def getRankings(self, token, positiveWords, negativeWords):
#stemSentence('lattice mismatch')
results = None
positiveWords = [stemming.porter2.stem(word) for word in json.loads(positiveWords)]
negativeWords = [stemming.porter2.stem(word) for word in json.loads(negativeWords)]
radius = 1
#for state in states:
result = index[lsi[tfidf[[dictionary.doc2bow(positiveWords)]]]].flatten()# * (1.0 - index[lsi[tfidf[[dictionary.doc2bow(negativeWords)]]]].flatten())
rankings = { 'lsi' : [],
'doc2vec' : []}
cs = numpy.concatenate((numpy.zeros(radius + 1), numpy.cumsum(result), numpy.zeros(radius)))
integrated = numpy.zeros(len(result))
for i in range(len(result)):
integrated[i] = cs[i + 2 * radius + 1] - cs[i]
for i in numpy.argsort(-integrated)[0:10]:
#print i, len(index2id), len(result)
rankings['lsi'].append((index2id[i][0], 0.0))
#result[i]
#positiveVec = doc2vec.infer_vector(positiveWords)
#negativeVec = doc2vec.infer_vector(negativeWords)
#rankings['doc2vec'] = []
#for idx, score in doc2vec.docvecs.most_similar(positive = [positiveVec], negative = [negativeVec], topn = 10):
# rankings['doc2vec'].append((idx, 0.0))
sentenceIdxs = set([idx for idx, score in rankings['lsi']])# | set([idx for idx, score in rankings['doc2vec']])
session, engine = mldb.getSession()
sentences = {}
for idx in sentenceIdxs:
sentencem = session.query(mldb.Sentence).get(index2id[idx - 1])
sentence = session.query(mldb.Sentence).get(index2id[idx])
sentencep = session.query(mldb.Sentence).get(index2id[idx + 1])
sentences[idx] = {
'string' : sentencem.pattern.string + sentence.pattern.string + sentencep.pattern.string,
'paper' : {
'title' : sentence.paper.data['dc:title'],
'url' : 'http://www.sciencedirect.com/science/article/pii/{0}'.format(sentence.paper.pii)
}
}
return {
'token' : token,
'sentences': sentences,
'rankings' : rankings
}
import traceback
import os
import stemming.porter2
import mldb
import sqlalchemy.orm
appDir = os.path.abspath(os.path.dirname(__file__))
templateLoader = jinja2.FileSystemLoader(searchpath = "html")
templateEnv = jinja2.Environment(loader = templateLoader)
#f = open('/home/bbales2/scraping/webpage/db/sentences.json', 'r')
#sentences = json.loads(f.read())
#f.close()
session, engine = mldb.getSession()
with open('/home/bbales2/scraping/webpage/db/index2id.big') as f:
index2id = json.load(f)
#list(session.query(mldb.Sentence).options(sqlalchemy.orm.load_only("id")).order_by(mldb.Sentence.id).with_entities(mldb.Sentence.id))
index = gensim.similarities.docsim.MatrixSimilarity.load('/home/bbales2/scraping/webpage/db/index.big')
dictionary = gensim.corpora.dictionary.Dictionary.load('/home/bbales2/scraping/webpage/db/dictionary.big')
tfidf = gensim.models.tfidfmodel.TfidfModel.load('/home/bbales2/scraping/webpage/db/tfidf.big')
lsi = gensim.models.lsimodel.LsiModel.load('/home/bbales2/scraping/webpage/db/lsi.big')
word2vec = gensim.models.word2vec.Word2Vec.load('/home/bbales2/scraping/webpage/db/word2vec.big')
#doc2vec = gensim.models.word2vec.Word2Vec.load('/home/bbales2/scraping/webpage/db/doc2vec.big')
def logexceptions(func):
def inner(*args, **kwargs):
try:
