restrict base results in near search by rank

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

diff --git a/nominatim/api/search/icu_tokenizer.py b/nominatim/api/search/icu_tokenizer.py
index 17e679057ecb5a4eb63daeb08f38e361f19a3ada..196fde2a8444e69d5d74a0a0310dd94812425d96 100644 (file)
--- a/nominatim/api/search/icu_tokenizer.py
+++ b/nominatim/api/search/icu_tokenizer.py
@@ -83,7 +83,7 @@ class ICUToken(qmod.Token):
         seq = difflib.SequenceMatcher(a=self.lookup_word, b=norm)
         distance = 0
         for tag, afrom, ato, bfrom, bto in seq.get_opcodes():
         seq = difflib.SequenceMatcher(a=self.lookup_word, b=norm)
         distance = 0
         for tag, afrom, ato, bfrom, bto in seq.get_opcodes():

-            if tag == 'delete' and (afrom == 0 or ato == len(self.lookup_word)):
+            if tag in ('delete', 'insert') and (afrom == 0 or ato == len(self.lookup_word)):

                 distance += 1
             elif tag == 'replace':
                 distance += max((ato-afrom), (bto-bfrom))
                 distance += 1
             elif tag == 'replace':
                 distance += max((ato-afrom), (bto-bfrom))


@@ -101,10 +101,16 @@ class ICUToken(qmod.Token):
         penalty = 0.0
         if row.type == 'w':
             penalty = 0.3
         penalty = 0.0
         if row.type == 'w':
             penalty = 0.3

+        elif row.type == 'W':
+            if len(row.word_token) == 1 and row.word_token == row.word:
+                penalty = 0.2 if row.word.isdigit() else 0.3

         elif row.type == 'H':
             penalty = sum(0.1 for c in row.word_token if c != ' ' and not c.isdigit())
             if all(not c.isdigit() for c in row.word_token):
                 penalty += 0.2 * (len(row.word_token) - 1)
         elif row.type == 'H':
             penalty = sum(0.1 for c in row.word_token if c != ' ' and not c.isdigit())
             if all(not c.isdigit() for c in row.word_token):
                 penalty += 0.2 * (len(row.word_token) - 1)

+        elif row.type == 'C':
+            if len(row.word_token) == 1:
+                penalty = 0.3

 
         if row.info is None:
             lookup_word = row.word
 
         if row.info is None:
             lookup_word = row.word


@@ -133,10 +139,19 @@ class ICUQueryAnalyzer(AbstractQueryAnalyzer):
     async def setup(self) -> None:
         """ Set up static data structures needed for the analysis.
         """
     async def setup(self) -> None:
         """ Set up static data structures needed for the analysis.
         """

-        rules = await self.conn.get_property('tokenizer_import_normalisation')
-        self.normalizer = Transliterator.createFromRules("normalization", rules)
-        rules = await self.conn.get_property('tokenizer_import_transliteration')
-        self.transliterator = Transliterator.createFromRules("transliteration", rules)
+        async def _make_normalizer() -> Any:
+            rules = await self.conn.get_property('tokenizer_import_normalisation')
+            return Transliterator.createFromRules("normalization", rules)
+
+        self.normalizer = await self.conn.get_cached_value('ICUTOK', 'normalizer',
+                                                           _make_normalizer)
+
+        async def _make_transliterator() -> Any:
+            rules = await self.conn.get_property('tokenizer_import_transliteration')
+            return Transliterator.createFromRules("transliteration", rules)
+
+        self.transliterator = await self.conn.get_cached_value('ICUTOK', 'transliterator',
+                                                               _make_transliterator)

 
         if 'word' not in self.conn.t.meta.tables:
             sa.Table('word', self.conn.t.meta,
 
         if 'word' not in self.conn.t.meta.tables:
             sa.Table('word', self.conn.t.meta,


@@ -153,7 +168,7 @@ class ICUQueryAnalyzer(AbstractQueryAnalyzer):
         """
         log().section('Analyze query (using ICU tokenizer)')
         normalized = list(filter(lambda p: p.text,
         """
         log().section('Analyze query (using ICU tokenizer)')
         normalized = list(filter(lambda p: p.text,

-                                 (qmod.Phrase(p.ptype, self.normalizer.transliterate(p.text))
+                                 (qmod.Phrase(p.ptype, self.normalize_text(p.text))

                                   for p in phrases)))
         query = qmod.QueryStruct(normalized)
         log().var_dump('Normalized query', query.source)
                                   for p in phrases)))
         query = qmod.QueryStruct(normalized)
         log().var_dump('Normalized query', query.source)


@@ -187,6 +202,14 @@ class ICUQueryAnalyzer(AbstractQueryAnalyzer):
         return query
 
 
         return query
 
 

+    def normalize_text(self, text: str) -> str:
+        """ Bring the given text into a normalized form. That is the
+            standardized form search will work with. All information removed
+            at this stage is inevitably lost.
+        """
+        return cast(str, self.normalizer.transliterate(text))
+
+

     def split_query(self, query: qmod.QueryStruct) -> Tuple[QueryParts, WordDict]:
         """ Transliterate the phrases and split them into tokens.
 
     def split_query(self, query: qmod.QueryStruct) -> Tuple[QueryParts, WordDict]:
         """ Transliterate the phrases and split them into tokens.
 


@@ -248,12 +271,11 @@ class ICUQueryAnalyzer(AbstractQueryAnalyzer):
                        and (repl.ttype != qmod.TokenType.HOUSENUMBER
                             or len(tlist.tokens[0].lookup_word) > 4):
                         repl.add_penalty(0.39)
                        and (repl.ttype != qmod.TokenType.HOUSENUMBER
                             or len(tlist.tokens[0].lookup_word) > 4):
                         repl.add_penalty(0.39)

-            elif tlist.ttype == qmod.TokenType.HOUSENUMBER:
+            elif tlist.ttype == qmod.TokenType.HOUSENUMBER \
+                 and len(tlist.tokens[0].lookup_word) <= 3:

                 if any(c.isdigit() for c in tlist.tokens[0].lookup_word):
                     for repl in node.starting:
                 if any(c.isdigit() for c in tlist.tokens[0].lookup_word):
                     for repl in node.starting:

-                        if repl.end == tlist.end and repl.ttype != qmod.TokenType.HOUSENUMBER \
-                           and (repl.ttype != qmod.TokenType.HOUSENUMBER
-                                or len(tlist.tokens[0].lookup_word) <= 3):
+                        if repl.end == tlist.end and repl.ttype != qmod.TokenType.HOUSENUMBER:

                             repl.add_penalty(0.5 - tlist.tokens[0].penalty)
             elif tlist.ttype not in (qmod.TokenType.COUNTRY, qmod.TokenType.PARTIAL):
                 norm = parts[i].normalized
                             repl.add_penalty(0.5 - tlist.tokens[0].penalty)
             elif tlist.ttype not in (qmod.TokenType.COUNTRY, qmod.TokenType.PARTIAL):
                 norm = parts[i].normalized