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 import nominatim.api.search.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.count for trange in address
 170                        for t in self.query.get_partials_list(trange)}
 171
 172         if expected_count < 8000:
 173             sdata.lookups.append(dbf.FieldLookup('nameaddress_vector',
 174                                                  list(partials), lookups.Restrict))
 175         elif len(partials) != 1 or list(partials.values())[0] < 10000:
 176             sdata.lookups.append(dbf.FieldLookup('nameaddress_vector',
 177                                                  list(partials), lookups.LookupAll))
 178         else:
 179             sdata.lookups.append(
 180                 dbf.FieldLookup('nameaddress_vector',
 181                                 [t.token for t
 182                                  in self.query.get_tokens(address[0], TokenType.WORD)],
 183                                 lookups.LookupAny))
 184
 185         sdata.housenumbers = dbf.WeightedStrings([], [])
 186         yield dbs.PlaceSearch(0.05, sdata, expected_count)
 187
 188
 189     def build_name_search(self, sdata: dbf.SearchData,
 190                           name: TokenRange, address: List[TokenRange],
 191                           is_category: bool) -> Iterator[dbs.AbstractSearch]:
 192         """ Build abstract search queries for simple name or address searches.
 193         """
 194         if is_category or not sdata.housenumbers or self.configured_for_housenumbers:
 195             ranking = self.get_name_ranking(name)
 196             name_penalty = ranking.normalize_penalty()
 197             if ranking.rankings:
 198                 sdata.rankings.append(ranking)
 199             for penalty, count, lookup in self.yield_lookups(name, address):
 200                 sdata.lookups = lookup
 201                 yield dbs.PlaceSearch(penalty + name_penalty, sdata, count)
 202
 203
 204     def yield_lookups(self, name: TokenRange, address: List[TokenRange])\
 205                           -> Iterator[Tuple[float, int, List[dbf.FieldLookup]]]:
 206         """ Yield all variants how the given name and address should best
 207             be searched for. This takes into account how frequent the terms
 208             are and tries to find a lookup that optimizes index use.
 209         """
 210         penalty = 0.0 # extra penalty
 211         name_partials = {t.token: t for t in self.query.get_partials_list(name)}
 212
 213         addr_partials = [t for r in address for t in self.query.get_partials_list(r)]
 214         addr_tokens = list({t.token for t in addr_partials})
 215
 216         partials_indexed = all(t.is_indexed for t in name_partials.values()) \
 217                            and all(t.is_indexed for t in addr_partials)
 218         exp_count = min(t.count for t in name_partials.values()) / (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(list(name_partials.keys()), 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_tokens)),\
 235                   dbf.lookup_by_any_name([t.token for t in name_fulls],
 236                                          addr_tokens, fulls_count > 10000)
 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 all(t.is_indexed for t in name_partials.values()):
 242             lookup = [dbf.FieldLookup('name_vector', list(name_partials.keys()), lookups.LookupAll)]
 243             if addr_tokens:
 244                 lookup.append(dbf.FieldLookup('nameaddress_vector', addr_tokens, lookups.LookupAll))
 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 }