nominatim/api/search/db_search_builder.py

   1 # SPDX-License-Identifier: GPL-3.0-or-later
   2 #
   3 # This file is part of Nominatim. (https://nominatim.org)
   4 #
   5 # Copyright (C) 2023 by the Nominatim developer community.
   6 # For a full list of authors see the git log.
   7 """
   8 Convertion from token assignment to an abstract DB search.
   9 """
  10 from typing import Optional, List, Tuple, Iterator
  11 import heapq
  12
  13 from nominatim.api.types import SearchDetails, DataLayer
  14 from nominatim.api.search.query import QueryStruct, Token, TokenType, TokenRange, BreakType
  15 from nominatim.api.search.token_assignment import TokenAssignment
  16 import nominatim.api.search.db_search_fields as dbf
  17 import nominatim.api.search.db_searches as dbs
  18
  19
  20 def wrap_near_search(categories: List[Tuple[str, str]],
  21                      search: dbs.AbstractSearch) -> dbs.NearSearch:
  22     """ Create a new search that wraps the given search in a search
  23         for near places of the given category.
  24     """
  25     return dbs.NearSearch(penalty=search.penalty,
  26                           categories=dbf.WeightedCategories(categories,
  27                                                             [0.0] * len(categories)),
  28                           search=search)
  29
  30
  31 def build_poi_search(category: List[Tuple[str, str]],
  32                      countries: Optional[List[str]]) -> dbs.PoiSearch:
  33     """ Create a new search for places by the given category, possibly
  34         constraint to the given countries.
  35     """
  36     if countries:
  37         ccs = dbf.WeightedStrings(countries, [0.0] * len(countries))
  38     else:
  39         ccs = dbf.WeightedStrings([], [])
  40
  41     class _PoiData(dbf.SearchData):
  42         penalty = 0.0
  43         qualifiers = dbf.WeightedCategories(category, [0.0] * len(category))
  44         countries=ccs
  45
  46     return dbs.PoiSearch(_PoiData())
  47
  48
  49 class SearchBuilder:
  50     """ Build the abstract search queries from token assignments.
  51     """
  52
  53     def __init__(self, query: QueryStruct, details: SearchDetails) -> None:
  54         self.query = query
  55         self.details = details
  56
  57
  58     @property
  59     def configured_for_country(self) -> bool:
  60         """ Return true if the search details are configured to
  61             allow countries in the result.
  62         """
  63         return self.details.min_rank <= 4 and self.details.max_rank >= 4 \
  64                and self.details.layer_enabled(DataLayer.ADDRESS)
  65
  66
  67     @property
  68     def configured_for_postcode(self) -> bool:
  69         """ Return true if the search details are configured to
  70             allow postcodes in the result.
  71         """
  72         return self.details.min_rank <= 5 and self.details.max_rank >= 11\
  73                and self.details.layer_enabled(DataLayer.ADDRESS)
  74
  75
  76     @property
  77     def configured_for_housenumbers(self) -> bool:
  78         """ Return true if the search details are configured to
  79             allow addresses in the result.
  80         """
  81         return self.details.max_rank >= 30 \
  82                and self.details.layer_enabled(DataLayer.ADDRESS)
  83
  84
  85     def build(self, assignment: TokenAssignment) -> Iterator[dbs.AbstractSearch]:
  86         """ Yield all possible abstract searches for the given token assignment.
  87         """
  88         sdata = self.get_search_data(assignment)
  89         if sdata is None:
  90             return
  91
  92         categories = self.get_search_categories(assignment)
  93
  94         if assignment.name is None:
  95             if categories and not sdata.postcodes:
  96                 sdata.qualifiers = categories
  97                 categories = None
  98                 builder = self.build_poi_search(sdata)
  99             elif assignment.housenumber:
 100                 hnr_tokens = self.query.get_tokens(assignment.housenumber,
 101                                                    TokenType.HOUSENUMBER)
 102                 builder = self.build_housenumber_search(sdata, hnr_tokens, assignment.address)
 103             else:
 104                 builder = self.build_special_search(sdata, assignment.address,
 105                                                     bool(categories))
 106         else:
 107             builder = self.build_name_search(sdata, assignment.name, assignment.address,
 108                                              bool(categories))
 109
 110         if categories:
 111             penalty = min(categories.penalties)
 112             categories.penalties = [p - penalty for p in categories.penalties]
 113             for search in builder:
 114                 yield dbs.NearSearch(penalty, categories, search)
 115         else:
 116             yield from builder
 117
 118
 119     def build_poi_search(self, sdata: dbf.SearchData) -> Iterator[dbs.AbstractSearch]:
 120         """ Build abstract search query for a simple category search.
