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, Dict
  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         near_items = self.get_near_items(assignment)
  93
  94         if assignment.name is None:
  95             if near_items and not sdata.postcodes:
  96                 sdata.qualifiers = near_items
  97                 near_items = 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(near_items))
 106         else:
 107             builder = self.build_name_search(sdata, assignment.name, assignment.address,
 108                                              bool(near_items))
 109
 110         if near_items:
 111             penalty = min(near_items.penalties)
 112             near_items.penalties = [p - penalty for p in near_items.penalties]
 113             for search in builder:
 114                 yield dbs.NearSearch(penalty + assignment.penalty, near_items, search)
 115         else:
 116             for search in builder:
 117                 search.penalty += assignment.penalty
 118                 yield search
 119
 120
 121     def build_poi_search(self, sdata: dbf.SearchData) -> Iterator[dbs.AbstractSearch]:
 122         """ Build abstract search query for a simple category search.
 123             This kind of search requires an additional geographic constraint.
 124         """
 125         if not sdata.housenumbers \
 126            and ((self.details.viewbox and self.details.bounded_viewbox) or self.details.near):
 127             yield dbs.PoiSearch(sdata)
 128
 129
 130     def build_special_search(self, sdata: dbf.SearchData,
 131                              address: List[TokenRange],
 132                              is_category: bool) -> Iterator[dbs.AbstractSearch]:
 133         """ Build abstract search queries for searches that do not involve
 134             a named place.
 135         """
 136         if sdata.qualifiers:
 137             # No special searches over qualifiers supported.
 138             return
 139
 140         if sdata.countries and not address and not sdata.postcodes \
 141            and self.configured_for_country:
 142             yield dbs.CountrySearch(sdata)
 143
 144         if sdata.postcodes and (is_category or self.configured_for_postcode):
 145             penalty = 0.0 if sdata.countries else 0.1
 146             if address:
 147                 sdata.lookups = [dbf.FieldLookup('nameaddress_vector',
 148                                                  [t.token for r in address
 149                                                   for t in self.query.get_partials_list(r)],
 150                                                  'restrict')]
 151                 penalty += 0.2
 152             yield dbs.PostcodeSearch(penalty, sdata)
 153
 154
 155     def build_housenumber_search(self, sdata: dbf.SearchData, hnrs: List[Token],
 156                                  address: List[TokenRange]) -> Iterator[dbs.AbstractSearch]:
 157         """ Build a simple address search for special entries where the
 158             housenumber is the main name token.
 159         """
 160         sdata.lookups = [dbf.FieldLookup('name_vector', [t.token for t in hnrs], 'lookup_any')]
 161
 162         partials = [t for trange in address
 163                        for t in self.query.get_partials_list(trange)]
 164
 165         if len(partials) != 1 or partials[0].count < 10000:
 166             sdata.lookups.append(dbf.FieldLookup('nameaddress_vector',
 167                                                  [t.token for t in partials], 'lookup_all'))
 168         else:
 169             sdata.lookups.append(
 170                 dbf.FieldLookup('nameaddress_vector',
 171                                 [t.token for t
 172                                  in self.query.get_tokens(address[0], TokenType.WORD)],
 173                                 'lookup_any'))
 174
 175         sdata.housenumbers = dbf.WeightedStrings([], [])
 176         yield dbs.PlaceSearch(0.05, sdata, sum(t.count for t in hnrs))
 177
 178
 179     def build_name_search(self, sdata: dbf.SearchData,
 180                           name: TokenRange, address: List[TokenRange],
 181                           is_category: bool) -> Iterator[dbs.AbstractSearch]:
 182         """ Build abstract search queries for simple name or address searches.
 183         """
 184         if is_category or not sdata.housenumbers or self.configured_for_housenumbers:
 185             ranking = self.get_name_ranking(name)
 186             name_penalty = ranking.normalize_penalty()
 187             if ranking.rankings:
 188                 sdata.rankings.append(ranking)
 189             for penalty, count, lookup in self.yield_lookups(name, address):
 190                 sdata.lookups = lookup
 191                 yield dbs.PlaceSearch(penalty + name_penalty, sdata, count)
 192
 193
 194     def yield_lookups(self, name: TokenRange, address: List[TokenRange])\
 195                           -> Iterator[Tuple[float, int, List[dbf.FieldLookup]]]:
 196         """ Yield all variants how the given name and address should best
 197             be searched for. This takes into account how frequent the terms
 198             are and tries to find a lookup that optimizes index use.
