use analyser provided in the 'analyzer' property

Implements per-name choice of analyzer. If a non-default
analyzer is choosen, then the 'word' identifier is extended
with the name of the ana;yzer, so that we still have unique
items.

nominatim/tokenizer/icu_rule_loader.py

@@ -10,6 +10,7 @@ from nominatim.config import flatten_config_list
 from nominatim.db.properties import set_property, get_property
 from nominatim.errors import UsageError
 from nominatim.tokenizer.place_sanitizer import PlaceSanitizer
+from nominatim.tokenizer.icu_token_analysis import ICUTokenAnalysis
 
 LOG = logging.getLogger()
 
@@ -74,8 +75,8 @@ class ICURuleLoader:
     def make_token_analysis(self):
         """ Create a token analyser from the reviouly loaded rules.
         """
-        return self.analysis[None].create(self.normalization_rules,
+        return ICUTokenAnalysis(self.normalization_rules,
-                                          self.transliteration_rules)
+                                self.transliteration_rules, self.analysis)
 
 
     def get_search_rules(self):
@@ -149,15 +150,7 @@ class TokenAnalyzerRule:
         module_name = 'nominatim.tokenizer.token_analysis.' \
                       + _get_section(rules, 'analyzer').replace('-', '_')
         analysis_mod = importlib.import_module(module_name)
-        self._mod_create = analysis_mod.create
+        self.create = analysis_mod.create
 
         # Load the configuration.
         self.config = analysis_mod.configure(rules, normalization_rules)
-
-
-    def create(self, normalization_rules, transliteration_rules):
-        """ Create an analyzer from the given rules.
-        """
-        return self._mod_create(normalization_rules,
-                                transliteration_rules,
-                                self.config)

nominatim/tokenizer/icu_token_analysis.py

@@ -0,0 +1,23 @@
+"""
+Container class collecting all components required to transform an OSM name
+into a Nominatim token.
+"""
+
+from icu import Transliterator
+
+class ICUTokenAnalysis:
+    """ Container class collecting the transliterators and token analysis
+        modules for a single NameAnalyser instance.
+    """
+
+    def __init__(self, norm_rules, trans_rules, analysis_rules):
+        self.normalizer = Transliterator.createFromRules("icu_normalization",
+                                                         norm_rules)
+        trans_rules += ";[:Space:]+ > ' '"
+        self.to_ascii = Transliterator.createFromRules("icu_to_ascii",
+                                                       trans_rules)
+        self.search = Transliterator.createFromRules("icu_search",
+                                                     norm_rules + trans_rules)
+
+        self.analysis = {name: arules.create(self.to_ascii, arules.config)
+                         for name, arules in analysis_rules.items()}

nominatim/tokenizer/icu_tokenizer.py

@@ -164,7 +164,7 @@ class LegacyICUTokenizer(AbstractTokenizer):
         """ Count the partial terms from the names in the place table.
         """
         words = Counter()
-        name_proc = self.loader.make_token_analysis()
+        analysis = self.loader.make_token_analysis()
 
         with conn.cursor(name="words") as cur:
             cur.execute(""" SELECT v, count(*) FROM
@@ -172,12 +172,10 @@ class LegacyICUTokenizer(AbstractTokenizer):
                             WHERE length(v) < 75 GROUP BY v""")
 
             for name, cnt in cur:
-                terms = set()
+                word = analysis.search.transliterate(name)
-                for word in name_proc.get_variants_ascii(name_proc.get_normalized(name)):
+                if word and ' ' in word:
-                    if ' ' in word:
+                    for term in set(word.split()):
-                        terms.update(word.split())
+                        words[term] += cnt
-                for term in terms:
-                    words[term] += cnt
 
         return words
 
@@ -209,14 +207,14 @@ class LegacyICUNameAnalyzer(AbstractAnalyzer):
     def _search_normalized(self, name):
         """ Return the search token transliteration of the given name.
         """
-        return self.token_analysis.get_search_normalized(name)
+        return self.token_analysis.search.transliterate(name).strip()
 
 
     def _normalized(self, name):
         """ Return the normalized version of the given name with all
             non-relevant information removed.
         """
-        return self.token_analysis.get_normalized(name)
+        return self.token_analysis.normalizer.transliterate(name).strip()
 
 
     def get_word_token_info(self, words):
@@ -456,6 +454,7 @@ class LegacyICUNameAnalyzer(AbstractAnalyzer):
         if addr_terms:
             token_info.add_address_terms(addr_terms)
 