 121             This kind of search requires an additional geographic constraint.
 122         """
 123         if not sdata.housenumbers \
 124            and ((self.details.viewbox and self.details.bounded_viewbox) or self.details.near):
 125             yield dbs.PoiSearch(sdata)
 126
 127
 128     def build_special_search(self, sdata: dbf.SearchData,
 129                              address: List[TokenRange],
 130                              is_category: bool) -> Iterator[dbs.AbstractSearch]:
 131         """ Build abstract search queries for searches that do not involve
 132             a named place.
 133         """
 134         if sdata.qualifiers:
 135             # No special searches over qualifiers supported.
 136             return
 137
 138         if sdata.countries and not address and not sdata.postcodes \
 139            and self.configured_for_country:
 140             yield dbs.CountrySearch(sdata)
 141
 142         if sdata.postcodes and (is_category or self.configured_for_postcode):
 143             penalty = 0.0 if sdata.countries else 0.1
 144             if address:
 145                 sdata.lookups = [dbf.FieldLookup('nameaddress_vector',
 146                                                  [t.token for r in address
 147                                                   for t in self.query.get_partials_list(r)],
 148                                                  'restrict')]
 149                 penalty += 0.2
 150             yield dbs.PostcodeSearch(penalty, sdata)
 151
 152
 153     def build_housenumber_search(self, sdata: dbf.SearchData, hnrs: List[Token],
 154                                  address: List[TokenRange]) -> Iterator[dbs.AbstractSearch]:
 155         """ Build a simple address search for special entries where the
 156             housenumber is the main name token.
 157         """
 158         sdata.lookups = [dbf.FieldLookup('name_vector', [t.token for t in hnrs], 'lookup_any')]
 159
 160         partials = [t for trange in address
 161                        for t in self.query.get_partials_list(trange)]
 162
 163         if len(partials) != 1 or partials[0].count < 10000:
 164             sdata.lookups.append(dbf.FieldLookup('nameaddress_vector',
 165                                                  [t.token for t in partials], 'lookup_all'))
 166         else:
 167             sdata.lookups.append(
 168                 dbf.FieldLookup('nameaddress_vector',
 169                                 [t.token for t
 170                                  in self.query.get_tokens(address[0], TokenType.WORD)],
 171                                 'lookup_any'))
 172
 173         sdata.housenumbers = dbf.WeightedStrings([], [])
 174         yield dbs.PlaceSearch(0.05, sdata, sum(t.count for t in hnrs))
 175
 176
 177     def build_name_search(self, sdata: dbf.SearchData,
 178                           name: TokenRange, address: List[TokenRange],
 179                           is_category: bool) -> Iterator[dbs.AbstractSearch]:
 180         """ Build abstract search queries for simple name or address searches.
 181         """
 182         if is_category or not sdata.housenumbers or self.configured_for_housenumbers:
 183             ranking = self.get_name_ranking(name)
 184             name_penalty = ranking.normalize_penalty()
 185             if ranking.rankings:
 186                 sdata.rankings.append(ranking)
 187             for penalty, count, lookup in self.yield_lookups(name, address):
 188                 sdata.lookups = lookup
 189                 yield dbs.PlaceSearch(penalty + name_penalty, sdata, count)
 190
 191
 192     def yield_lookups(self, name: TokenRange, address: List[TokenRange])\
 193                           -> Iterator[Tuple[float, int, List[dbf.FieldLookup]]]:
 194         """ Yield all variants how the given name and address should best
 195             be searched for. This takes into account how frequent the terms
 196             are and tries to find a lookup that optimizes index use.
