Merge pull request #3653 from lonvia/trailing-spaces-in-normalization

[nominatim.git] / src / nominatim_api / search / icu_tokenizer.py

diff --git a/src/nominatim_api/search/icu_tokenizer.py b/src/nominatim_api/search/icu_tokenizer.py
index d52614fdaf4863b0aaa2e2721659988d9c03470d..487dd1710354c9c9801cef9ce22033c1a5169e87 100644 (file)
--- a/src/nominatim_api/search/icu_tokenizer.py
+++ b/src/nominatim_api/search/icu_tokenizer.py
@@ -7,7 +7,7 @@
 """
 Implementation of query analysis for the ICU tokenizer.
 """
-from typing import Tuple, Dict, List, Optional, NamedTuple, Iterator, Any, cast
+from typing import Tuple, Dict, List, Optional, Iterator, Any, cast
 from collections import defaultdict
 import dataclasses
 import difflib
@@ -36,17 +36,30 @@ DB_TO_TOKEN_TYPE = {
     'C': qmod.TokenType.COUNTRY
 }
 
+PENALTY_IN_TOKEN_BREAK = {
+     qmod.BreakType.START: 0.5,
+     qmod.BreakType.END: 0.5,
+     qmod.BreakType.PHRASE: 0.5,
+     qmod.BreakType.SOFT_PHRASE: 0.5,
+     qmod.BreakType.WORD: 0.1,
+     qmod.BreakType.PART: 0.0,
+     qmod.BreakType.TOKEN: 0.0
+}
+
 
-class QueryPart(NamedTuple):
+@dataclasses.dataclass
+class QueryPart:
     """ Normalized and transliterated form of a single term in the query.
         When the term came out of a split during the transliteration,
         the normalized string is the full word before transliteration.
         The word number keeps track of the word before transliteration
         and can be used to identify partial transliterated terms.
+        Penalty is the break penalty for the break following the token.
     """
     token: str
     normalized: str
     word_number: int
+    penalty: float
 
 
 QueryParts = List[QueryPart]
@@ -60,10 +73,12 @@ def yield_words(terms: List[QueryPart], start: int) -> Iterator[Tuple[str, qmod.
     total = len(terms)
     for first in range(start, total):
         word = terms[first].token
-        yield word, qmod.TokenRange(first, first + 1)
+        penalty = PENALTY_IN_TOKEN_BREAK[qmod.BreakType.WORD]
+        yield word, qmod.TokenRange(first, first + 1, penalty=penalty)
         for last in range(first + 1, min(first + 20, total)):
             word = ' '.join((word, terms[last].token))
-            yield word, qmod.TokenRange(first, last + 1)
+            penalty += terms[last - 1].penalty
+            yield word, qmod.TokenRange(first, last + 1, penalty=penalty)
 
 
 @dataclasses.dataclass
@@ -96,25 +111,25 @@ class ICUToken(qmod.Token):
         self.penalty += (distance/len(self.lookup_word))
 
     @staticmethod
-    def from_db_row(row: SaRow) -> 'ICUToken':
+    def from_db_row(row: SaRow, base_penalty: float = 0.0) -> 'ICUToken':
         """ Create a ICUToken from the row of the word table.
         """
         count = 1 if row.info is None else row.info.get('count', 1)
         addr_count = 1 if row.info is None else row.info.get('addr_count', 1)
 
-        penalty = 0.0
+        penalty = base_penalty
         if row.type == 'w':
-            penalty = 0.3
+            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
+                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())
+            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
+                penalty += 0.3
 
         if row.info is None:
             lookup_word = row.word
@@ -204,7 +219,7 @@ class ICUQueryAnalyzer(AbstractQueryAnalyzer):
 
         for row in await self.lookup_in_db(list(words.keys())):
             for trange in words[row.word_token]:
-                token = ICUToken.from_db_row(row)
+                token = ICUToken.from_db_row(row, trange.penalty or 0.0)
                 if row.type == 'S':
                     if row.info['op'] in ('in', 'near'):
                         if trange.start == 0:
@@ -229,7 +244,7 @@ class ICUQueryAnalyzer(AbstractQueryAnalyzer):
             standardized form search will work with. All information removed
             at this stage is inevitably lost.
         """
-        return cast(str, self.normalizer.transliterate(text))
+        return cast(str, self.normalizer.transliterate(text)).strip('-: ')
 
     def split_query(self, query: qmod.QueryStruct) -> Tuple[QueryParts, WordDict]:
         """ Transliterate the phrases and split them into tokens.
@@ -256,9 +271,11 @@ class ICUQueryAnalyzer(AbstractQueryAnalyzer):
                 if trans:
                     for term in trans.split(' '):
                         if term:
-                            parts.append(QueryPart(term, word, wordnr))
+                            parts.append(QueryPart(term, word, wordnr,
+                                                   PENALTY_IN_TOKEN_BREAK[qmod.BreakType.TOKEN]))
                             query.add_node(qmod.BreakType.TOKEN, phrase.ptype)
                     query.nodes[-1].btype = qmod.BreakType(breakchar)
+                    parts[-1].penalty = PENALTY_IN_TOKEN_BREAK[qmod.BreakType(breakchar)]
                 wordnr += 1
 
             for word, wrange in yield_words(parts, phrase_start):
@@ -280,7 +297,7 @@ class ICUQueryAnalyzer(AbstractQueryAnalyzer):
         """ Add tokens to query that are not saved in the database.
         """
         for part, node, i in zip(parts, query.nodes, range(1000)):
-            if len(part.token) <= 4 and part[0].isdigit()\
+            if len(part.token) <= 4 and part.token.isdigit()\
                and not node.has_tokens(i+1, qmod.TokenType.HOUSENUMBER):
                 query.add_token(qmod.TokenRange(i, i+1), qmod.TokenType.HOUSENUMBER,
                                 ICUToken(penalty=0.5, token=0,