+
     def _compute_partial_tokens(self, name):
         """ Normalize the given term, split it into partial words and return
             then token list for them.
@@ -492,19 +491,25 @@ class LegacyICUNameAnalyzer(AbstractAnalyzer):
         partial_tokens = set()
 
         for name in names:
+            analyzer_id = name.get_attr('analyzer')
             norm_name = self._normalized(name.name)
-            full, part = self._cache.names.get(norm_name, (None, None))
+            if analyzer_id is None:
+                token_id = norm_name
+            else:
+                token_id = f'{norm_name}@{analyzer_id}'
+
+            full, part = self._cache.names.get(token_id, (None, None))
             if full is None:
-                variants = self.token_analysis.get_variants_ascii(norm_name)
+                variants = self.token_analysis.analysis[analyzer_id].get_variants_ascii(norm_name)
                 if not variants:
                     continue
 
                 with self.conn.cursor() as cur:
                     cur.execute("SELECT (getorcreate_full_word(%s, %s)).*",
-                                (norm_name, variants))
+                                (token_id, variants))
                     full, part = cur.fetchone()
 
-                self._cache.names[norm_name] = (full, part)
+                self._cache.names[token_id] = (full, part)
 
             full_tokens.add(full)
             partial_tokens.update(part)

nominatim/tokenizer/sanitizers/split_name_list.py

@@ -24,7 +24,6 @@ def create(func):
         new_names = []
         for name in obj.names:
             split_names = regexp.split(name.name)
-            print(split_names)
             if len(split_names) == 1:
                 new_names.append(name)
             else:

nominatim/tokenizer/token_analysis/generic.py

@@ -131,10 +131,10 @@ def _create_variants(src, preflag, postflag, repl, decompose):
 
 ### Analysis section
 
-def create(norm_rules, trans_rules, config):
+def create(trans_rules, config):
     """ Create a new token analysis instance for this module.
     """
-    return GenericTokenAnalysis(norm_rules, trans_rules, config)
+    return GenericTokenAnalysis(trans_rules, config)
 
 
 class GenericTokenAnalysis:
@@ -142,14 +142,8 @@ class GenericTokenAnalysis:
         and provides the functions to apply the transformations.
     """
 
-    def __init__(self, norm_rules, trans_rules, config):
+    def __init__(self, to_ascii, config):
-        self.normalizer = Transliterator.createFromRules("icu_normalization",
+        self.to_ascii = to_ascii
-                                                         norm_rules)
-        self.to_ascii = Transliterator.createFromRules("icu_to_ascii",
-                                                       trans_rules +
-                                                       ";[:Space:]+ > ' '")
-        self.search = Transliterator.createFromRules("icu_search",
-                                                     norm_rules + trans_rules)
 
         # Set up datrie
         self.replacements = datrie.Trie(config['chars'])
@@ -157,12 +151,6 @@ class GenericTokenAnalysis:
             self.replacements[src] = repllist
 
 
-    def get_normalized(self, name):
-        """ Normalize the given name, i.e. remove all elements not relevant
-            for search.
-        """
-        return self.normalizer.transliterate(name).strip()
-
     def get_variants_ascii(self, norm_name):
         """ Compute the spelling variants for the given normalized name
             and transliterate the result.
@@ -213,10 +201,3 @@ class GenericTokenAnalysis:
                 results.add(trans_name)
 
         return list(results)
-
-
-    def get_search_normalized(self, name):
-        """ Return the normalized version of the name (including transliteration)
-            to be applied at search time.
-        """
-        return self.search.transliterate(' ' + name + ' ').strip()

test/python/test_tokenizer_icu.py

@@ -169,9 +169,7 @@ def test_init_word_table(tokenizer_factory, test_config, place_row, word_table):
     tok.init_new_db(test_config)
 
     assert word_table.get_partial_words() == {('test', 1),
-                                              ('no', 1), ('area', 2),
+                                              ('no', 1), ('area', 2)}
-                                              ('holz', 1), ('strasse', 1),
-                                              ('str', 1)}
 
 
 def test_init_from_project(monkeypatch, test_config, tokenizer_factory):