"""
Convertion from token assignment to an abstract DB search.
"""
-from typing import Optional, List, Tuple, Iterator
+from typing import Optional, List, Tuple, Iterator, Dict
import heapq
from nominatim.api.types import SearchDetails, DataLayer
from nominatim.api.search.token_assignment import TokenAssignment
import nominatim.api.search.db_search_fields as dbf
import nominatim.api.search.db_searches as dbs
-from nominatim.api.logging import log
def wrap_near_search(categories: List[Tuple[str, str]],
if sdata is None:
return
- categories = self.get_search_categories(assignment)
+ near_items = self.get_near_items(assignment)
+ if near_items is not None and not near_items:
+ return # impossible compbination of near items and category parameter
if assignment.name is None:
- if categories and not sdata.postcodes:
- sdata.qualifiers = categories
- categories = None
+ if near_items and not sdata.postcodes:
+ sdata.qualifiers = near_items
+ near_items = None
builder = self.build_poi_search(sdata)
elif assignment.housenumber:
hnr_tokens = self.query.get_tokens(assignment.housenumber,
builder = self.build_housenumber_search(sdata, hnr_tokens, assignment.address)
else:
builder = self.build_special_search(sdata, assignment.address,
- bool(categories))
+ bool(near_items))
else:
builder = self.build_name_search(sdata, assignment.name, assignment.address,
- bool(categories))
+ bool(near_items))
- if categories:
- penalty = min(categories.penalties)
- categories.penalties = [p - penalty for p in categories.penalties]
+ if near_items:
+ penalty = min(near_items.penalties)
+ near_items.penalties = [p - penalty for p in near_items.penalties]
for search in builder:
- yield dbs.NearSearch(penalty, categories, search)
+ yield dbs.NearSearch(penalty + assignment.penalty, near_items, search)
else:
- yield from builder
+ for search in builder:
+ search.penalty += assignment.penalty
+ yield search
def build_poi_search(self, sdata: dbf.SearchData) -> Iterator[dbs.AbstractSearch]:
""" Build a simple address search for special entries where the
housenumber is the main name token.
"""
- partial_tokens: List[int] = []
- for trange in address:
- partial_tokens.extend(t.token for t in self.query.get_partials_list(trange))
+ sdata.lookups = [dbf.FieldLookup('name_vector', [t.token for t in hnrs], 'lookup_any')]
+
+ partials = [t for trange in address
+ for t in self.query.get_partials_list(trange)]
+
+ if len(partials) != 1 or partials[0].count < 10000:
+ sdata.lookups.append(dbf.FieldLookup('nameaddress_vector',
+ [t.token for t in partials], 'lookup_all'))
+ else:
+ sdata.lookups.append(
+ dbf.FieldLookup('nameaddress_vector',
+ [t.token for t
+ in self.query.get_tokens(address[0], TokenType.WORD)],
+ 'lookup_any'))
- sdata.lookups = [dbf.FieldLookup('name_vector', [t.token for t in hnrs], 'lookup_any'),
- dbf.FieldLookup('nameaddress_vector', partial_tokens, 'lookup_all')
- ]
+ sdata.housenumbers = dbf.WeightedStrings([], [])
yield dbs.PlaceSearch(0.05, sdata, sum(t.count for t in hnrs))
be searched for. This takes into account how frequent the terms
are and tries to find a lookup that optimizes index use.
"""
- penalty = 0.0 # extra penalty currently unused
-
+ penalty = 0.0 # extra penalty
name_partials = self.query.get_partials_list(name)
- exp_name_count = min(t.count for t in name_partials)
- addr_partials = []
- for trange in address:
- addr_partials.extend(self.query.get_partials_list(trange))
+ name_tokens = [t.token for t in name_partials]
+
+ addr_partials = [t for r in address for t in self.query.get_partials_list(r)]
addr_tokens = [t.token for t in addr_partials]
+
partials_indexed = all(t.is_indexed for t in name_partials) \
and all(t.is_indexed for t in addr_partials)
+ exp_count = min(t.count for t in name_partials) / (2**(len(name_partials) - 1))
- if (len(name_partials) > 3 or exp_name_count < 1000) and partials_indexed:
- # Lookup by name partials, use address partials to restrict results.
- lookup = [dbf.FieldLookup('name_vector',
- [t.token for t in name_partials], 'lookup_all')]
- if addr_tokens:
- lookup.append(dbf.FieldLookup('nameaddress_vector', addr_tokens, 'restrict'))
- yield penalty, exp_name_count, lookup
- return
-
- exp_addr_count = min(t.count for t in addr_partials) if addr_partials else exp_name_count
- if exp_addr_count < 1000 and partials_indexed:
- # Lookup by address partials and restrict results through name terms.
