+# SPDX-License-Identifier: GPL-2.0-only
+#
+# This file is part of Nominatim. (https://nominatim.org)
+#
+# Copyright (C) 2022 by the Nominatim developer community.
+# For a full list of authors see the git log.
"""
Tokenizer implementing normalisation as used before Nominatim 4.
"""
from nominatim.db import utils as db_utils
from nominatim.db.sql_preprocessor import SQLPreprocessor
from nominatim.errors import UsageError
+from nominatim.tokenizer.base import AbstractAnalyzer, AbstractTokenizer
DBCFG_NORMALIZATION = "tokenizer_normalization"
DBCFG_MAXWORDFREQ = "tokenizer_maxwordfreq"
raise UsageError("Database module cannot be accessed.") from err
-class LegacyTokenizer:
+class LegacyTokenizer(AbstractTokenizer):
""" The legacy tokenizer uses a special PostgreSQL module to normalize
names and queries. The tokenizer thus implements normalization through
calls to the database.
self._init_db_tables(config)
- def init_from_project(self):
+ def init_from_project(self, _):
""" Initialise the tokenizer from the project directory.
"""
with connect(self.dsn) as conn:
modulepath=modulepath)
- def check_database(self):
+ def check_database(self, _):
""" Check that the tokenizer is set up correctly.
"""
hint = """\
self._save_config(conn, config)
+ def update_statistics(self):
+ """ Recompute the frequency of full words.
+ """
+ with connect(self.dsn) as conn:
+ if conn.table_exists('search_name'):
+ with conn.cursor() as cur:
+ cur.drop_table("word_frequencies")
+ LOG.info("Computing word frequencies")
+ cur.execute("""CREATE TEMP TABLE word_frequencies AS
+ SELECT unnest(name_vector) as id, count(*)
+ FROM search_name GROUP BY id""")
+ cur.execute("CREATE INDEX ON word_frequencies(id)")
+ LOG.info("Update word table with recomputed frequencies")
+ cur.execute("""UPDATE word SET search_name_count = count
+ FROM word_frequencies
+ WHERE word_token like ' %' and word_id = id""")
+ cur.drop_table("word_frequencies")
+ conn.commit()
+
def name_analyzer(self):
""" Create a new analyzer for tokenizing names and queries
using this tokinzer. Analyzers are context managers and should
properties.set_property(conn, DBCFG_MAXWORDFREQ, config.MAX_WORD_FREQUENCY)
-class LegacyNameAnalyzer:
+class LegacyNameAnalyzer(AbstractAnalyzer):
""" The legacy analyzer uses the special Postgresql module for
splitting names.
self._cache = _TokenCache(self.conn)
- def __enter__(self):
- return self
-
-
- def __exit__(self, exc_type, exc_value, traceback):
- self.close()
-
-
def close(self):
""" Free all resources used by the analyzer.
"""
to_delete = existing_phrases - norm_phrases
if to_add:
- psycopg2.extras.execute_values(
- cur,
+ cur.execute_values(
""" INSERT INTO word (word_id, word_token, word, class, type,
search_name_count, operator)
(SELECT nextval('seq_word'), ' ' || make_standard_name(name), name,
to_add)
if to_delete and should_replace:
- psycopg2.extras.execute_values(
- cur,
+ cur.execute_values(
""" DELETE FROM word USING (VALUES %s) as v(name, in_class, in_type, op)
WHERE word = name and class = in_class and type = in_type
and ((op = '-' and operator is null) or op = operator)""",
"""
token_info = _TokenInfo(self._cache)
- names = place.get('name')
+ names = place.name
if names:
token_info.add_names(self.conn, names)
- country_feature = place.get('country_feature')
- if country_feature and re.fullmatch(r'[A-Za-z][A-Za-z]', country_feature):
- self.add_country_names(country_feature.lower(), names)
+ if place.is_country():
+ self.add_country_names(place.country_code, names)
- address = place.get('address')
+ address = place.address
if address:
self._process_place_address(token_info, address)
with conn.cursor() as cur:
return cur.scalar("SELECT word_ids_from_name(%s)::text", (name, ))
- self.data['street'] = self.cache.streets.get(street, _get_street)
+ tokens = self.cache.streets.get(street, _get_street)
+ if tokens:
+ self.data['street'] = tokens
def add_place(self, conn, place):
tokens = {}
for key, value in terms:
- tokens[key] = self.cache.address_terms.get(value, _get_address_term)
+ items = self.cache.address_terms.get(value, _get_address_term)
+ if items[0] or items[1]:
+ tokens[key] = items
- self.data['addr'] = tokens
+ if tokens:
+ self.data['addr'] = tokens
class _LRU:
with conn.cursor() as cur:
cur.execute("""SELECT i, ARRAY[getorcreate_housenumber_id(i::text)]::text
FROM generate_series(1, 100) as i""")
- self._cached_housenumbers = {str(r[0]) : r[1] for r in cur}
+ self._cached_housenumbers = {str(r[0]): r[1] for r in cur}
# For postcodes remember the ones that have already been added
self.postcodes = set()
projects / nominatim.git / blobdiff