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https://github.com/osm-search/Nominatim.git
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dcfb228c9a
Search is now split into three functions: for free-text search, for structured search and for search by category. Note that the free-text search does not have as many hidden features like coordinate search. Use the search parameters for that.
353 lines
16 KiB
Python
353 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, 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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from nominatim.api.logging import log
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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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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, categories, search)
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else:
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yield from builder
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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 or sdata.housenumbers:
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# No special searches over housenumbers or 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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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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yield dbs.PostcodeSearch(0.4, sdata)
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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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sdata.rankings.append(self.get_name_ranking(name))
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name_penalty = sdata.rankings[-1].normalize_penalty()
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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 currently unused
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name_partials = self.query.get_partials_list(name)
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exp_name_count = min(t.count for t in name_partials)
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addr_partials = []
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for trange in address:
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addr_partials.extend(self.query.get_partials_list(trange))
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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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if (len(name_partials) > 3 or exp_name_count < 1000) and partials_indexed:
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# Lookup by name partials, use address partials to restrict results.
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lookup = [dbf.FieldLookup('name_vector',
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[t.token for t in name_partials], 'lookup_all')]
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if addr_tokens:
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lookup.append(dbf.FieldLookup('nameaddress_vector', addr_tokens, 'restrict'))
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yield penalty, exp_name_count, lookup
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return
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exp_addr_count = min(t.count for t in addr_partials) if addr_partials else exp_name_count
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if exp_addr_count < 1000 and partials_indexed:
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# Lookup by address partials and restrict results through name terms.
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yield penalty, exp_addr_count,\
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[dbf.FieldLookup('name_vector', [t.token for t in name_partials], 'restrict'),
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dbf.FieldLookup('nameaddress_vector', addr_tokens, 'lookup_all')]
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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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rare_names = list(filter(lambda t: t.count < 1000, name_fulls))
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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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log().var_dump('before', penalty)
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penalty += 1.2 * sum(t.penalty for t in addr_partials if not t.is_indexed)
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log().var_dump('after', penalty)
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if rare_names:
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# Any of the full names applies with all of the partials from the address
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lookup = [dbf.FieldLookup('name_vector', [t.token for t in rare_names], 'lookup_any')]
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if addr_tokens:
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lookup.append(dbf.FieldLookup('nameaddress_vector', addr_tokens, 'restrict'))
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yield penalty, sum(t.count for t in rare_names), lookup
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# To catch remaining results, lookup by name and address
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if all(t.is_indexed for t in name_partials):
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lookup = [dbf.FieldLookup('name_vector',
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[t.token for t in name_partials], 'lookup_all')]
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else:
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# we don't have the partials, try with the non-rare names
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non_rare_names = [t.token for t in name_fulls if t.count >= 1000]
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if not non_rare_names:
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return
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lookup = [dbf.FieldLookup('name_vector', non_rare_names, 'lookup_any')]
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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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yield penalty + 0.1 * max(0, 5 - len(name_partials) - len(addr_tokens)),\
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min(exp_name_count, exp_addr_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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