src/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) 2024 by the Nominatim developer community.
   6 # For a full list of authors see the git log.
   7 """
   8 Conversion from token assignment to an abstract DB search.
   9 """
  10 from typing import Optional, List, Tuple, Iterator, Dict
  11 import heapq
  12
  13 from ..types import SearchDetails, DataLayer
  14 from .query import QueryStruct, Token, TokenType, TokenRange, BreakType
  15 from .token_assignment import TokenAssignment
  16 from . import db_search_fields as dbf
  17 from . import db_searches as dbs
  18 from . import db_search_lookups as lookups
  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         near_items = self.get_near_items(assignment)
  94         if near_items is not None and not near_items:
  95             return # impossible compbination of near items and category parameter
  96
  97         if assignment.name is None:
  98             if near_items and not sdata.postcodes:
  99                 sdata.qualifiers = near_items
 100                 near_items = None
 101                 builder = self.build_poi_search(sdata)
 102             elif assignment.housenumber:
 103                 hnr_tokens = self.query.get_tokens(assignment.housenumber,
 104                                                    TokenType.HOUSENUMBER)
 105                 builder = self.build_housenumber_search(sdata, hnr_tokens, assignment.address)
 106             else:
 107                 builder = self.build_special_search(sdata, assignment.address,
 108                                                     bool(near_items))
 109         else:
 110             builder = self.build_name_search(sdata, assignment.name, assignment.address,
 111                                              bool(near_items))
 112
 113         if near_items:
 114             penalty = min(near_items.penalties)
 115             near_items.penalties = [p - penalty for p in near_items.penalties]
 116             for search in builder:
 117                 search_penalty = search.penalty
 118                 search.penalty = 0.0
 119                 yield dbs.NearSearch(penalty + assignment.penalty + search_penalty,
 120                                      near_items, search)
 121         else:
 122             for search in builder:
 123                 search.penalty += assignment.penalty
 124                 yield search
 125
 126
 127     def build_poi_search(self, sdata: dbf.SearchData) -> Iterator[dbs.AbstractSearch]:
 128         """ Build abstract search query for a simple category search.
 129             This kind of search requires an additional geographic constraint.
 130         """
 131         if not sdata.housenumbers \
 132            and ((self.details.viewbox and self.details.bounded_viewbox) or self.details.near):
 133             yield dbs.PoiSearch(sdata)
 134
 135
 136     def build_special_search(self, sdata: dbf.SearchData,
 137                              address: List[TokenRange],
 138                              is_category: bool) -> Iterator[dbs.AbstractSearch]:
 139         """ Build abstract search queries for searches that do not involve
 140             a named place.
 141         """
 142         if sdata.qualifiers:
 143             # No special searches over qualifiers supported.
 144             return
 145
 146         if sdata.countries and not address and not sdata.postcodes \
 147            and self.configured_for_country:
 148             yield dbs.CountrySearch(sdata)
 149
 150         if sdata.postcodes and (is_category or self.configured_for_postcode):
 151             penalty = 0.0 if sdata.countries else 0.1
 152             if address:
 153                 sdata.lookups = [dbf.FieldLookup('nameaddress_vector',
 154                                                  [t.token for r in address
 155                                                   for t in self.query.get_partials_list(r)],
 156                                                  lookups.Restrict)]
 157                 penalty += 0.2
 158             yield dbs.PostcodeSearch(penalty, sdata)
 159
 160
 161     def build_housenumber_search(self, sdata: dbf.SearchData, hnrs: List[Token],
 162                                  address: List[TokenRange]) -> Iterator[dbs.AbstractSearch]:
 163         """ Build a simple address search for special entries where the
 164             housenumber is the main name token.
