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py-phias/aore/fias/search.py
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Python

# -*- coding: utf-8 -*-
import json
import re
import Levenshtein
import psycopg2
import aore.sphinxapi as sphinxapi
from aore.config import db as dbparams, sphinx_index_sugg, sphinx_index_addjobj
from aore.dbutils.dbimpl import DBImpl
from aore.fias.wordentry import WordEntry
from aore.miscutils.trigram import trigram
class SphinxSearch:
def __init__(self):
self.delta_len = 2
self.rating_limit_soft = 0.4
self.rating_limit_hard = 0.82
self.default_rating_delta = 2
self.regression_coef = 0.04
self.db = DBImpl(psycopg2, dbparams)
self.client_sugg = sphinxapi.SphinxClient()
self.client_sugg.SetServer("localhost", 9312)
self.client_sugg.SetLimits(0, 10)
self.client_sugg.SetConnectTimeout(3.0)
self.client_show = sphinxapi.SphinxClient()
self.client_show.SetServer("localhost", 9312)
self.client_show.SetLimits(0, 10)
self.client_show.SetConnectTimeout(3.0)
def __configure(self, index_name, wlen=None):
if index_name == "idx_fias_sugg":
if wlen:
self.client_sugg.SetMatchMode(sphinxapi.SPH_MATCH_EXTENDED2)
self.client_sugg.SetRankingMode(sphinxapi.SPH_RANK_WORDCOUNT)
self.client_sugg.SetFilterRange("len", int(wlen) - self.delta_len, int(wlen) + self.delta_len)
self.client_sugg.SetSelect("word, len, @weight+{}-abs(len-{}) AS krank".format(self.delta_len, wlen))
self.client_sugg.SetSortMode(sphinxapi.SPH_SORT_EXTENDED, "krank DESC")
else:
self.client_show.SetMatchMode(sphinxapi.SPH_MATCH_EXTENDED2)
self.client_show.SetRankingMode(sphinxapi.SPH_RANK_BM25)
def __get_suggest(self, word, rating_limit, count):
word_len = str(len(word) / 2)
trigrammed_word = '"{}"/1'.format(trigram(word))
self.__configure(sphinx_index_sugg, word_len)
result = self.client_sugg.Query(trigrammed_word, sphinx_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])
outlist.sort(key=lambda x: x[1], reverse=True)
return outlist
def __split_phrase(self, phrase):
phrase = unicode(phrase).replace('-', '').replace('@', '').lower()
return re.split(r"[ ,:.#$]+", phrase)
def __add_word_variations(self, word_entry):
if word_entry.MT_MANY_SUGG:
suggs = self.__get_suggest(word_entry.word, self.rating_limit_soft, 6)
for suggestion in suggs:
word_entry.add_variation(suggestion[0])
if word_entry.MT_SOME_SUGG:
suggs = self.__get_suggest(word_entry.word, self.rating_limit_hard, 3)
for suggestion in suggs:
word_entry.add_variation(suggestion[0])
if word_entry.MT_LAST_STAR:
word_entry.add_variation(word_entry.word+'*')
if word_entry.MT_AS_IS:
word_entry.add_variation(word_entry.word)
if word_entry.MT_ADD_SOCR:
word_entry.add_variation_socr()
def __get_word_entries(self, words):
for word in words:
if word != '':
we = WordEntry(self.db, word)
self.__add_word_variations(we)
yield we
def find(self, text):
words = self.__split_phrase(text)
word_entries = self.__get_word_entries(words)
sentence = "{}".format(" MAYBE ".join(x.get_variations() for x in word_entries))
self.__configure(sphinx_index_addjobj)
rs = self.client_show.Query(sentence, sphinx_index_addjobj)
results = []
for ma in rs['matches']:
results.append([ma['attrs']['aoid'], ma['attrs']['fullname'], ma['weight']])
print results