 199         """
 200         penalty = 0.0 # extra penalty
 201         name_partials = self.query.get_partials_list(name)
 202         name_tokens = [t.token for t in name_partials]
 203
 204         addr_partials = [t for r in address for t in self.query.get_partials_list(r)]
 205         addr_tokens = [t.token for t in addr_partials]
 206
 207         partials_indexed = all(t.is_indexed for t in name_partials) \
 208                            and all(t.is_indexed for t in addr_partials)
 209         exp_count = min(t.count for t in name_partials) / (2**(len(name_partials) - 1))
 210
 211         if (len(name_partials) > 3 or exp_count < 8000) and partials_indexed:
 212             yield penalty, exp_count, dbf.lookup_by_names(name_tokens, addr_tokens)
 213             return
 214
 215         # Partial term to frequent. Try looking up by rare full names first.
 216         name_fulls = self.query.get_tokens(name, TokenType.WORD)
 217         fulls_count = sum(t.count for t in name_fulls)
 218         # At this point drop unindexed partials from the address.
 219         # This might yield wrong results, nothing we can do about that.
 220         if not partials_indexed:
 221             addr_tokens = [t.token for t in addr_partials if t.is_indexed]
 222             penalty += 1.2 * sum(t.penalty for t in addr_partials if not t.is_indexed)
 223         # Any of the full names applies with all of the partials from the address
 224         yield penalty, fulls_count / (2**len(addr_partials)),\
 225               dbf.lookup_by_any_name([t.token for t in name_fulls], addr_tokens,
 226                                      'restrict' if fulls_count < 10000 else 'lookup_all')
 227
 228         # To catch remaining results, lookup by name and address
 229         # We only do this if there is a reasonable number of results expected.
 230         exp_count = exp_count / (2**len(addr_partials)) if addr_partials else exp_count
 231         if exp_count < 10000 and all(t.is_indexed for t in name_partials):
 232             lookup = [dbf.FieldLookup('name_vector', name_tokens, 'lookup_all')]
 233             if addr_tokens:
 234                 lookup.append(dbf.FieldLookup('nameaddress_vector', addr_tokens, 'lookup_all'))
 235             penalty += 0.35 * max(0, 5 - len(name_partials) - len(addr_tokens))
 236             yield penalty, exp_count, lookup
 237
 238
 239     def get_name_ranking(self, trange: TokenRange) -> dbf.FieldRanking:
 240         """ Create a ranking expression for a name term in the given range.
 241         """
 242         name_fulls = self.query.get_tokens(trange, TokenType.WORD)
 243         ranks = [dbf.RankedTokens(t.penalty, [t.token]) for t in name_fulls]
 244         ranks.sort(key=lambda r: r.penalty)
 245         # Fallback, sum of penalty for partials
 246         name_partials = self.query.get_partials_list(trange)
 247         default = sum(t.penalty for t in name_partials) + 0.2
 248         return dbf.FieldRanking('name_vector', default, ranks)
 249
 250
 251     def get_addr_ranking(self, trange: TokenRange) -> dbf.FieldRanking:
 252         """ Create a list of ranking expressions for an address term
 253             for the given ranges.
