def __get_suggest(self, word, rating_limit, count):
word_len = str(len(word) / 2)
trigrammed_word = '"{}"/1'.format(trigram(word))
self.__configure(SphinxConfig.index_sugg, word_len)
result = self.client_sugg.Query(trigrammed_word, SphinxConfig.index_sugg)
# Если по данному слову не найдено подсказок (а такое бывает?)
# возвращаем []
if not result['matches']:
return []
maxrank = result['matches'][0]['attrs']['krank']
maxleven = None
outlist = list()
for match in result['matches']:
if len(outlist) >= count:
break
if maxrank - match['attrs']['krank'] < self.default_rating_delta:
jaro_rating = Levenshtein.jaro(word, match['attrs']['word'])
if not maxleven:
maxleven = jaro_rating - jaro_rating * self.regression_coef
if jaro_rating >= rating_limit and jaro_rating >= maxleven:
outlist.append([match['attrs']['word'], jaro_rating])
del jaro_rating
outlist.sort(key=lambda x: x[1], reverse=True)
return outlist
def __dbexport_sugg_dict(self):
logging.info("Place suggestion dict to DB %s...", self.files['dict.txt'])
dict_dat_fname = os.path.abspath(Folders.temp + "/suggdict.csv")
csv_counter = 0
with open(self.files['dict.txt'], "r") as dict_file, open(dict_dat_fname, "w") as exit_file:
line = None
while line != '':
nodes = []
line = dict_file.readline()
if line == '':
break
csv_counter += 1
splitting_seq = line.split(' ')
keyword = splitting_seq[0]
freq = splitting_seq[1].rstrip('\n')
assert keyword and freq, "Cannot process {}".format(self.files['dict.txt'])
nodes.append(keyword)
nodes.append(trigram(keyword))
nodes.append(freq)
exit_file.write("\t".join(nodes) + "\n")
try:
dict_file.close()
exit_file.close()
except:
pass
self.aodp.bulk_csv(AoXmlTableEntry.OperationType.update, "AOTRIG", csv_counter, dict_dat_fname)
logging.info("Done.")