 165         """
 166         sdata.lookups = [dbf.FieldLookup('name_vector', [t.token for t in hnrs], lookups.LookupAny)]
 167         expected_count = sum(t.count for t in hnrs)
 168
 169         partials = {t.token: t.addr_count for trange in address
 170                        for t in self.query.get_partials_list(trange)}
 171
 172         if not partials:
 173             # can happen when none of the partials is indexed
 174             return
 175
 176         if expected_count < 8000:
 177             sdata.lookups.append(dbf.FieldLookup('nameaddress_vector',
 178                                                  list(partials), lookups.Restrict))
 179         elif len(partials) != 1 or list(partials.values())[0] < 10000:
 180             sdata.lookups.append(dbf.FieldLookup('nameaddress_vector',
 181                                                  list(partials), lookups.LookupAll))
 182         else:
 183             addr_fulls = [t.token for t
 184                           in self.query.get_tokens(address[0], TokenType.WORD)]
 185             if len(addr_fulls) > 5:
 186                 return
 187             sdata.lookups.append(
 188                 dbf.FieldLookup('nameaddress_vector', addr_fulls, lookups.LookupAny))
 189
 190         sdata.housenumbers = dbf.WeightedStrings([], [])
 191         yield dbs.PlaceSearch(0.05, sdata, expected_count)
 192
 193
 194     def build_name_search(self, sdata: dbf.SearchData,
 195                           name: TokenRange, address: List[TokenRange],
 196                           is_category: bool) -> Iterator[dbs.AbstractSearch]:
 197         """ Build abstract search queries for simple name or address searches.
 198         """
 199         if is_category or not sdata.housenumbers or self.configured_for_housenumbers:
 200             ranking = self.get_name_ranking(name)
 201             name_penalty = ranking.normalize_penalty()
 202             if ranking.rankings:
 203                 sdata.rankings.append(ranking)
 204             for penalty, count, lookup in self.yield_lookups(name, address):
 205                 sdata.lookups = lookup
 206                 yield dbs.PlaceSearch(penalty + name_penalty, sdata, count)
 207
 208
 209     def yield_lookups(self, name: TokenRange, address: List[TokenRange])\
 210                           -> Iterator[Tuple[float, int, List[dbf.FieldLookup]]]:
 211         """ Yield all variants how the given name and address should best
 212             be searched for. This takes into account how frequent the terms
 213             are and tries to find a lookup that optimizes index use.
 214         """
 215         penalty = 0.0 # extra penalty
 216         name_partials = {t.token: t for t in self.query.get_partials_list(name)}
 217
 218         addr_partials = [t for r in address for t in self.query.get_partials_list(r)]
 219         addr_tokens = list({t.token for t in addr_partials})
 220
 221         exp_count = min(t.count for t in name_partials.values()) / (2**(len(name_partials) - 1))
 222
 223         if (len(name_partials) > 3 or exp_count < 8000):
 224             yield penalty, exp_count, dbf.lookup_by_names(list(name_partials.keys()), addr_tokens)
 225             return
 226
 227         addr_count = min(t.addr_count for t in addr_partials) if addr_partials else 50000
 228         # Partial term to frequent. Try looking up by rare full names first.
 229         name_fulls = self.query.get_tokens(name, TokenType.WORD)
 230         if name_fulls:
 231             fulls_count = sum(t.count for t in name_fulls)
 232
 233             if fulls_count < 80000 or addr_count < 50000:
 234                 yield penalty,fulls_count / (2**len(addr_tokens)), \
 235                     self.get_full_name_ranking(name_fulls, addr_partials,
 236                                                fulls_count > 30000 / max(1, len(addr_tokens)))
 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         exp_count = exp_count / (2**len(addr_tokens)) if addr_tokens else exp_count
 241         if exp_count < 10000 and addr_count < 20000:
 242             penalty += 0.35 * max(1 if name_fulls else 0.1,
 243                                   5 - len(name_partials) - len(addr_tokens))
 244             yield penalty, exp_count,\
 245                   self.get_name_address_ranking(list(name_partials.keys()), addr_partials)
 246
 247
 248     def get_name_address_ranking(self, name_tokens: List[int],
 249                                  addr_partials: List[Token]) -> List[dbf.FieldLookup]:
 250         """ Create a ranking expression looking up by name and address.
 251         """
 252         lookup = [dbf.FieldLookup('name_vector', name_tokens, lookups.LookupAll)]
 253
 254         addr_restrict_tokens = []
 255         addr_lookup_tokens = []
 256         for t in addr_partials:
 257             if t.addr_count > 20000:
 258                 addr_restrict_tokens.append(t.token)
 259             else:
 260                 addr_lookup_tokens.append(t.token)
 261
 262         if addr_restrict_tokens:
 263             lookup.append(dbf.FieldLookup('nameaddress_vector',
 264                                           addr_restrict_tokens, lookups.Restrict))
 265         if addr_lookup_tokens:
 266             lookup.append(dbf.FieldLookup('nameaddress_vector',
 267                                           addr_lookup_tokens, lookups.LookupAll))
 268
 269         return lookup
 270
 271
 272     def get_full_name_ranking(self, name_fulls: List[Token], addr_partials: List[Token],
 273                               use_lookup: bool) -> List[dbf.FieldLookup]:
 274         """ Create a ranking expression with full name terms and
 275             additional address lookup. When 'use_lookup' is true, then
 276             address lookups will use the index, when the occurrences are not
 277             too many.
