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 from nominatim.api.logging import log
  19
  20
  21 def wrap_near_search(categories: List[Tuple[str, str]],
  22                      search: dbs.AbstractSearch) -> dbs.NearSearch:
  23     """ Create a new search that wraps the given search in a search
  24         for near places of the given category.
  25     """
  26     return dbs.NearSearch(penalty=search.penalty,
  27                           categories=dbf.WeightedCategories(categories,
  28                                                             [0.0] * len(categories)),
  29                           search=search)
  30
  31
  32 def build_poi_search(category: List[Tuple[str, str]],
  33                      countries: Optional[List[str]]) -> dbs.PoiSearch:
  34     """ Create a new search for places by the given category, possibly
  35         constraint to the given countries.
  36     """
  37     if countries:
  38         ccs = dbf.WeightedStrings(countries, [0.0] * len(countries))
  39     else:
  40         ccs = dbf.WeightedStrings([], [])
  41
  42     class _PoiData(dbf.SearchData):
  43         penalty = 0.0
  44         qualifiers = dbf.WeightedCategories(category, [0.0] * len(category))
  45         countries=ccs
  46
  47     return dbs.PoiSearch(_PoiData())
  48
  49
  50 class SearchBuilder:
  51     """ Build the abstract search queries from token assignments.
  52     """
  53
  54     def __init__(self, query: QueryStruct, details: SearchDetails) -> None:
  55         self.query = query
  56         self.details = details
  57
  58
  59     @property
  60     def configured_for_country(self) -> bool:
  61         """ Return true if the search details are configured to
  62             allow countries in the result.
  63         """
  64         return self.details.min_rank <= 4 and self.details.max_rank >= 4 \
  65                and self.details.layer_enabled(DataLayer.ADDRESS)
  66
  67
  68     @property
  69     def configured_for_postcode(self) -> bool:
  70         """ Return true if the search details are configured to
  71             allow postcodes in the result.
  72         """
  73         return self.details.min_rank <= 5 and self.details.max_rank >= 11\
  74                and self.details.layer_enabled(DataLayer.ADDRESS)
  75
  76
  77     @property
  78     def configured_for_housenumbers(self) -> bool:
  79         """ Return true if the search details are configured to
  80             allow addresses in the result.
  81         """
  82         return self.details.max_rank >= 30 \
  83                and self.details.layer_enabled(DataLayer.ADDRESS)
  84
  85
  86     def build(self, assignment: TokenAssignment) -> Iterator[dbs.AbstractSearch]:
  87         """ Yield all possible abstract searches for the given token assignment.
  88         """
  89         sdata = self.get_search_data(assignment)
  90         if sdata is None:
  91             return
  92
  93         categories = self.get_search_categories(assignment)
  94
  95         if assignment.name is None:
  96             if categories and not sdata.postcodes:
  97                 sdata.qualifiers = categories
  98                 categories = None
  99                 builder = self.build_poi_search(sdata)
 100             elif assignment.housenumber:
 101                 hnr_tokens = self.query.get_tokens(assignment.housenumber,
 102                                                    TokenType.HOUSENUMBER)
 103                 builder = self.build_housenumber_search(sdata, hnr_tokens, assignment.address)
 104             else:
 105                 builder = self.build_special_search(sdata, assignment.address,
 106                                                     bool(categories))
 107         else:
 108             builder = self.build_name_search(sdata, assignment.name, assignment.address,
 109                                              bool(categories))
 110
 111         if categories:
 112             penalty = min(categories.penalties)
 113             categories.penalties = [p - penalty for p in categories.penalties]
 114             for search in builder:
 115                 yield dbs.NearSearch(penalty, categories, search)
 116         else:
 117             yield from builder
 118
 119
 120     def build_poi_search(self, sdata: dbf.SearchData) -> Iterator[dbs.AbstractSearch]:
 121         """ Build abstract search query for a simple category search.
 122             This kind of search requires an additional geographic constraint.
 123         """
 124         if not sdata.housenumbers \
 125            and ((self.details.viewbox and self.details.bounded_viewbox) or self.details.near):
 126             yield dbs.PoiSearch(sdata)
 127
 128
 129     def build_special_search(self, sdata: dbf.SearchData,
 130                              address: List[TokenRange],
 131                              is_category: bool) -> Iterator[dbs.AbstractSearch]:
 132         """ Build abstract search queries for searches that do not involve
 133             a named place.
