144 lines
5.4 KiB
Python
144 lines
5.4 KiB
Python
# -*- coding: utf-8 -*-
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import logging
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import re
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import Levenshtein
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import sphinxapi
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from aore.config import sphinx_conf
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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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# Config's
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delta_len = 2
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rating_limit_soft = 0.41
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rating_limit_soft_count = 6
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rating_limit_hard = 0.82
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rating_limit_hard_count = 3
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default_rating_delta = 2
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regression_coef = 0.08
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max_result = 10
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def __init__(self, db):
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self.db = db
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self.client_sugg = sphinxapi.SphinxClient()
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self.client_sugg.SetServer(sphinx_conf.host_name, sphinx_conf.port)
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self.client_sugg.SetLimits(0, self.max_result)
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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(sphinx_conf.host_name, sphinx_conf.port)
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self.client_show.SetLimits(0, self.max_result)
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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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self.client_sugg.ResetFilters()
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if index_name == sphinx_conf.index_sugg and wlen:
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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.SetRankingMode(sphinxapi.SPH_RANK_BM25)
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self.client_show.SetSortMode(sphinxapi.SPH_SORT_RELEVANCE)
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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_conf.index_sugg, word_len)
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result = self.client_sugg.Query(trigrammed_word, sphinx_conf.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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del 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 __add_word_variations(self, word_entry, strong):
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if word_entry.MT_MANY_SUGG and not strong:
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suggs = self.__get_suggest(word_entry.word, self.rating_limit_soft, self.rating_limit_soft_count)
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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 and not strong:
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suggs = self.__get_suggest(word_entry.word, self.rating_limit_hard, self.rating_limit_hard_count)
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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, strong):
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we_list = []
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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, strong)
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assert we.get_variations() != "", "Cannot process sentence."
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we_list.append(we)
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return we_list
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def find(self, text, strong):
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def split_phrase(phrase):
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phrase = unicode(phrase).replace('-', '').replace('@', '').lower()
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return re.split(r"[ ,:.#$]+", phrase)
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words = split_phrase(text)
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word_entries = self.__get_word_entries(words, strong)
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word_count = len(word_entries)
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for x in range(word_count, max(0, word_count - 3), -1):
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logging.info("\"{}\"/{}".format(" ".join(x.get_variations() for x in word_entries), x))
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self.client_show.AddQuery("\"{}\"/{}".format(" ".join(x.get_variations() for x in word_entries), x),
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sphinx_conf.index_addjobj)
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self.__configure(sphinx_conf.index_addjobj)
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logging.info("QUERY ")
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rs = self.client_show.RunQueries()
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logging.info("OK")
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results = []
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parsed_ids = []
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for i in range(0, len(rs)):
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for ma in rs[i]['matches']:
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if len(results) >= self.max_result:
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break
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if not ma['attrs']['aoid'] in parsed_ids:
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parsed_ids.append(ma['attrs']['aoid'])
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results.append(
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dict(aoid=ma['attrs']['aoid'], text=ma['attrs']['fullname'], ratio=ma['weight'], cort=i))
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if strong:
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results.sort(key=lambda x: Levenshtein.ratio(text, x['text']), reverse=True)
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return results
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