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         sdata.housenumbers = dbf.WeightedStrings([], [])
 167         yield dbs.PlaceSearch(0.05, sdata, sum(t.count for t in hnrs))
 168
 169
 170     def build_name_search(self, sdata: dbf.SearchData,
 171                           name: TokenRange, address: List[TokenRange],
 172                           is_category: bool) -> Iterator[dbs.AbstractSearch]:
 173         """ Build abstract search queries for simple name or address searches.
 174         """
 175         if is_category or not sdata.housenumbers or self.configured_for_housenumbers:
 176             ranking = self.get_name_ranking(name)
 177             name_penalty = ranking.normalize_penalty()
 178             if ranking.rankings:
 179                 sdata.rankings.append(ranking)
 180             for penalty, count, lookup in self.yield_lookups(name, address):
 181                 sdata.lookups = lookup
 182                 yield dbs.PlaceSearch(penalty + name_penalty, sdata, count)
 183
 184
 185     def yield_lookups(self, name: TokenRange, address: List[TokenRange])\
 186                           -> Iterator[Tuple[float, int, List[dbf.FieldLookup]]]:
 187         """ Yield all variants how the given name and address should best
 188             be searched for. This takes into account how frequent the terms
 189             are and tries to find a lookup that optimizes index use.
 190         """
 191         penalty = 0.0 # extra penalty currently unused
 192
 193         name_partials = self.query.get_partials_list(name)
 194         exp_name_count = min(t.count for t in name_partials)
 195         addr_partials = []
 196         for trange in address:
 197             addr_partials.extend(self.query.get_partials_list(trange))
 198         addr_tokens = [t.token for t in addr_partials]
 199         partials_indexed = all(t.is_indexed for t in name_partials) \
 200                            and all(t.is_indexed for t in addr_partials)
 201
 202         if (len(name_partials) > 3 or exp_name_count < 1000) and partials_indexed:
 203             # Lookup by name partials, use address partials to restrict results.
 204             lookup = [dbf.FieldLookup('name_vector',
 205                                   [t.token for t in name_partials], 'lookup_all')]
 206             if addr_tokens:
 207                 lookup.append(dbf.FieldLookup('nameaddress_vector', addr_tokens, 'restrict'))
 208             yield penalty, exp_name_count, lookup
 209             return
 210
 211         exp_addr_count = min(t.count for t in addr_partials) if addr_partials else exp_name_count
 212         if exp_addr_count < 1000 and partials_indexed:
 213             # Lookup by address partials and restrict results through name terms.
 214             # Give this a small penalty because lookups in the address index are
 215             # more expensive
 216             yield penalty + exp_addr_count/5000, exp_addr_count,\
 217                   [dbf.FieldLookup('name_vector', [t.token for t in name_partials], 'restrict'),
 218                    dbf.FieldLookup('nameaddress_vector', addr_tokens, 'lookup_all')]
 219             return
 220
 221         # Partial term to frequent. Try looking up by rare full names first.
 222         name_fulls = self.query.get_tokens(name, TokenType.WORD)
 223         rare_names = list(filter(lambda t: t.count < 1000, name_fulls))
 224         # At this point drop unindexed partials from the address.
 225         # This might yield wrong results, nothing we can do about that.
 226         if not partials_indexed:
 227             addr_tokens = [t.token for t in addr_partials if t.is_indexed]
 228             log().var_dump('before', penalty)
 229             penalty += 1.2 * sum(t.penalty for t in addr_partials if not t.is_indexed)
 230             log().var_dump('after', penalty)
 231         if rare_names:
 232             # Any of the full names applies with all of the partials from the address
 233             lookup = [dbf.FieldLookup('name_vector', [t.token for t in rare_names], 'lookup_any')]
 234             if addr_tokens:
 235                 lookup.append(dbf.FieldLookup('nameaddress_vector', addr_tokens, 'restrict'))
 236             yield penalty, sum(t.count for t in rare_names), lookup
 237
 238         # To catch remaining results, lookup by name and address
 239         # We only do this if there is a reasonable number of results expected.
 240         if min(exp_name_count, exp_addr_count) < 10000:
 241             if all(t.is_indexed for t in name_partials):
 242                 lookup = [dbf.FieldLookup('name_vector',
 243                                           [t.token for t in name_partials], 'lookup_all')]
 244             else:
 245                 # we don't have the partials, try with the non-rare names
 246                 non_rare_names = [t.token for t in name_fulls if t.count >= 1000]
 247                 if not non_rare_names:
 248                     return
 249                 lookup = [dbf.FieldLookup('name_vector', non_rare_names, 'lookup_any')]
 250             if addr_tokens:
 251                 lookup.append(dbf.FieldLookup('nameaddress_vector', addr_tokens, 'lookup_all'))
 252             yield penalty + 0.1 * max(0, 5 - len(name_partials) - len(addr_tokens)),\
 253                   min(exp_name_count, exp_addr_count), lookup
 254
 255
 256     def get_name_ranking(self, trange: TokenRange) -> dbf.FieldRanking:
 257         """ Create a ranking expression for a name term in the given range.
