118 lines
4.4 KiB
Python
118 lines
4.4 KiB
Python
# -*- coding: utf-8 -*-
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import json
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import re
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import Levenshtein
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import psycopg2
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import aore.sphinxapi as sphinxapi
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from aore.config import db as dbparams, sphinx_index_sugg, sphinx_index_addjobj
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from aore.dbutils.dbimpl import DBImpl
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from aore.fias.wordentry import WordEntry
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from aore.miscutils.trigram import trigram
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class SphinxSearch:
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def __init__(self):
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self.delta_len = 2
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self.rating_limit_soft = 0.4
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self.rating_limit_hard = 0.82
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self.default_rating_delta = 2
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self.regression_coef = 0.04
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self.db = DBImpl(psycopg2, dbparams)
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self.client_sugg = sphinxapi.SphinxClient()
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self.client_sugg.SetServer("localhost", 9312)
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self.client_sugg.SetLimits(0, 10)
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self.client_sugg.SetConnectTimeout(3.0)
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self.client_show = sphinxapi.SphinxClient()
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self.client_show.SetServer("localhost", 9312)
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self.client_show.SetLimits(0, 10)
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self.client_show.SetConnectTimeout(3.0)
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def __configure(self, index_name, wlen=None):
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if index_name == "idx_fias_sugg":
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if wlen:
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self.client_sugg.SetMatchMode(sphinxapi.SPH_MATCH_EXTENDED2)
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self.client_sugg.SetRankingMode(sphinxapi.SPH_RANK_WORDCOUNT)
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self.client_sugg.SetFilterRange("len", int(wlen) - self.delta_len, int(wlen) + self.delta_len)
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self.client_sugg.SetSelect("word, len, @weight+{}-abs(len-{}) AS krank".format(self.delta_len, wlen))
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self.client_sugg.SetSortMode(sphinxapi.SPH_SORT_EXTENDED, "krank DESC")
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else:
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self.client_show.SetMatchMode(sphinxapi.SPH_MATCH_EXTENDED2)
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self.client_show.SetRankingMode(sphinxapi.SPH_RANK_BM25)
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def __get_suggest(self, word, rating_limit, count):
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word_len = str(len(word) / 2)
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trigrammed_word = '"{}"/1'.format(trigram(word))
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self.__configure(sphinx_index_sugg, word_len)
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result = self.client_sugg.Query(trigrammed_word, sphinx_index_sugg)
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# Если по данному слову не найдено подсказок (а такое бывает?)
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# возвращаем []
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if not result['matches']:
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return []
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maxrank = result['matches'][0]['attrs']['krank']
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maxleven = None
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outlist = list()
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for match in result['matches']:
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if len(outlist) >= count:
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break;
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if maxrank - match['attrs']['krank'] < self.default_rating_delta:
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jaro_rating = Levenshtein.jaro(word, match['attrs']['word'])
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if not maxleven:
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maxleven = jaro_rating - jaro_rating * self.regression_coef
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if jaro_rating >= rating_limit and jaro_rating >= maxleven:
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outlist.append([match['attrs']['word'], jaro_rating])
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outlist.sort(key=lambda x: x[1], reverse=True)
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return outlist
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def __split_phrase(self, phrase):
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phrase = unicode(phrase).replace('-', '').replace('@', '').lower()
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return re.split(r"[ ,:.#$]+", phrase)
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def __add_word_variations(self, word_entry):
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if word_entry.MT_MANY_SUGG:
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suggs = self.__get_suggest(word_entry.word, self.rating_limit_soft, 6)
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for suggestion in suggs:
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word_entry.add_variation(suggestion[0])
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if word_entry.MT_SOME_SUGG:
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suggs = self.__get_suggest(word_entry.word, self.rating_limit_hard, 3)
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for suggestion in suggs:
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word_entry.add_variation(suggestion[0])
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if word_entry.MT_LAST_STAR:
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word_entry.add_variation(word_entry.word+'*')
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if word_entry.MT_AS_IS:
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word_entry.add_variation(word_entry.word)
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if word_entry.MT_ADD_SOCR:
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word_entry.add_variation_socr()
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def __get_word_entries(self, words):
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for word in words:
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if word != '':
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we = WordEntry(self.db, word)
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self.__add_word_variations(we)
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yield we
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def find(self, text):
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words = self.__split_phrase(text)
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word_entries = self.__get_word_entries(words)
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sentence = "{}".format(" MAYBE ".join(x.get_variations() for x in word_entries))
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self.__configure(sphinx_index_addjobj)
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rs = self.client_show.Query(sentence, sphinx_index_addjobj)
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results = []
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for ma in rs['matches']:
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results.append([ma['attrs']['aoid'], ma['attrs']['fullname'], ma['weight']])
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print results
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