 134         """
 135         if sdata.qualifiers:
 136             # No special searches over qualifiers supported.
 137             return
 138
 139         if sdata.countries and not address and not sdata.postcodes \
 140            and self.configured_for_country:
 141             yield dbs.CountrySearch(sdata)
 142
 143         if sdata.postcodes and (is_category or self.configured_for_postcode):
 144             penalty = 0.0 if sdata.countries else 0.1
 145             if address:
 146                 sdata.lookups = [dbf.FieldLookup('nameaddress_vector',
 147                                                  [t.token for r in address
 148                                                   for t in self.query.get_partials_list(r)],
 149                                                  'restrict')]
 150                 penalty += 0.2
 151             yield dbs.PostcodeSearch(penalty, sdata)
 152
 153
 154     def build_housenumber_search(self, sdata: dbf.SearchData, hnrs: List[Token],
 155                                  address: List[TokenRange]) -> Iterator[dbs.AbstractSearch]:
 156         """ Build a simple address search for special entries where the
 157             housenumber is the main name token.
 158         """
 159         partial_tokens: List[int] = []
 160         for trange in address:
 161             partial_tokens.extend(t.token for t in self.query.get_partials_list(trange))
 162
 163         sdata.lookups = [dbf.FieldLookup('name_vector', [t.token for t in hnrs], 'lookup_any'),
 164                          dbf.FieldLookup('nameaddress_vector', partial_tokens, 'lookup_all')
 165                         ]
 166         yield dbs.PlaceSearch(0.05, sdata, sum(t.count for t in hnrs))
 167
 168
 169     def build_name_search(self, sdata: dbf.SearchData,
 170                           name: TokenRange, address: List[TokenRange],
 171                           is_category: bool) -> Iterator[dbs.AbstractSearch]:
 172         """ Build abstract search queries for simple name or address searches.
 173         """
 174         if is_category or not sdata.housenumbers or self.configured_for_housenumbers:
 175             ranking = self.get_name_ranking(name)
 176             name_penalty = ranking.normalize_penalty()
 177             if ranking.rankings:
 178                 sdata.rankings.append(ranking)
 179             for penalty, count, lookup in self.yield_lookups(name, address):
 180                 sdata.lookups = lookup
 181                 yield dbs.PlaceSearch(penalty + name_penalty, sdata, count)
 182
 183
 184     def yield_lookups(self, name: TokenRange, address: List[TokenRange])\
 185                           -> Iterator[Tuple[float, int, List[dbf.FieldLookup]]]:
 186         """ Yield all variants how the given name and address should best
 187             be searched for. This takes into account how frequent the terms
 188             are and tries to find a lookup that optimizes index use.
 189         """
 190         penalty = 0.0 # extra penalty currently unused
 191
 192         name_partials = self.query.get_partials_list(name)
 193         exp_name_count = min(t.count for t in name_partials)
 194         addr_partials = []
 195         for trange in address:
 196             addr_partials.extend(self.query.get_partials_list(trange))
 197         addr_tokens = [t.token for t in addr_partials]
 198         partials_indexed = all(t.is_indexed for t in name_partials) \
 199                            and all(t.is_indexed for t in addr_partials)
 200
 201         if (len(name_partials) > 3 or exp_name_count < 1000) and partials_indexed:
 202             # Lookup by name partials, use address partials to restrict results.
 203             lookup = [dbf.FieldLookup('name_vector',
 204                                   [t.token for t in name_partials], 'lookup_all')]
 205             if addr_tokens:
 206                 lookup.append(dbf.FieldLookup('nameaddress_vector', addr_tokens, 'restrict'))
 207             yield penalty, exp_name_count, lookup
 208             return
 209
 210         exp_addr_count = min(t.count for t in addr_partials) if addr_partials else exp_name_count
 211         if exp_addr_count < 1000 and partials_indexed:
 212             # Lookup by address partials and restrict results through name terms.