 278         """
 279         # At this point drop unindexed partials from the address.
 280         # This might yield wrong results, nothing we can do about that.
 281         if use_lookup:
 282             addr_restrict_tokens = []
 283             addr_lookup_tokens = [t.token for t in addr_partials]
 284         else:
 285             addr_restrict_tokens = [t.token for t in addr_partials]
 286             addr_lookup_tokens = []
 287
 288         return dbf.lookup_by_any_name([t.token for t in name_fulls],
 289                                       addr_restrict_tokens, addr_lookup_tokens)
 290
 291
 292     def get_name_ranking(self, trange: TokenRange,
 293                          db_field: str = 'name_vector') -> dbf.FieldRanking:
 294         """ Create a ranking expression for a name term in the given range.
 295         """
 296         name_fulls = self.query.get_tokens(trange, TokenType.WORD)
 297         ranks = [dbf.RankedTokens(t.penalty, [t.token]) for t in name_fulls]
 298         ranks.sort(key=lambda r: r.penalty)
 299         # Fallback, sum of penalty for partials
 300         name_partials = self.query.get_partials_list(trange)
 301         default = sum(t.penalty for t in name_partials) + 0.2
 302         return dbf.FieldRanking(db_field, default, ranks)
 303
 304
 305     def get_addr_ranking(self, trange: TokenRange) -> dbf.FieldRanking:
 306         """ Create a list of ranking expressions for an address term
 307             for the given ranges.
 308         """
 309         todo: List[Tuple[int, int, dbf.RankedTokens]] = []
 310         heapq.heappush(todo, (0, trange.start, dbf.RankedTokens(0.0, [])))
 311         ranks: List[dbf.RankedTokens] = []
 312
 313         while todo: # pylint: disable=too-many-nested-blocks
 314             neglen, pos, rank = heapq.heappop(todo)
 315             for tlist in self.query.nodes[pos].starting:
 316                 if tlist.ttype in (TokenType.PARTIAL, TokenType.WORD):
 317                     if tlist.end < trange.end:
 318                         chgpenalty = PENALTY_WORDCHANGE[self.query.nodes[tlist.end].btype]
 319                         if tlist.ttype == TokenType.PARTIAL:
 320                             penalty = rank.penalty + chgpenalty \
 321                                       + max(t.penalty for t in tlist.tokens)
 322                             heapq.heappush(todo, (neglen - 1, tlist.end,
 323                                                   dbf.RankedTokens(penalty, rank.tokens)))
 324                         else:
 325                             for t in tlist.tokens:
 326                                 heapq.heappush(todo, (neglen - 1, tlist.end,
 327                                                       rank.with_token(t, chgpenalty)))
 328                     elif tlist.end == trange.end:
 329                         if tlist.ttype == TokenType.PARTIAL:
 330                             ranks.append(dbf.RankedTokens(rank.penalty
 331                                                           + max(t.penalty for t in tlist.tokens),
 332                                                           rank.tokens))
 333                         else:
 334                             ranks.extend(rank.with_token(t, 0.0) for t in tlist.tokens)
 335                         if len(ranks) >= 10:
 336                             # Too many variants, bail out and only add
 337                             # Worst-case Fallback: sum of penalty of partials
 338                             name_partials = self.query.get_partials_list(trange)
 339                             default = sum(t.penalty for t in name_partials) + 0.2
 340                             ranks.append(dbf.RankedTokens(rank.penalty + default, []))
 341                             # Bail out of outer loop
 342                             todo.clear()
 343                             break
 344
 345         ranks.sort(key=lambda r: len(r.tokens))
 346         default = ranks[0].penalty + 0.3
 347         del ranks[0]
 348         ranks.sort(key=lambda r: r.penalty)
 349
 350         return dbf.FieldRanking('nameaddress_vector', default, ranks)
 351
 352
 353     def get_search_data(self, assignment: TokenAssignment) -> Optional[dbf.SearchData]:
 354         """ Collect the tokens for the non-name search fields in the
 355             assignment.