 254         """
 255         todo: List[Tuple[int, int, dbf.RankedTokens]] = []
 256         heapq.heappush(todo, (0, trange.start, dbf.RankedTokens(0.0, [])))
 257         ranks: List[dbf.RankedTokens] = []
 258
 259         while todo: # pylint: disable=too-many-nested-blocks
 260             neglen, pos, rank = heapq.heappop(todo)
 261             for tlist in self.query.nodes[pos].starting:
 262                 if tlist.ttype in (TokenType.PARTIAL, TokenType.WORD):
 263                     if tlist.end < trange.end:
 264                         chgpenalty = PENALTY_WORDCHANGE[self.query.nodes[tlist.end].btype]
 265                         if tlist.ttype == TokenType.PARTIAL:
 266                             penalty = rank.penalty + chgpenalty \
 267                                       + max(t.penalty for t in tlist.tokens)
 268                             heapq.heappush(todo, (neglen - 1, tlist.end,
 269                                                   dbf.RankedTokens(penalty, rank.tokens)))
 270                         else:
 271                             for t in tlist.tokens:
 272                                 heapq.heappush(todo, (neglen - 1, tlist.end,
 273                                                       rank.with_token(t, chgpenalty)))
 274                     elif tlist.end == trange.end:
 275                         if tlist.ttype == TokenType.PARTIAL:
 276                             ranks.append(dbf.RankedTokens(rank.penalty
 277                                                           + max(t.penalty for t in tlist.tokens),
 278                                                           rank.tokens))
 279                         else:
 280                             ranks.extend(rank.with_token(t, 0.0) for t in tlist.tokens)
 281                         if len(ranks) >= 10:
 282                             # Too many variants, bail out and only add
 283                             # Worst-case Fallback: sum of penalty of partials
 284                             name_partials = self.query.get_partials_list(trange)
 285                             default = sum(t.penalty for t in name_partials) + 0.2
 286                             ranks.append(dbf.RankedTokens(rank.penalty + default, []))
 287                             # Bail out of outer loop
 288                             todo.clear()
 289                             break
 290
 291         ranks.sort(key=lambda r: len(r.tokens))
 292         default = ranks[0].penalty + 0.3
 293         del ranks[0]
 294         ranks.sort(key=lambda r: r.penalty)
 295
 296         return dbf.FieldRanking('nameaddress_vector', default, ranks)
 297
 298
 299     def get_search_data(self, assignment: TokenAssignment) -> Optional[dbf.SearchData]:
 300         """ Collect the tokens for the non-name search fields in the
 301             assignment.
 302         """
 303         sdata = dbf.SearchData()
 304         sdata.penalty = assignment.penalty
 305         if assignment.country:
 306             tokens = self.query.get_tokens(assignment.country, TokenType.COUNTRY)
 307             if self.details.countries:
 308                 tokens = [t for t in tokens if t.lookup_word in self.details.countries]
 309                 if not tokens:
 310                     return None
 311             sdata.set_strings('countries', tokens)
 312         elif self.details.countries:
 313             sdata.countries = dbf.WeightedStrings(self.details.countries,
 314                                                   [0.0] * len(self.details.countries))
 315         if assignment.housenumber:
 316             sdata.set_strings('housenumbers',
 317                               self.query.get_tokens(assignment.housenumber,
 318                                                     TokenType.HOUSENUMBER))
 319         if assignment.postcode:
 320             sdata.set_strings('postcodes',
 321                               self.query.get_tokens(assignment.postcode,
 322                                                     TokenType.POSTCODE))
 323         if assignment.qualifier:
 324             tokens = self.query.get_tokens(assignment.qualifier, TokenType.QUALIFIER)
 325             if self.details.categories:
 326                 tokens = [t for t in tokens if t.get_category() in self.details.categories]
 327                 if not tokens:
 328                     return None
 329             sdata.set_qualifiers(tokens)
 330         elif self.details.categories:
 331             sdata.qualifiers = dbf.WeightedCategories(self.details.categories,
 332                                                       [0.0] * len(self.details.categories))
 333
 334         if assignment.address:
 335             sdata.set_ranking([self.get_addr_ranking(r) for r in assignment.address])
 336         else:
 337             sdata.rankings = []
 338
 339         return sdata
 340
 341
 342     def get_near_items(self, assignment: TokenAssignment) -> Optional[dbf.WeightedCategories]:
 343         """ Collect tokens for near items search or use the categories
 344             requested per parameter.
 345             Returns None if no category search is requested.
 346         """
 347         if assignment.near_item:
 348             tokens: Dict[Tuple[str, str], float] = {}
 349             for t in self.query.get_tokens(assignment.near_item, TokenType.NEAR_ITEM):
 350                 cat = t.get_category()
 351                 if (not self.details.categories or cat in self.details.categories)\
 352                    and t.penalty < tokens.get(cat, 1000.0):
 353                     tokens[cat] = t.penalty
 354             return dbf.WeightedCategories(list(tokens.keys()), list(tokens.values()))
 355
 356         return None
 357
 358
 359 PENALTY_WORDCHANGE = {
 360     BreakType.START: 0.0,
 361     BreakType.END: 0.0,
 362     BreakType.PHRASE: 0.0,
 363     BreakType.WORD: 0.1,
 364     BreakType.PART: 0.2,
 365     BreakType.TOKEN: 0.4
 366 }