load_esa_models(self):
logger.info("Loading ESA models")
self.esas = {
# 'en': EsaModel.load(self.prefix + self.target_prefix + "en_esa_on_tfidf.model"),
'es': EsaModel.load(self.prefix + "es_esa_on_tfidf.model"),
# 'de': EsaModel.load(self.prefix + "de_esa_on_tfidf.model"),
# 'nl': EsaModel.load(self.prefix + "nl_esa_on_tfidf.model")
}
def test_constructor_with_big_file_wikicorpus(self):
#load tf-idf corpus
tfidf_corpus = MmCorpus('/vagrant/data/wiki_tfidf_corpus.mm')
#load lda corpus
#lda_corpus = MmCorpus('/media/sdc1/test_dump/result/test_lda_corpus.mm')
#load dictionary
id2token = Dictionary.load("/vagrant/data/wiki_wordids.dict")
#load article titles
document_titles = DocumentTitles.load("/vagrant/data/wiki_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('/vagrant/data/wiki_cesa.model')
tmp_esa = EsaModel.load('/vagrant/data/wiki_cesa.model')
print tmp_esa
from config import CONFIG
import os
from gensim import models
from gensim import corpora
from nyan.shared_modules.feature_extractor.esa.esamodel import EsaModel
from nyan.shared_modules.feature_extractor.esa.document_titles import DocumentTitles
from wikiextract.mappings import generate_mappings
language = "en"
NUM_TOPICS = 2000
TF_IDF_PATH = os.path.join(CONFIG['prefix'], language + "_tfidf.model")
TF_IDF = models.TfidfModel.load(TF_IDF_PATH)
TF_IDF_CORPUS_PATH = os.path.join(CONFIG['prefix'], language + "_tfidf_corpus.mm")
MM_TF_IDF = corpora.MmCorpus(TF_IDF_CORPUS_PATH)
ARTICLES_PATH = os.path.join(CONFIG['prefix'], language + "_articles.txt")
ARTICLE_TITLES = DocumentTitles.load(ARTICLES_PATH)
SMALL_EN_ESA_PATH = os.path.join(CONFIG['prefix'], "en_esa%d_on_tfidf.model" % NUM_TOPICS)
SMALL_EN_ESA = EsaModel(MM_TF_IDF, document_titles=ARTICLE_TITLES, num_features=50000,
num_concepts=NUM_TOPICS, lang='en')
SMALL_EN_ESA.save(SMALL_EN_ESA_PATH)
SMALL_EN_ESA = EsaModel.load(SMALL_EN_ESA_PATH)
# Regenerate mappings
generate_mappings()