 197         """
 198         penalty = 0.0 # extra penalty
 199         name_partials = self.query.get_partials_list(name)
 200         name_tokens = [t.token for t in name_partials]
 201
 202         addr_partials = [t for r in address for t in self.query.get_partials_list(r)]
 203         addr_tokens = [t.token for t in addr_partials]
 204
 205         partials_indexed = all(t.is_indexed for t in name_partials) \
 206                            and all(t.is_indexed for t in addr_partials)
 207         exp_count = min(t.count for t in name_partials)
 208
 209         if (len(name_partials) > 3 or exp_count < 1000) and partials_indexed:
 210             yield penalty, exp_count, dbf.lookup_by_names(name_tokens, addr_tokens)
 211             return
 212
 213         exp_count = min(exp_count, min(t.count for t in addr_partials)) \
 214                     if addr_partials else exp_count
 215
 216         # Partial term to frequent. Try looking up by rare full names first.
 217         name_fulls = self.query.get_tokens(name, TokenType.WORD)
 218         rare_names = list(filter(lambda t: t.count < 10000, name_fulls))
 219         # At this point drop unindexed partials from the address.
 220         # This might yield wrong results, nothing we can do about that.
 221         if not partials_indexed:
 222             addr_tokens = [t.token for t in addr_partials if t.is_indexed]
 223             penalty += 1.2 * sum(t.penalty for t in addr_partials if not t.is_indexed)
 224         if rare_names:
 225             # Any of the full names applies with all of the partials from the address
 226             yield penalty, sum(t.count for t in rare_names),\
 227                   dbf.lookup_by_any_name([t.token for t in rare_names], addr_tokens)
 228
 229         # To catch remaining results, lookup by name and address
 230         # We only do this if there is a reasonable number of results expected.
 231         if exp_count < 10000:
 232             if all(t.is_indexed for t in name_partials):
 233                 lookup = [dbf.FieldLookup('name_vector', name_tokens, 'lookup_all')]
 234             else:
 235                 # we don't have the partials, try with the non-rare names
 236                 non_rare_names = [t.token for t in name_fulls if t.count >= 10000]
 237                 if not non_rare_names:
 238                     return
 239                 lookup = [dbf.FieldLookup('name_vector', non_rare_names, 'lookup_any')]
 240             if addr_tokens:
 241                 lookup.append(dbf.FieldLookup('nameaddress_vector', addr_tokens, 'lookup_all'))
 242             penalty += 0.1 * max(0, 5 - len(name_partials) - len(addr_tokens))
 243             if len(rare_names) == len(name_fulls):
 244                 # if there already was a search for all full tokens,
 245                 # avoid this if anything has been found
 246                 penalty += 0.25
 247             yield penalty, exp_count, lookup
 248
 249
 250     def get_name_ranking(self, trange: TokenRange) -> dbf.FieldRanking:
 251         """ Create a ranking expression for a name term in the given range.
 252         """
 253         name_fulls = self.query.get_tokens(trange, TokenType.WORD)
 254         ranks = [dbf.RankedTokens(t.penalty, [t.token]) for t in name_fulls]
 255         ranks.sort(key=lambda r: r.penalty)
 256         # Fallback, sum of penalty for partials
 257         name_partials = self.query.get_partials_list(trange)
 258         default = sum(t.penalty for t in name_partials) + 0.2
 259         return dbf.FieldRanking('name_vector', default, ranks)
 260
 261
 262     def get_addr_ranking(self, trange: TokenRange) -> dbf.FieldRanking:
 263         """ Create a list of ranking expressions for an address term
 264             for the given ranges.