 213             # Give this a small penalty because lookups in the address index are
 214             # more expensive
 215             yield penalty + exp_addr_count/5000, exp_addr_count,\
 216                   [dbf.FieldLookup('name_vector', [t.token for t in name_partials], 'restrict'),
 217                    dbf.FieldLookup('nameaddress_vector', addr_tokens, 'lookup_all')]
 218             return
 219
 220         # Partial term to frequent. Try looking up by rare full names first.
 221         name_fulls = self.query.get_tokens(name, TokenType.WORD)
 222         rare_names = list(filter(lambda t: t.count < 1000, name_fulls))
 223         # At this point drop unindexed partials from the address.
 224         # This might yield wrong results, nothing we can do about that.
 225         if not partials_indexed:
 226             addr_tokens = [t.token for t in addr_partials if t.is_indexed]
 227             log().var_dump('before', penalty)
 228             penalty += 1.2 * sum(t.penalty for t in addr_partials if not t.is_indexed)
 229             log().var_dump('after', penalty)
 230         if rare_names:
 231             # Any of the full names applies with all of the partials from the address
 232             lookup = [dbf.FieldLookup('name_vector', [t.token for t in rare_names], 'lookup_any')]
 233             if addr_tokens:
 234                 lookup.append(dbf.FieldLookup('nameaddress_vector', addr_tokens, 'restrict'))
 235             yield penalty, sum(t.count for t in rare_names), lookup
 236
 237         # To catch remaining results, lookup by name and address
 238         # We only do this if there is a reasonable number of results expected.
 239         if min(exp_name_count, exp_addr_count) < 10000:
 240             if all(t.is_indexed for t in name_partials):
 241                 lookup = [dbf.FieldLookup('name_vector',
 242                                           [t.token for t in name_partials], 'lookup_all')]
 243             else:
 244                 # we don't have the partials, try with the non-rare names
 245                 non_rare_names = [t.token for t in name_fulls if t.count >= 1000]
 246                 if not non_rare_names:
 247                     return
 248                 lookup = [dbf.FieldLookup('name_vector', non_rare_names, 'lookup_any')]
 249             if addr_tokens:
 250                 lookup.append(dbf.FieldLookup('nameaddress_vector', addr_tokens, 'lookup_all'))
 251             yield penalty + 0.1 * max(0, 5 - len(name_partials) - len(addr_tokens)),\
 252                   min(exp_name_count, exp_addr_count), lookup
 253
 254
 255     def get_name_ranking(self, trange: TokenRange) -> dbf.FieldRanking:
 256         """ Create a ranking expression for a name term in the given range.
 257         """
 258         name_fulls = self.query.get_tokens(trange, TokenType.WORD)
 259         ranks = [dbf.RankedTokens(t.penalty, [t.token]) for t in name_fulls]
 260         ranks.sort(key=lambda r: r.penalty)
 261         # Fallback, sum of penalty for partials
 262         name_partials = self.query.get_partials_list(trange)
 263         default = sum(t.penalty for t in name_partials) + 0.2
 264         return dbf.FieldRanking('name_vector', default, ranks)
 265
 266
 267     def get_addr_ranking(self, trange: TokenRange) -> dbf.FieldRanking:
 268         """ Create a list of ranking expressions for an address term
 269             for the given ranges.
