Merge pull request #3346 from lonvia/reduce-artificial-importance

[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 f89d8b62e827178928bcc3c8d7fd210a30ef00a8..f2b653f2c2def3c3657abdc9159c3be66d215d5f 100644 (file)
--- a/nominatim/api/search/db_search_builder.py
+++ b/nominatim/api/search/db_search_builder.py
@@ -15,6 +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

+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]],


@@ -113,7 +114,10 @@ class SearchBuilder:
             penalty = min(near_items.penalties)
             near_items.penalties = [p - penalty for p in near_items.penalties]
             for search in builder:
             penalty = min(near_items.penalties)
             near_items.penalties = [p - penalty for p in near_items.penalties]
             for search in builder:

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

         else:
             for search in builder:
                 search.penalty += assignment.penalty
         else:
             for search in builder:
                 search.penalty += assignment.penalty


@@ -149,7 +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)
 


@@ -159,23 +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.
         """

-        sdata.lookups = [dbf.FieldLookup('name_vector', [t.token for t in hnrs], 'lookup_any')]
+        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 for trange in address
-                       for t in self.query.get_partials_list(trange)]
+        partials = {t.token: t.count for trange in address
+                       for t in self.query.get_partials_list(trange)}

 
 

-        if len(partials) != 1 or partials[0].count < 10000:
+        if expected_count < 8000:

             sdata.lookups.append(dbf.FieldLookup('nameaddress_vector',
             sdata.lookups.append(dbf.FieldLookup('nameaddress_vector',

-                                                 [t.token for t in partials], 'lookup_all'))
+                                                 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:
         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(
             sdata.lookups.append(

-                dbf.FieldLookup('nameaddress_vector',
-                                [t.token for t
-                                 in self.query.get_tokens(address[0], TokenType.WORD)],
-                                'lookup_any'))
+                dbf.FieldLookup('nameaddress_vector', addr_fulls, lookups.LookupAny))

 
         sdata.housenumbers = dbf.WeightedStrings([], [])
 
         sdata.housenumbers = dbf.WeightedStrings([], [])

-        yield dbs.PlaceSearch(0.05, sdata, sum(t.count for t in hnrs))
+        yield dbs.PlaceSearch(0.05, sdata, expected_count)

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


@@ -200,45 +209,48 @@ class SearchBuilder:
             are and tries to find a lookup that optimizes index use.
         """
         penalty = 0.0 # extra penalty
             are and tries to find a lookup that optimizes index use.
         """
         penalty = 0.0 # extra penalty

-        name_partials = self.query.get_partials_list(name)
-        name_tokens = [t.token for t in name_partials]
+        name_partials = {t.token: t for t in self.query.get_partials_list(name)}

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

 
 

-        partials_indexed = all(t.is_indexed for t in name_partials) \
+        partials_indexed = all(t.is_indexed for t in name_partials.values()) \

                            and all(t.is_indexed for t in addr_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))
+        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:
 
         if (len(name_partials) > 3 or exp_count < 8000) and partials_indexed:

-            yield penalty, exp_count, dbf.lookup_by_names(name_tokens, addr_tokens)
+            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)

-        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
-        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')
+        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
+            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.

-        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')]
+        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'))
-            penalty += 0.35 * max(0, 5 - len(name_partials) - len(addr_tokens))
+                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
 
 
             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)


@@ -247,7 +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:


@@ -305,11 +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,


@@ -323,24 +333,54 @@ class SearchBuilder:
                               self.query.get_tokens(assignment.postcode,
                                                     TokenType.POSTCODE))
         if assignment.qualifier:
                               self.query.get_tokens(assignment.postcode,
                                                     TokenType.POSTCODE))
         if assignment.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
+            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:
             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])
+            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_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.
+        """
+        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:
+            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.
     def get_near_items(self, assignment: TokenAssignment) -> Optional[dbf.WeightedCategories]:
         """ Collect tokens for near items search or use the categories
             requested per parameter.