- # Give this a small penalty because lookups in the address index are
- # more expensive
- yield penalty + exp_addr_count/5000, exp_addr_count,\
- [dbf.FieldLookup('name_vector', [t.token for t in name_partials], 'restrict'),
- dbf.FieldLookup('nameaddress_vector', addr_tokens, 'lookup_all')]
+ if (len(name_partials) > 3 or exp_count < 8000) and partials_indexed:
+ yield penalty, exp_count, dbf.lookup_by_names(name_tokens, addr_tokens)
return
# Partial term to frequent. Try looking up by rare full names first.
name_fulls = self.query.get_tokens(name, TokenType.WORD)
- rare_names = list(filter(lambda t: t.count < 1000, name_fulls))
+ fulls_count = sum(t.count for t in name_fulls)
# At this point drop unindexed partials from the address.
# This might yield wrong results, nothing we can do about that.
if not partials_indexed:
addr_tokens = [t.token for t in addr_partials if t.is_indexed]
- log().var_dump('before', penalty)
penalty += 1.2 * sum(t.penalty for t in addr_partials if not t.is_indexed)
- log().var_dump('after', penalty)
- if rare_names:
- # Any of the full names applies with all of the partials from the address
- lookup = [dbf.FieldLookup('name_vector', [t.token for t in rare_names], 'lookup_any')]
- if addr_tokens:
- lookup.append(dbf.FieldLookup('nameaddress_vector', addr_tokens, 'restrict'))
- yield penalty, sum(t.count for t in rare_names), lookup
+ # Any of the full names applies with all of the partials from the address
+ yield penalty, fulls_count / (2**len(addr_partials)),\
+ dbf.lookup_by_any_name([t.token for t in name_fulls], addr_tokens,
+ 'restrict' if fulls_count < 10000 else 'lookup_all')
# To catch remaining results, lookup by name and address
# We only do this if there is a reasonable number of results expected.
- if min(exp_name_count, exp_addr_count) < 10000:
- if all(t.is_indexed for t in name_partials):
- lookup = [dbf.FieldLookup('name_vector',
- [t.token for t in name_partials], 'lookup_all')]
- else:
- # we don't have the partials, try with the non-rare names
- non_rare_names = [t.token for t in name_fulls if t.count >= 1000]
- if not non_rare_names:
- return
- lookup = [dbf.FieldLookup('name_vector', non_rare_names, 'lookup_any')]
+ exp_count = exp_count / (2**len(addr_partials)) if addr_partials else exp_count
+ if exp_count < 10000 and all(t.is_indexed for t in name_partials):
+ lookup = [dbf.FieldLookup('name_vector', name_tokens, 'lookup_all')]
if addr_tokens:
lookup.append(dbf.FieldLookup('nameaddress_vector', addr_tokens, 'lookup_all'))
- yield penalty + 0.1 * max(0, 5 - len(name_partials) - len(addr_tokens)),\
- min(exp_name_count, exp_addr_count), lookup
+ penalty += 0.35 * max(0, 5 - len(name_partials) - len(addr_tokens))
+ yield penalty, exp_count, lookup
def get_name_ranking(self, trange: TokenRange) -> dbf.FieldRanking:
self.query.get_tokens(assignment.postcode,
TokenType.POSTCODE))
if assignment.qualifier:
- sdata.set_qualifiers(self.query.get_tokens(assignment.qualifier,
- TokenType.QUALIFIER))
+ tokens = self.query.get_tokens(assignment.qualifier, TokenType.QUALIFIER)
+ if self.details.categories:
+ tokens = [t for t in tokens if t.get_category() in self.details.categories]
+ if not tokens:
+ return None
+ sdata.set_qualifiers(tokens)
+ elif self.details.categories:
+ sdata.qualifiers = dbf.WeightedCategories(self.details.categories,
+ [0.0] * len(self.details.categories))
if assignment.address:
sdata.set_ranking([self.get_addr_ranking(r) for r in assignment.address])
return sdata
- def get_search_categories(self,
- assignment: TokenAssignment) -> Optional[dbf.WeightedCategories]:
- """ Collect tokens for category search or use the categories
+ def get_near_items(self, assignment: TokenAssignment) -> Optional[dbf.WeightedCategories]:
+ """ Collect tokens for near items search or use the categories
requested per parameter.
Returns None if no category search is requested.
"""
- if assignment.category:
- tokens = [t for t in self.query.get_tokens(assignment.category,
- TokenType.CATEGORY)
- if not self.details.categories
- or t.get_category() in self.details.categories]
- return dbf.WeightedCategories([t.get_category() for t in tokens],
- [t.penalty for t in tokens])
-
- if self.details.categories:
- return dbf.WeightedCategories(self.details.categories,
- [0.0] * len(self.details.categories))
+ if assignment.near_item:
+ tokens: Dict[Tuple[str, str], float] = {}
+ for t in self.query.get_tokens(assignment.near_item, TokenType.NEAR_ITEM):
+ cat = t.get_category()
+ # The category of a near search will be that of near_item.
+ # Thus, if search is restricted to a category parameter,
+ # the two sets must intersect.
+ if (not self.details.categories or cat in self.details.categories)\
+ and t.penalty < tokens.get(cat, 1000.0):
+ tokens[cat] = t.penalty
+ return dbf.WeightedCategories(list(tokens.keys()), list(tokens.values()))
return None
projects / nominatim.git / blobdiff