 270         """
 271         todo: List[Tuple[int, int, dbf.RankedTokens]] = []
 272         heapq.heappush(todo, (0, trange.start, dbf.RankedTokens(0.0, [])))
 273         ranks: List[dbf.RankedTokens] = []
 274
 275         while todo: # pylint: disable=too-many-nested-blocks
 276             neglen, pos, rank = heapq.heappop(todo)
 277             for tlist in self.query.nodes[pos].starting:
 278                 if tlist.ttype in (TokenType.PARTIAL, TokenType.WORD):
 279                     if tlist.end < trange.end:
 280                         chgpenalty = PENALTY_WORDCHANGE[self.query.nodes[tlist.end].btype]
 281                         if tlist.ttype == TokenType.PARTIAL:
 282                             penalty = rank.penalty + chgpenalty \
 283                                       + max(t.penalty for t in tlist.tokens)
 284                             heapq.heappush(todo, (neglen - 1, tlist.end,
 285                                                   dbf.RankedTokens(penalty, rank.tokens)))
 286                         else:
 287                             for t in tlist.tokens:
 288                                 heapq.heappush(todo, (neglen - 1, tlist.end,
 289                                                       rank.with_token(t, chgpenalty)))
 290                     elif tlist.end == trange.end:
 291                         if tlist.ttype == TokenType.PARTIAL:
 292                             ranks.append(dbf.RankedTokens(rank.penalty
 293                                                           + max(t.penalty for t in tlist.tokens),
 294                                                           rank.tokens))
 295                         else:
 296                             ranks.extend(rank.with_token(t, 0.0) for t in tlist.tokens)
 297                         if len(ranks) >= 10:
 298                             # Too many variants, bail out and only add
 299                             # Worst-case Fallback: sum of penalty of partials
 300                             name_partials = self.query.get_partials_list(trange)
 301                             default = sum(t.penalty for t in name_partials) + 0.2
 302                             ranks.append(dbf.RankedTokens(rank.penalty + default, []))
 303                             # Bail out of outer loop
 304                             todo.clear()
 305                             break
 306
 307         ranks.sort(key=lambda r: len(r.tokens))
 308         default = ranks[0].penalty + 0.3
 309         del ranks[0]
 310         ranks.sort(key=lambda r: r.penalty)
 311
 312         return dbf.FieldRanking('nameaddress_vector', default, ranks)
 313
 314
 315     def get_search_data(self, assignment: TokenAssignment) -> Optional[dbf.SearchData]:
 316         """ Collect the tokens for the non-name search fields in the
 317             assignment.
 318         """
 319         sdata = dbf.SearchData()
 320         sdata.penalty = assignment.penalty
 321         if assignment.country:
 322             tokens = self.query.get_tokens(assignment.country, TokenType.COUNTRY)
 323             if self.details.countries:
 324                 tokens = [t for t in tokens if t.lookup_word in self.details.countries]
 325                 if not tokens:
 326                     return None
 327             sdata.set_strings('countries', tokens)
 328         elif self.details.countries:
 329             sdata.countries = dbf.WeightedStrings(self.details.countries,
 330                                                   [0.0] * len(self.details.countries))
 331         if assignment.housenumber:
 332             sdata.set_strings('housenumbers',
 333                               self.query.get_tokens(assignment.housenumber,
 334                                                     TokenType.HOUSENUMBER))
 335         if assignment.postcode:
 336             sdata.set_strings('postcodes',
 337                               self.query.get_tokens(assignment.postcode,
 338                                                     TokenType.POSTCODE))
 339         if assignment.qualifier:
 340             sdata.set_qualifiers(self.query.get_tokens(assignment.qualifier,
 341                                                        TokenType.QUALIFIER))
 342
 343         if assignment.address:
 344             sdata.set_ranking([self.get_addr_ranking(r) for r in assignment.address])
 345         else:
 346             sdata.rankings = []
 347
 348         return sdata
 349
 350
 351     def get_search_categories(self,
 352                               assignment: TokenAssignment) -> Optional[dbf.WeightedCategories]:
 353         """ Collect tokens for category search or use the categories
 354             requested per parameter.
 355             Returns None if no category search is requested.
 356         """
 357         if assignment.category:
 358             tokens = [t for t in self.query.get_tokens(assignment.category,
 359                                                        TokenType.CATEGORY)
 360                       if not self.details.categories
 361                          or t.get_category() in self.details.categories]
 362             return dbf.WeightedCategories([t.get_category() for t in tokens],
 363                                           [t.penalty for t in tokens])
 364
 365         if self.details.categories:
 366             return dbf.WeightedCategories(self.details.categories,
 367                                           [0.0] * len(self.details.categories))
 368
 369         return None
 370
 371
 372 PENALTY_WORDCHANGE = {
 373     BreakType.START: 0.0,
 374     BreakType.END: 0.0,
 375     BreakType.PHRASE: 0.0,
 376     BreakType.WORD: 0.1,
 377     BreakType.PART: 0.2,
 378     BreakType.TOKEN: 0.4
 379 }