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dcdda314e2
Excluding non-rare full names is not really possible because it makes addresses with street names like 'main st' unsearchable. This tries to leav all names in but refrain from ordering results by accuracy when too many results are expected. This means that the DB will simply get the first n results without any particular order.
364 lines
16 KiB
Python
364 lines
16 KiB
Python
# SPDX-License-Identifier: GPL-3.0-or-later
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#
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# This file is part of Nominatim. (https://nominatim.org)
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#
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# Copyright (C) 2023 by the Nominatim developer community.
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# For a full list of authors see the git log.
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"""
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Convertion from token assignment to an abstract DB search.
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"""
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from typing import Optional, List, Tuple, Iterator
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import heapq
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from nominatim.api.types import SearchDetails, DataLayer
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from nominatim.api.search.query import QueryStruct, Token, TokenType, TokenRange, BreakType
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from nominatim.api.search.token_assignment import TokenAssignment
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import nominatim.api.search.db_search_fields as dbf
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import nominatim.api.search.db_searches as dbs
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def wrap_near_search(categories: List[Tuple[str, str]],
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search: dbs.AbstractSearch) -> dbs.NearSearch:
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""" Create a new search that wraps the given search in a search
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for near places of the given category.
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"""
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return dbs.NearSearch(penalty=search.penalty,
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categories=dbf.WeightedCategories(categories,
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[0.0] * len(categories)),
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search=search)
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def build_poi_search(category: List[Tuple[str, str]],
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countries: Optional[List[str]]) -> dbs.PoiSearch:
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""" Create a new search for places by the given category, possibly
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constraint to the given countries.
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"""
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if countries:
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ccs = dbf.WeightedStrings(countries, [0.0] * len(countries))
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else:
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ccs = dbf.WeightedStrings([], [])
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class _PoiData(dbf.SearchData):
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penalty = 0.0
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qualifiers = dbf.WeightedCategories(category, [0.0] * len(category))
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countries=ccs
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return dbs.PoiSearch(_PoiData())
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class SearchBuilder:
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""" Build the abstract search queries from token assignments.
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"""
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def __init__(self, query: QueryStruct, details: SearchDetails) -> None:
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self.query = query
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self.details = details
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@property
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def configured_for_country(self) -> bool:
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""" Return true if the search details are configured to
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allow countries in the result.
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"""
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return self.details.min_rank <= 4 and self.details.max_rank >= 4 \
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and self.details.layer_enabled(DataLayer.ADDRESS)
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@property
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def configured_for_postcode(self) -> bool:
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""" Return true if the search details are configured to
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allow postcodes in the result.
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"""
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return self.details.min_rank <= 5 and self.details.max_rank >= 11\
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and self.details.layer_enabled(DataLayer.ADDRESS)
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@property
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def configured_for_housenumbers(self) -> bool:
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""" Return true if the search details are configured to
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allow addresses in the result.
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"""
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return self.details.max_rank >= 30 \
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and self.details.layer_enabled(DataLayer.ADDRESS)
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def build(self, assignment: TokenAssignment) -> Iterator[dbs.AbstractSearch]:
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""" Yield all possible abstract searches for the given token assignment.
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"""
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sdata = self.get_search_data(assignment)
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if sdata is None:
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return
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categories = self.get_search_categories(assignment)
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if assignment.name is None:
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if categories and not sdata.postcodes:
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sdata.qualifiers = categories
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categories = None
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builder = self.build_poi_search(sdata)
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elif assignment.housenumber:
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hnr_tokens = self.query.get_tokens(assignment.housenumber,
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TokenType.HOUSENUMBER)
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builder = self.build_housenumber_search(sdata, hnr_tokens, assignment.address)
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else:
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builder = self.build_special_search(sdata, assignment.address,
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bool(categories))
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else:
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builder = self.build_name_search(sdata, assignment.name, assignment.address,
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bool(categories))
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if categories:
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penalty = min(categories.penalties)
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categories.penalties = [p - penalty for p in categories.penalties]
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for search in builder:
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yield dbs.NearSearch(penalty + assignment.penalty, categories, search)
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else:
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for search in builder:
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search.penalty += assignment.penalty
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yield search
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def build_poi_search(self, sdata: dbf.SearchData) -> Iterator[dbs.AbstractSearch]:
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""" Build abstract search query for a simple category search.
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This kind of search requires an additional geographic constraint.
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"""
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if not sdata.housenumbers \
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and ((self.details.viewbox and self.details.bounded_viewbox) or self.details.near):
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yield dbs.PoiSearch(sdata)
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def build_special_search(self, sdata: dbf.SearchData,
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address: List[TokenRange],
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is_category: bool) -> Iterator[dbs.AbstractSearch]:
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""" Build abstract search queries for searches that do not involve
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a named place.
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"""
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if sdata.qualifiers:
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# No special searches over qualifiers supported.