 356         """
 357         sdata = dbf.SearchData()
 358         sdata.penalty = assignment.penalty
 359         if assignment.country:
 360             tokens = self.get_country_tokens(assignment.country)
 361             if not tokens:
 362                 return None
 363             sdata.set_strings('countries', tokens)
 364         elif self.details.countries:
 365             sdata.countries = dbf.WeightedStrings(self.details.countries,
 366                                                   [0.0] * len(self.details.countries))
 367         if assignment.housenumber:
 368             sdata.set_strings('housenumbers',
 369                               self.query.get_tokens(assignment.housenumber,
 370                                                     TokenType.HOUSENUMBER))
 371         if assignment.postcode:
 372             sdata.set_strings('postcodes',
 373                               self.query.get_tokens(assignment.postcode,
 374                                                     TokenType.POSTCODE))
 375         if assignment.qualifier:
 376             tokens = self.get_qualifier_tokens(assignment.qualifier)
 377             if not tokens:
 378                 return None
 379             sdata.set_qualifiers(tokens)
 380         elif self.details.categories:
 381             sdata.qualifiers = dbf.WeightedCategories(self.details.categories,
 382                                                       [0.0] * len(self.details.categories))
 383
 384         if assignment.address:
 385             if not assignment.name and assignment.housenumber:
 386                 # housenumber search: the first item needs to be handled like
 387                 # a name in ranking or penalties are not comparable with
 388                 # normal searches.
 389                 sdata.set_ranking([self.get_name_ranking(assignment.address[0],
 390                                                          db_field='nameaddress_vector')]
 391                                   + [self.get_addr_ranking(r) for r in assignment.address[1:]])
 392             else:
 393                 sdata.set_ranking([self.get_addr_ranking(r) for r in assignment.address])
 394         else:
 395             sdata.rankings = []
 396
 397         return sdata
 398
 399
 400     def get_country_tokens(self, trange: TokenRange) -> List[Token]:
 401         """ Return the list of country tokens for the given range,
 402             optionally filtered by the country list from the details
 403             parameters.
 404         """
 405         tokens = self.query.get_tokens(trange, TokenType.COUNTRY)
 406         if self.details.countries:
 407             tokens = [t for t in tokens if t.lookup_word in self.details.countries]
 408
 409         return tokens
 410
 411
 412     def get_qualifier_tokens(self, trange: TokenRange) -> List[Token]:
 413         """ Return the list of qualifier tokens for the given range,
 414             optionally filtered by the qualifier list from the details
 415             parameters.
 416         """
 417         tokens = self.query.get_tokens(trange, TokenType.QUALIFIER)
 418         if self.details.categories:
 419             tokens = [t for t in tokens if t.get_category() in self.details.categories]
 420
 421         return tokens
 422
 423
 424     def get_near_items(self, assignment: TokenAssignment) -> Optional[dbf.WeightedCategories]:
 425         """ Collect tokens for near items search or use the categories
 426             requested per parameter.
 427             Returns None if no category search is requested.
 428         """
 429         if assignment.near_item:
 430             tokens: Dict[Tuple[str, str], float] = {}
 431             for t in self.query.get_tokens(assignment.near_item, TokenType.NEAR_ITEM):
 432                 cat = t.get_category()
 433                 # The category of a near search will be that of near_item.
 434                 # Thus, if search is restricted to a category parameter,
 435                 # the two sets must intersect.
 436                 if (not self.details.categories or cat in self.details.categories)\
 437                    and t.penalty < tokens.get(cat, 1000.0):
 438                     tokens[cat] = t.penalty
 439             return dbf.WeightedCategories(list(tokens.keys()), list(tokens.values()))
 440
 441         return None
 442
 443
 444 PENALTY_WORDCHANGE = {
 445     BreakType.START: 0.0,
 446     BreakType.END: 0.0,
 447     BreakType.PHRASE: 0.0,
 448     BreakType.WORD: 0.1,
 449     BreakType.PART: 0.2,
 450     BreakType.TOKEN: 0.4
 451 }