 265         """
 266         todo: List[Tuple[int, int, dbf.RankedTokens]] = []
 267         heapq.heappush(todo, (0, trange.start, dbf.RankedTokens(0.0, [])))
 268         ranks: List[dbf.RankedTokens] = []
 269
 270         while todo: # pylint: disable=too-many-nested-blocks
 271             neglen, pos, rank = heapq.heappop(todo)
 272             for tlist in self.query.nodes[pos].starting:
 273                 if tlist.ttype in (TokenType.PARTIAL, TokenType.WORD):
 274                     if tlist.end < trange.end:
 275                         chgpenalty = PENALTY_WORDCHANGE[self.query.nodes[tlist.end].btype]
 276                         if tlist.ttype == TokenType.PARTIAL:
 277                             penalty = rank.penalty + chgpenalty \
 278                                       + max(t.penalty for t in tlist.tokens)
 279                             heapq.heappush(todo, (neglen - 1, tlist.end,
 280                                                   dbf.RankedTokens(penalty, rank.tokens)))
 281                         else:
 282                             for t in tlist.tokens:
 283                                 heapq.heappush(todo, (neglen - 1, tlist.end,
 284                                                       rank.with_token(t, chgpenalty)))
 285                     elif tlist.end == trange.end:
 286                         if tlist.ttype == TokenType.PARTIAL:
 287                             ranks.append(dbf.RankedTokens(rank.penalty
 288                                                           + max(t.penalty for t in tlist.tokens),
 289                                                           rank.tokens))
 290                         else:
 291                             ranks.extend(rank.with_token(t, 0.0) for t in tlist.tokens)
 292                         if len(ranks) >= 10:
 293                             # Too many variants, bail out and only add
 294                             # Worst-case Fallback: sum of penalty of partials
 295                             name_partials = self.query.get_partials_list(trange)
 296                             default = sum(t.penalty for t in name_partials) + 0.2
 297                             ranks.append(dbf.RankedTokens(rank.penalty + default, []))
 298                             # Bail out of outer loop
 299                             todo.clear()
 300                             break
 301
 302         ranks.sort(key=lambda r: len(r.tokens))
 303         default = ranks[0].penalty + 0.3
 304         del ranks[0]
 305         ranks.sort(key=lambda r: r.penalty)
 306
 307         return dbf.FieldRanking('nameaddress_vector', default, ranks)
 308
 309
 310     def get_search_data(self, assignment: TokenAssignment) -> Optional[dbf.SearchData]:
 311         """ Collect the tokens for the non-name search fields in the
 312             assignment.
 313         """
 314         sdata = dbf.SearchData()
 315         sdata.penalty = assignment.penalty
 316         if assignment.country:
 317             tokens = self.query.get_tokens(assignment.country, TokenType.COUNTRY)
 318             if self.details.countries:
 319                 tokens = [t for t in tokens if t.lookup_word in self.details.countries]
 320                 if not tokens:
 321                     return None
 322             sdata.set_strings('countries', tokens)
 323         elif self.details.countries:
 324             sdata.countries = dbf.WeightedStrings(self.details.countries,
 325                                                   [0.0] * len(self.details.countries))
 326         if assignment.housenumber:
 327             sdata.set_strings('housenumbers',
 328                               self.query.get_tokens(assignment.housenumber,
 329                                                     TokenType.HOUSENUMBER))
 330         if assignment.postcode:
 331             sdata.set_strings('postcodes',
 332                               self.query.get_tokens(assignment.postcode,
 333                                                     TokenType.POSTCODE))
 334         if assignment.qualifier:
 335             sdata.set_qualifiers(self.query.get_tokens(assignment.qualifier,
 336                                                        TokenType.QUALIFIER))
 337
 338         if assignment.address:
 339             sdata.set_ranking([self.get_addr_ranking(r) for r in assignment.address])
 340         else:
 341             sdata.rankings = []
 342
 343         return sdata
 344
 345
 346     def get_search_categories(self,
 347                               assignment: TokenAssignment) -> Optional[dbf.WeightedCategories]:
 348         """ Collect tokens for category search or use the categories
 349             requested per parameter.
 350             Returns None if no category search is requested.
 351         """
 352         if assignment.category:
 353             tokens = [t for t in self.query.get_tokens(assignment.category,
 354                                                        TokenType.CATEGORY)
 355                       if not self.details.categories
 356                          or t.get_category() in self.details.categories]
 357             return dbf.WeightedCategories([t.get_category() for t in tokens],
 358                                           [t.penalty for t in tokens])
 359
 360         if self.details.categories:
 361             return dbf.WeightedCategories(self.details.categories,
 362                                           [0.0] * len(self.details.categories))
 363
 364         return None
 365
 366
 367 PENALTY_WORDCHANGE = {
 368     BreakType.START: 0.0,
 369     BreakType.END: 0.0,
 370     BreakType.PHRASE: 0.0,
 371     BreakType.WORD: 0.1,
 372     BreakType.PART: 0.2,
 373     BreakType.TOKEN: 0.4
 374 }