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return
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if sdata.countries and not address and not sdata.postcodes \
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and self.configured_for_country:
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yield dbs.CountrySearch(sdata)
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if sdata.postcodes and (is_category or self.configured_for_postcode):
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penalty = 0.0 if sdata.countries else 0.1
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if address:
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sdata.lookups = [dbf.FieldLookup('nameaddress_vector',
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[t.token for r in address
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for t in self.query.get_partials_list(r)],
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'restrict')]
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penalty += 0.2
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yield dbs.PostcodeSearch(penalty, sdata)
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def build_housenumber_search(self, sdata: dbf.SearchData, hnrs: List[Token],
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address: List[TokenRange]) -> Iterator[dbs.AbstractSearch]:
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""" Build a simple address search for special entries where the
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housenumber is the main name token.
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"""
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sdata.lookups = [dbf.FieldLookup('name_vector', [t.token for t in hnrs], 'lookup_any')]
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partials = [t for trange in address
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for t in self.query.get_partials_list(trange)]
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if len(partials) != 1 or partials[0].count < 10000:
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sdata.lookups.append(dbf.FieldLookup('nameaddress_vector',
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[t.token for t in partials], 'lookup_all'))
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else:
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sdata.lookups.append(
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dbf.FieldLookup('nameaddress_vector',
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[t.token for t
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in self.query.get_tokens(address[0], TokenType.WORD)],
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'lookup_any'))
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sdata.housenumbers = dbf.WeightedStrings([], [])
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yield dbs.PlaceSearch(0.05, sdata, sum(t.count for t in hnrs))
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def build_name_search(self, sdata: dbf.SearchData,
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name: TokenRange, address: List[TokenRange],
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is_category: bool) -> Iterator[dbs.AbstractSearch]:
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""" Build abstract search queries for simple name or address searches.
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"""
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if is_category or not sdata.housenumbers or self.configured_for_housenumbers:
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ranking = self.get_name_ranking(name)
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name_penalty = ranking.normalize_penalty()
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if ranking.rankings:
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sdata.rankings.append(ranking)
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for penalty, count, lookup in self.yield_lookups(name, address):
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sdata.lookups = lookup
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yield dbs.PlaceSearch(penalty + name_penalty, sdata, count)
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def yield_lookups(self, name: TokenRange, address: List[TokenRange])\
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-> Iterator[Tuple[float, int, List[dbf.FieldLookup]]]:
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""" Yield all variants how the given name and address should best
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be searched for. This takes into account how frequent the terms
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are and tries to find a lookup that optimizes index use.
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"""
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penalty = 0.0 # extra penalty
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name_partials = self.query.get_partials_list(name)
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name_tokens = [t.token for t in name_partials]
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addr_partials = [t for r in address for t in self.query.get_partials_list(r)]
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addr_tokens = [t.token for t in addr_partials]
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partials_indexed = all(t.is_indexed for t in name_partials) \
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and all(t.is_indexed for t in addr_partials)
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exp_count = min(t.count for t in name_partials)
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if (len(name_partials) > 3 or exp_count < 3000) and partials_indexed:
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yield penalty, exp_count, dbf.lookup_by_names(name_tokens, addr_tokens)
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return
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# Partial term to frequent. Try looking up by rare full names first.
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name_fulls = self.query.get_tokens(name, TokenType.WORD)
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fulls_count = sum(t.count for t in name_fulls) / (2**len(addr_partials))
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# At this point drop unindexed partials from the address.
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# This might yield wrong results, nothing we can do about that.
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if not partials_indexed:
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addr_tokens = [t.token for t in addr_partials if t.is_indexed]
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penalty += 1.2 * sum(t.penalty for t in addr_partials if not t.is_indexed)
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# Any of the full names applies with all of the partials from the address
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yield penalty, fulls_count,\
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dbf.lookup_by_any_name([t.token for t in name_fulls], addr_tokens,
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'restrict' if fulls_count < 10000 else 'lookup_all')
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# To catch remaining results, lookup by name and address
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# We only do this if there is a reasonable number of results expected.
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exp_count = exp_count / (2**len(addr_partials)) if addr_partials else exp_count
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if exp_count < 10000 and all(t.is_indexed for t in name_partials):
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lookup = [dbf.FieldLookup('name_vector', name_tokens, 'lookup_all')]
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if addr_tokens:
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lookup.append(dbf.FieldLookup('nameaddress_vector', addr_tokens, 'lookup_all'))
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penalty += 0.35 * max(0, 5 - len(name_partials) - len(addr_tokens))
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yield penalty, exp_count, lookup
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def get_name_ranking(self, trange: TokenRange) -> dbf.FieldRanking:
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""" Create a ranking expression for a name term in the given range.
