postcodes: exclude seen places later

[nominatim.git] / nominatim / api / search / db_search_builder.py

diff --git a/nominatim/api/search/db_search_builder.py b/nominatim/api/search/db_search_builder.py
index 2a3153be334d17ed3262853cd156172febb0f8b3..ef7a66b8507387630c6d0aacc5bfb2b67a08b566 100644 (file)
--- a/nominatim/api/search/db_search_builder.py
+++ b/nominatim/api/search/db_search_builder.py
@@ -5,9 +5,9 @@
 # Copyright (C) 2023 by the Nominatim developer community.
 # For a full list of authors see the git log.
 """
 # Copyright (C) 2023 by the Nominatim developer community.
 # For a full list of authors see the git log.
 """

-Convertion from token assignment to an abstract DB search.
+Conversion 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
 import heapq
 
 from nominatim.api.types import SearchDetails, DataLayer


@@ -15,7 +15,7 @@ from nominatim.api.search.query import QueryStruct, Token, TokenType, TokenRange
 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.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
+import nominatim.api.search.db_search_lookups as lookups

 
 
 def wrap_near_search(categories: List[Tuple[str, str]],
 
 
 def wrap_near_search(categories: List[Tuple[str, str]],


@@ -90,12 +90,14 @@ class SearchBuilder:
         if sdata is None:
             return
 
         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 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_poi_search(sdata)
             elif assignment.housenumber:
                 hnr_tokens = self.query.get_tokens(assignment.housenumber,


@@ -103,18 +105,23 @@ class SearchBuilder:
                 builder = self.build_housenumber_search(sdata, hnr_tokens, assignment.address)
             else:
                 builder = self.build_special_search(sdata, assignment.address,
                 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,
         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:
             for search in builder:

-                yield dbs.NearSearch(penalty, categories, search)
+                search_penalty = search.penalty
+                search.penalty = 0.0
+                yield dbs.NearSearch(penalty + assignment.penalty + search_penalty,
+                                     near_items, search)

         else:
         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]:
 
 
     def build_poi_search(self, sdata: dbf.SearchData) -> Iterator[dbs.AbstractSearch]:


@@ -146,7 +153,7 @@ class SearchBuilder:
                 sdata.lookups = [dbf.FieldLookup('nameaddress_vector',
                                                  [t.token for r in address
                                                   for t in self.query.get_partials_list(r)],
                 sdata.lookups = [dbf.FieldLookup('nameaddress_vector',
                                                  [t.token for r in address
                                                   for t in self.query.get_partials_list(r)],

-                                                 'restrict')]
+                                                 lookups.Restrict)]

                 penalty += 0.2
             yield dbs.PostcodeSearch(penalty, sdata)
 
                 penalty += 0.2
             yield dbs.PostcodeSearch(penalty, sdata)
 


@@ -156,14 +163,28 @@ class SearchBuilder:
         """ Build a simple address search for special entries where the
             housenumber is the main name token.
         """
         """ 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], lookups.LookupAny)]
+        expected_count = sum(t.count for t in hnrs)
+
+        partials = {t.token: t.count for trange in address
+                       for t in self.query.get_partials_list(trange)}
+
+        if expected_count < 8000:
+            sdata.lookups.append(dbf.FieldLookup('nameaddress_vector',
+                                                 list(partials), lookups.Restrict))
+        elif len(partials) != 1 or list(partials.values())[0] < 10000:
+            sdata.lookups.append(dbf.FieldLookup('nameaddress_vector',
+                                                 list(partials), lookups.LookupAll))
+        else:
+            addr_fulls = [t.token for t
+                          in self.query.get_tokens(address[0], TokenType.WORD)]
+            if len(addr_fulls) > 5:
+                return
+            sdata.lookups.append(
+                dbf.FieldLookup('nameaddress_vector', addr_fulls, lookups.LookupAny))

 
 

-        sdata.lookups = [dbf.FieldLookup('name_vector', [t.token for t in hnrs], 'lookup_any'),
-                         dbf.FieldLookup('nameaddress_vector', partial_tokens, 'lookup_all')
-                        ]
-        yield dbs.PlaceSearch(0.05, sdata, sum(t.count for t in hnrs))
+        sdata.housenumbers = dbf.WeightedStrings([], [])
+        yield dbs.PlaceSearch(0.05, sdata, expected_count)

 
 
     def build_name_search(self, sdata: dbf.SearchData,
 
 
     def build_name_search(self, sdata: dbf.SearchData,


@@ -187,72 +208,49 @@ class SearchBuilder:
             be searched for. This takes into account how frequent the terms
             are and tries to find a lookup that optimizes index use.
         """
             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
-
-        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))
-        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)
+        penalty = 0.0 # extra penalty
+        name_partials = {t.token: t for t in self.query.get_partials_list(name)}

 
 

-        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
+        addr_partials = [t for r in address for t in self.query.get_partials_list(r)]
+        addr_tokens = list({t.token for t in addr_partials})

 
 

-        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')]
+        partials_indexed = all(t.is_indexed for t in name_partials.values()) \
+                           and all(t.is_indexed for t in addr_partials)
+        exp_count = min(t.count for t in name_partials.values()) / (2**(len(name_partials) - 1))
+
+        if (len(name_partials) > 3 or exp_count < 8000) and partials_indexed:
+            yield penalty, exp_count, dbf.lookup_by_names(list(name_partials.keys()), addr_tokens)

             return
 
         # Partial term to frequent. Try looking up by rare full names first.
         name_fulls = self.query.get_tokens(name, TokenType.WORD)
             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))
-        # 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:
+        if 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]
+                penalty += 1.2 * sum(t.penalty for t in addr_partials if not t.is_indexed)

             # Any of the full names applies with all of the partials from the address
             # 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
+            yield penalty, fulls_count / (2**len(addr_tokens)),\
+                  dbf.lookup_by_any_name([t.token for t in name_fulls],
+                                         addr_tokens,
+                                         fulls_count > 30000 / max(1, len(addr_tokens)))

 
         # To catch remaining results, lookup by name and address
         # We only do this if there is a reasonable number of results expected.
 
