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