def get_top_articles(self, date, min_rating):
"""
Returns iterator to articles from date and with a rating bigger than
min_rating.
"""
#get all articles from specific date
articles_from_date = Article.objects(date__gte=date.date(), date__lt=date.date() + timedelta(days=1))
#get all ranked article form loaded articles
return [a.article for a in RankedArticle.objects(user_id=self.mongodb_user.id,
rating__gte=min_rating,
article__in=articles_from_date)]
def test_constructor_with_file_wikicorpus(self):
#load tf-idf model
tfidf_model = tfidfmodel.TfidfModel.load("/vagrant/data/test_tfidf.model")
extractor = TfidfFeatureExtractor("/vagrant/data/test")
#load tf-idf corpus
tfidf_corpus = MmCorpus('/vagrant/data/test_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/test_wordids.dict")
#load article titles
document_titles = DocumentTitles.load("/vagrant/test_articles.txt")
#Connect to mongo database
connect(self.config_['database']['db-name'],
username=self.config_['database']['user'],
password=self.config_['database']['passwd'],
port=self.config_['database']['port'])
#Load articles as test corpus
user = User.objects(email=u"*****@*****.**").first()
ranked_article_ids = (a.article.id
for a
in RankedArticle.objects(user_id=user.id).only("article"))
all_article_ids = set(a.id
for a
in Article.objects(id__in=ranked_article_ids).only("id"))
read_article_ids = set(a.article.id
for a
in ReadArticleFeedback.objects(user_id=user.id).only("article"))
unread_article_ids = all_article_ids - read_article_ids
#sample test articles
X, y = get_samples(extractor, read_article_ids, unread_article_ids)
s,f = X.shape
logger.debug("Training with %d samples, %d features, %d marks" % (s, f, len(y)))
#train esa model
esa_model = CosineEsaModel(tfidf_corpus,
document_titles=document_titles,
test_corpus=X,
test_corpus_targets=y,
num_test_corpus=len(y),
num_best_features=15,
num_features=len(id2token))
for line in esa_model:
print repr(line)
esa_model.save('/vagrant/data/test_cesa.model')
tmp_esa = CosineEsaModel.load('/vagrant/data/test_cesa.model')
print tmp_esa