         # 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_tokens)) if addr_tokens else exp_count
+        if exp_count < 10000 and all(t.is_indexed for t in name_partials.values()):
+            lookup = [dbf.FieldLookup('name_vector', list(name_partials.keys()), lookups.LookupAll)]

             if addr_tokens:
             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
+                lookup.append(dbf.FieldLookup('nameaddress_vector', addr_tokens, lookups.LookupAll))
+            penalty += 0.35 * max(1 if name_fulls else 0.1,
+                                  5 - len(name_partials) - len(addr_tokens))
+            yield penalty, exp_count, lookup

 
 
 
 

-    def get_name_ranking(self, trange: TokenRange) -> dbf.FieldRanking:
+    def get_name_ranking(self, trange: TokenRange,
+                         db_field: str = 'name_vector') -> dbf.FieldRanking:

         """ Create a ranking expression for a name term in the given range.
         """
         name_fulls = self.query.get_tokens(trange, TokenType.WORD)
         """ Create a ranking expression for a name term in the given range.
         """
         name_fulls = self.query.get_tokens(trange, TokenType.WORD)


@@ -261,7 +259,7 @@ class SearchBuilder:
         # Fallback, sum of penalty for partials
         name_partials = self.query.get_partials_list(trange)
         default = sum(t.penalty for t in name_partials) + 0.2
         # Fallback, sum of penalty for partials
         name_partials = self.query.get_partials_list(trange)
         default = sum(t.penalty for t in name_partials) + 0.2

-        return dbf.FieldRanking('name_vector', default, ranks)
+        return dbf.FieldRanking(db_field, default, ranks)

 
 
     def get_addr_ranking(self, trange: TokenRange) -> dbf.FieldRanking:
 
 
     def get_addr_ranking(self, trange: TokenRange) -> dbf.FieldRanking:


@@ -319,11 +317,9 @@ class SearchBuilder:
         sdata = dbf.SearchData()
         sdata.penalty = assignment.penalty
         if assignment.country:
         sdata = dbf.SearchData()
         sdata.penalty = assignment.penalty
         if assignment.country:

-            tokens = self.query.get_tokens(assignment.country, TokenType.COUNTRY)
-            if self.details.countries:
-                tokens = [t for t in tokens if t.lookup_word in self.details.countries]
-                if not tokens:
-                    return None
+            tokens = self.get_country_tokens(assignment.country)
+            if not tokens:
+                return None

             sdata.set_strings('countries', tokens)
         elif self.details.countries:
             sdata.countries = dbf.WeightedStrings(self.details.countries,
             sdata.set_strings('countries', tokens)
         elif self.details.countries:
             sdata.countries = dbf.WeightedStrings(self.details.countries,


@@ -337,34 +333,70 @@ class SearchBuilder:
                               self.query.get_tokens(assignment.postcode,
                                                     TokenType.POSTCODE))
         if assignment.qualifier:
                               self.query.get_tokens(assignment.postcode,
                                                     TokenType.POSTCODE))
         if assignment.qualifier:

-            sdata.set_qualifiers(self.query.get_tokens(assignment.qualifier,
-                                                       TokenType.QUALIFIER))
+            tokens = self.get_qualifier_tokens(assignment.qualifier)
+            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:
 
         if assignment.address:

-            sdata.set_ranking([self.get_addr_ranking(r) for r in assignment.address])
+            if not assignment.name and assignment.housenumber:
+                # housenumber search: the first item needs to be handled like
+                # a name in ranking or penalties are not comparable with
+                # normal searches.
+                sdata.set_ranking([self.get_name_ranking(assignment.address[0],
+                                                         db_field='nameaddress_vector')]
+                                  + [self.get_addr_ranking(r) for r in assignment.address[1:]])
+            else:
+                sdata.set_ranking([self.get_addr_ranking(r) for r in assignment.address])

         else:
             sdata.rankings = []
 
         return sdata
 
 
         else:
             sdata.rankings = []
 
         return sdata
 
 

-    def get_search_categories(self,
-                              assignment: TokenAssignment) -> Optional[dbf.WeightedCategories]:
-        """ Collect tokens for category search or use the categories
-            requested per parameter.
-            Returns None if no category search is requested.
+    def get_country_tokens(self, trange: TokenRange) -> List[Token]:
+        """ Return the list of country tokens for the given range,
+            optionally filtered by the country list from the details
+            parameters.

         """
         """

-        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])
+        tokens = self.query.get_tokens(trange, TokenType.COUNTRY)
+        if self.details.countries:
+            tokens = [t for t in tokens if t.lookup_word in self.details.countries]
+
+        return tokens
+

 
 

+    def get_qualifier_tokens(self, trange: TokenRange) -> List[Token]:
+        """ Return the list of qualifier tokens for the given range,
+            optionally filtered by the qualifier list from the details
+            parameters.
+        """
+        tokens = self.query.get_tokens(trange, TokenType.QUALIFIER)

         if self.details.categories:
         if self.details.categories:

-            return dbf.WeightedCategories(self.details.categories,
-                                          [0.0] * len(self.details.categories))
+            tokens = [t for t in tokens if t.get_category() in self.details.categories]
+
+        return tokens
+
+
+    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.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
 
 
         return None