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,
 237                                          fulls_count > 30000 / max(1, len(addr_tokens)))
 238
 239         # To catch remaining results, lookup by name and address
 240         # We only do this if there is a reasonable number of results expected.
 241         exp_count = exp_count / (2**len(addr_tokens)) if addr_tokens else exp_count
 242         if exp_count < 10000 and all(t.is_indexed for t in name_partials.values()):
 243             lookup = [dbf.FieldLookup('name_vector', list(name_partials.keys()), lookups.LookupAll)]
 244             if addr_tokens:
 245                 lookup.append(dbf.FieldLookup('nameaddress_vector', addr_tokens, lookups.LookupAll))
 246             penalty += 0.35 * max(1 if name_fulls else 0.1,
 247                                   5 - len(name_partials) - len(addr_tokens))
 248             yield penalty, exp_count, lookup
 249
 250
 251     def get_name_ranking(self, trange: TokenRange) -> dbf.FieldRanking:
 252         """ Create a ranking expression for a name term in the given range.
 253         """
 254         name_fulls = self.query.get_tokens(trange, TokenType.WORD)
 255         ranks = [dbf.RankedTokens(t.penalty, [t.token]) for t in name_fulls]
 256         ranks.sort(key=lambda r: r.penalty)
 257         # Fallback, sum of penalty for partials
 258         name_partials = self.query.get_partials_list(trange)
 259         default = sum(t.penalty for t in name_partials) + 0.2
 260         return dbf.FieldRanking('name_vector', default, ranks)
 261
 262
 263     def get_addr_ranking(self, trange: TokenRange) -> dbf.FieldRanking:
 264         """ Create a list of ranking expressions for an address term
 265             for the given ranges.
 266         """
 267         todo: List[Tuple[int, int, dbf.RankedTokens]] = []
 268         heapq.heappush(todo, (0, trange.start, dbf.RankedTokens(0.0, [])))
 269         ranks: List[dbf.RankedTokens] = []
 270
 271         while todo: # pylint: disable=too-many-nested-blocks
 272             neglen, pos, rank = heapq.heappop(todo)
 273             for tlist in self.query.nodes[pos].starting:
 274                 if tlist.ttype in (TokenType.PARTIAL, TokenType.WORD):
 275                     if tlist.end < trange.end:
 276                         chgpenalty = PENALTY_WORDCHANGE[self.query.nodes[tlist.end].btype]
 277                         if tlist.ttype == TokenType.PARTIAL:
 278                             penalty = rank.penalty + chgpenalty \
 279                                       + max(t.penalty for t in tlist.tokens)
 280                             heapq.heappush(todo, (neglen - 1, tlist.end,
 281                                                   dbf.RankedTokens(penalty, rank.tokens)))
 282                         else:
 283                             for t in tlist.tokens:
 284                                 heapq.heappush(todo, (neglen - 1, tlist.end,
 285                                                       rank.with_token(t, chgpenalty)))
 286                     elif tlist.end == trange.end:
 287                         if tlist.ttype == TokenType.PARTIAL:
 288                             ranks.append(dbf.RankedTokens(rank.penalty
 289                                                           + max(t.penalty for t in tlist.tokens),
 290                                                           rank.tokens))
 291                         else:
 292                             ranks.extend(rank.with_token(t, 0.0) for t in tlist.tokens)
 293                         if len(ranks) >= 10:
 294                             # Too many variants, bail out and only add
 295                             # Worst-case Fallback: sum of penalty of partials
 296                             name_partials = self.query.get_partials_list(trange)
 297                             default = sum(t.penalty for t in name_partials) + 0.2
 298                             ranks.append(dbf.RankedTokens(rank.penalty + default, []))
 299                             # Bail out of outer loop
 300                             todo.clear()
 301                             break
 302
 303         ranks.sort(key=lambda r: len(r.tokens))
 304         default = ranks[0].penalty + 0.3
 305         del ranks[0]
 306         ranks.sort(key=lambda r: r.penalty)
 307
 308         return dbf.FieldRanking('nameaddress_vector', default, ranks)
 309
 310
 311     def get_search_data(self, assignment: TokenAssignment) -> Optional[dbf.SearchData]:
 312         """ Collect the tokens for the non-name search fields in the
 313             assignment.
 314         """
 315         sdata = dbf.SearchData()
 316         sdata.penalty = assignment.penalty
 317         if assignment.country:
 318             tokens = self.query.get_tokens(assignment.country, TokenType.COUNTRY)
 319             if self.details.countries:
 320                 tokens = [t for t in tokens if t.lookup_word in self.details.countries]
 321                 if not tokens:
 322                     return None
 323             sdata.set_strings('countries', tokens)
 324         elif self.details.countries:
 325             sdata.countries = dbf.WeightedStrings(self.details.countries,
 326                                                   [0.0] * len(self.details.countries))
 327         if assignment.housenumber:
 328             sdata.set_strings('housenumbers',
 329                               self.query.get_tokens(assignment.housenumber,
 330                                                     TokenType.HOUSENUMBER))
 331         if assignment.postcode:
 332             sdata.set_strings('postcodes',
 333                               self.query.get_tokens(assignment.postcode,
 334                                                     TokenType.POSTCODE))
 335         if assignment.qualifier:
 336             tokens = self.query.get_tokens(assignment.qualifier, TokenType.QUALIFIER)
 337             if self.details.categories:
 338                 tokens = [t for t in tokens if t.get_category() in self.details.categories]
 339                 if not tokens:
 340                     return None
 341             sdata.set_qualifiers(tokens)
 342         elif self.details.categories:
 343             sdata.qualifiers = dbf.WeightedCategories(self.details.categories,
 344                                                       [0.0] * len(self.details.categories))
 345
 346         if assignment.address:
 347             sdata.set_ranking([self.get_addr_ranking(r) for r in assignment.address])
 348         else:
 349             sdata.rankings = []
 350
 351         return sdata
 352
 353
 354     def get_near_items(self, assignment: TokenAssignment) -> Optional[dbf.WeightedCategories]:
 355         """ Collect tokens for near items search or use the categories
 356             requested per parameter.
 357             Returns None if no category search is requested.
 358         """
 359         if assignment.near_item:
 360             tokens: Dict[Tuple[str, str], float] = {}
 361             for t in self.query.get_tokens(assignment.near_item, TokenType.NEAR_ITEM):
 362                 cat = t.get_category()
 363                 # The category of a near search will be that of near_item.
 364                 # Thus, if search is restricted to a category parameter,
 365                 # the two sets must intersect.
 366                 if (not self.details.categories or cat in self.details.categories)\
 367                    and t.penalty < tokens.get(cat, 1000.0):
 368                     tokens[cat] = t.penalty
 369             return dbf.WeightedCategories(list(tokens.keys()), list(tokens.values()))
 370
 371         return None
 372
 373
 374 PENALTY_WORDCHANGE = {
 375     BreakType.START: 0.0,
 376     BreakType.END: 0.0,
 377     BreakType.PHRASE: 0.0,
 378     BreakType.WORD: 0.1,
 379     BreakType.PART: 0.2,
 380     BreakType.TOKEN: 0.4
 381 }