 258         """
 259         name_fulls = self.query.get_tokens(trange, TokenType.WORD)
 260         ranks = [dbf.RankedTokens(t.penalty, [t.token]) for t in name_fulls]
 261         ranks.sort(key=lambda r: r.penalty)
 262         # Fallback, sum of penalty for partials
 263         name_partials = self.query.get_partials_list(trange)
 264         default = sum(t.penalty for t in name_partials) + 0.2
 265         return dbf.FieldRanking('name_vector', default, ranks)
 266
 267
 268     def get_addr_ranking(self, trange: TokenRange) -> dbf.FieldRanking:
 269         """ Create a list of ranking expressions for an address term
 270             for the given ranges.
 271         """
 272         todo: List[Tuple[int, int, dbf.RankedTokens]] = []
 273         heapq.heappush(todo, (0, trange.start, dbf.RankedTokens(0.0, [])))
 274         ranks: List[dbf.RankedTokens] = []
 275
 276         while todo: # pylint: disable=too-many-nested-blocks
 277             neglen, pos, rank = heapq.heappop(todo)
 278             for tlist in self.query.nodes[pos].starting:
 279                 if tlist.ttype in (TokenType.PARTIAL, TokenType.WORD):
 280                     if tlist.end < trange.end:
 281                         chgpenalty = PENALTY_WORDCHANGE[self.query.nodes[tlist.end].btype]
 282                         if tlist.ttype == TokenType.PARTIAL:
 283                             penalty = rank.penalty + chgpenalty \
 284                                       + max(t.penalty for t in tlist.tokens)
 285                             heapq.heappush(todo, (neglen - 1, tlist.end,
 286                                                   dbf.RankedTokens(penalty, rank.tokens)))
 287                         else:
 288                             for t in tlist.tokens:
 289                                 heapq.heappush(todo, (neglen - 1, tlist.end,
 290                                                       rank.with_token(t, chgpenalty)))
 291                     elif tlist.end == trange.end:
 292                         if tlist.ttype == TokenType.PARTIAL:
 293                             ranks.append(dbf.RankedTokens(rank.penalty
 294                                                           + max(t.penalty for t in tlist.tokens),
 295                                                           rank.tokens))
 296                         else:
 297                             ranks.extend(rank.with_token(t, 0.0) for t in tlist.tokens)
 298                         if len(ranks) >= 10:
 299                             # Too many variants, bail out and only add
 300                             # Worst-case Fallback: sum of penalty of partials
 301                             name_partials = self.query.get_partials_list(trange)
 302                             default = sum(t.penalty for t in name_partials) + 0.2
 303                             ranks.append(dbf.RankedTokens(rank.penalty + default, []))
 304                             # Bail out of outer loop
 305                             todo.clear()
 306                             break
 307
 308         ranks.sort(key=lambda r: len(r.tokens))
 309         default = ranks[0].penalty + 0.3
 310         del ranks[0]
 311         ranks.sort(key=lambda r: r.penalty)
 312
 313         return dbf.FieldRanking('nameaddress_vector', default, ranks)
 314
 315
 316     def get_search_data(self, assignment: TokenAssignment) -> Optional[dbf.SearchData]:
 317         """ Collect the tokens for the non-name search fields in the
 318             assignment.
 319         """
 320         sdata = dbf.SearchData()
 321         sdata.penalty = assignment.penalty
 322         if assignment.country:
 323             tokens = self.query.get_tokens(assignment.country, TokenType.COUNTRY)
 324             if self.details.countries:
 325                 tokens = [t for t in tokens if t.lookup_word in self.details.countries]
 326                 if not tokens:
 327                     return None
 328             sdata.set_strings('countries', tokens)
 329         elif self.details.countries:
 330             sdata.countries = dbf.WeightedStrings(self.details.countries,
 331                                                   [0.0] * len(self.details.countries))
 332         if assignment.housenumber:
 333             sdata.set_strings('housenumbers',
 334                               self.query.get_tokens(assignment.housenumber,
 335                                                     TokenType.HOUSENUMBER))
 336         if assignment.postcode:
 337             sdata.set_strings('postcodes',
 338                               self.query.get_tokens(assignment.postcode,
 339                                                     TokenType.POSTCODE))
 340         if assignment.qualifier:
 341             sdata.set_qualifiers(self.query.get_tokens(assignment.qualifier,
 342                                                        TokenType.QUALIFIER))
 343
 344         if assignment.address:
 345             sdata.set_ranking([self.get_addr_ranking(r) for r in assignment.address])
 346         else:
 347             sdata.rankings = []
 348
 349         return sdata
 350
 351
 352     def get_search_categories(self,
 353                               assignment: TokenAssignment) -> Optional[dbf.WeightedCategories]:
 354         """ Collect tokens for category search or use the categories
 355             requested per parameter.
 356             Returns None if no category search is requested.
 357         """
 358         if assignment.category:
 359             tokens = [t for t in self.query.get_tokens(assignment.category,
 360                                                        TokenType.CATEGORY)
 361                       if not self.details.categories
 362                          or t.get_category() in self.details.categories]
 363             return dbf.WeightedCategories([t.get_category() for t in tokens],
 364                                           [t.penalty for t in tokens])
 365
 366         if self.details.categories:
 367             return dbf.WeightedCategories(self.details.categories,
 368                                           [0.0] * len(self.details.categories))
 369
 370         return None
 371
 372
 373 PENALTY_WORDCHANGE = {
 374     BreakType.START: 0.0,
 375     BreakType.END: 0.0,
 376     BreakType.PHRASE: 0.0,
 377     BreakType.WORD: 0.1,
 378     BreakType.PART: 0.2,
 379     BreakType.TOKEN: 0.4
 380 }