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"""
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name_fulls = self.query.get_tokens(trange, TokenType.WORD)
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ranks = [dbf.RankedTokens(t.penalty, [t.token]) for t in name_fulls]
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ranks.sort(key=lambda r: r.penalty)
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# Fallback, sum of penalty for partials
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name_partials = self.query.get_partials_list(trange)
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default = sum(t.penalty for t in name_partials) + 0.2
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return dbf.FieldRanking('name_vector', default, ranks)
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def get_addr_ranking(self, trange: TokenRange) -> dbf.FieldRanking:
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""" Create a list of ranking expressions for an address term
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for the given ranges.
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"""
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todo: List[Tuple[int, int, dbf.RankedTokens]] = []
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heapq.heappush(todo, (0, trange.start, dbf.RankedTokens(0.0, [])))
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ranks: List[dbf.RankedTokens] = []
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while todo: # pylint: disable=too-many-nested-blocks
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neglen, pos, rank = heapq.heappop(todo)
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for tlist in self.query.nodes[pos].starting:
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if tlist.ttype in (TokenType.PARTIAL, TokenType.WORD):
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if tlist.end < trange.end:
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chgpenalty = PENALTY_WORDCHANGE[self.query.nodes[tlist.end].btype]
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if tlist.ttype == TokenType.PARTIAL:
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penalty = rank.penalty + chgpenalty \
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+ max(t.penalty for t in tlist.tokens)
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heapq.heappush(todo, (neglen - 1, tlist.end,
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dbf.RankedTokens(penalty, rank.tokens)))
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else:
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for t in tlist.tokens:
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heapq.heappush(todo, (neglen - 1, tlist.end,
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rank.with_token(t, chgpenalty)))
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elif tlist.end == trange.end:
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if tlist.ttype == TokenType.PARTIAL:
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ranks.append(dbf.RankedTokens(rank.penalty
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+ max(t.penalty for t in tlist.tokens),
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rank.tokens))
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else:
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ranks.extend(rank.with_token(t, 0.0) for t in tlist.tokens)
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if len(ranks) >= 10:
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# Too many variants, bail out and only add
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# Worst-case Fallback: sum of penalty of partials
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name_partials = self.query.get_partials_list(trange)
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default = sum(t.penalty for t in name_partials) + 0.2
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ranks.append(dbf.RankedTokens(rank.penalty + default, []))
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# Bail out of outer loop
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todo.clear()
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break
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ranks.sort(key=lambda r: len(r.tokens))
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default = ranks[0].penalty + 0.3
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del ranks[0]
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ranks.sort(key=lambda r: r.penalty)
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return dbf.FieldRanking('nameaddress_vector', default, ranks)
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def get_search_data(self, assignment: TokenAssignment) -> Optional[dbf.SearchData]:
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""" Collect the tokens for the non-name search fields in the
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assignment.
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"""
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sdata = dbf.SearchData()
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sdata.penalty = assignment.penalty
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if assignment.country:
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tokens = self.query.get_tokens(assignment.country, TokenType.COUNTRY)
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if self.details.countries:
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tokens = [t for t in tokens if t.lookup_word in self.details.countries]
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if not tokens:
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return None
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sdata.set_strings('countries', tokens)
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elif self.details.countries:
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sdata.countries = dbf.WeightedStrings(self.details.countries,
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[0.0] * len(self.details.countries))
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if assignment.housenumber:
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sdata.set_strings('housenumbers',
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self.query.get_tokens(assignment.housenumber,
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TokenType.HOUSENUMBER))
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if assignment.postcode:
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sdata.set_strings('postcodes',
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self.query.get_tokens(assignment.postcode,
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TokenType.POSTCODE))
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if assignment.qualifier:
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sdata.set_qualifiers(self.query.get_tokens(assignment.qualifier,
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TokenType.QUALIFIER))
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if assignment.address:
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sdata.set_ranking([self.get_addr_ranking(r) for r in assignment.address])
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else:
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sdata.rankings = []
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return sdata
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def get_search_categories(self,
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assignment: TokenAssignment) -> Optional[dbf.WeightedCategories]:
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""" Collect tokens for category search or use the categories
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requested per parameter.
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Returns None if no category search is requested.
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"""
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if assignment.category:
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tokens = [t for t in self.query.get_tokens(assignment.category,
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TokenType.CATEGORY)
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if not self.details.categories
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or t.get_category() in self.details.categories]
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return dbf.WeightedCategories([t.get_category() for t in tokens],
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[t.penalty for t in tokens])
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if self.details.categories:
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return dbf.WeightedCategories(self.details.categories,
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[0.0] * len(self.details.categories))
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return None
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PENALTY_WORDCHANGE = {
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BreakType.START: 0.0,
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BreakType.END: 0.0,
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BreakType.PHRASE: 0.0,
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BreakType.WORD: 0.1,
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BreakType.PART: 0.2,
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BreakType.TOKEN: 0.4
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}
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