Tokenizer implementing normalisation as used before Nominatim 4 but using
libICU instead of the PostgreSQL module.
"""
-from collections import Counter
import itertools
import json
import logging
import re
from textwrap import dedent
-from pathlib import Path
from nominatim.db.connection import connect
from nominatim.db.properties import set_property, get_property
from nominatim.db.utils import CopyBuffer
from nominatim.db.sql_preprocessor import SQLPreprocessor
+from nominatim.indexer.place_info import PlaceInfo
from nominatim.tokenizer.icu_rule_loader import ICURuleLoader
-from nominatim.tokenizer.icu_name_processor import ICUNameProcessor, ICUNameProcessorRules
from nominatim.tokenizer.base import AbstractAnalyzer, AbstractTokenizer
-DBCFG_MAXWORDFREQ = "tokenizer_maxwordfreq"
DBCFG_TERM_NORMALIZATION = "tokenizer_term_normalization"
LOG = logging.getLogger()
def __init__(self, dsn, data_dir):
self.dsn = dsn
self.data_dir = data_dir
- self.naming_rules = None
+ self.loader = None
self.term_normalization = None
- self.max_word_frequency = None
def init_new_db(self, config, init_db=True):
This copies all necessary data in the project directory to make
sure the tokenizer remains stable even over updates.
"""
- if config.TOKENIZER_CONFIG:
- cfgfile = Path(config.TOKENIZER_CONFIG)
- else:
- cfgfile = config.config_dir / 'icu_tokenizer.yaml'
+ self.loader = ICURuleLoader(config)
- loader = ICURuleLoader(cfgfile)
- self.naming_rules = ICUNameProcessorRules(loader=loader)
self.term_normalization = config.TERM_NORMALIZATION
- self.max_word_frequency = config.MAX_WORD_FREQUENCY
self._install_php(config.lib_dir.php)
- self._save_config(config)
+ self._save_config()
if init_db:
self.update_sql_functions(config)
self._init_db_tables(config)
- def init_from_project(self):
+ def init_from_project(self, config):
""" Initialise the tokenizer from the project directory.
"""
+ self.loader = ICURuleLoader(config)
+
with connect(self.dsn) as conn:
- self.naming_rules = ICUNameProcessorRules(conn=conn)
+ self.loader.load_config_from_db(conn)
self.term_normalization = get_property(conn, DBCFG_TERM_NORMALIZATION)
- self.max_word_frequency = get_property(conn, DBCFG_MAXWORDFREQ)
def finalize_import(self, _):
""" Reimport the SQL functions for this tokenizer.
"""
with connect(self.dsn) as conn:
- max_word_freq = get_property(conn, DBCFG_MAXWORDFREQ)
sqlp = SQLPreprocessor(conn, config)
- sqlp.run_sql_file(conn, 'tokenizer/icu_tokenizer.sql',
- max_word_freq=max_word_freq)
+ sqlp.run_sql_file(conn, 'tokenizer/icu_tokenizer.sql')
- def check_database(self):
+ def check_database(self, config):
""" Check that the tokenizer is set up correctly.
"""
- self.init_from_project()
+ self.init_from_project(config)
- if self.naming_rules is None:
+ if self.term_normalization is None:
return "Configuration for tokenizer 'icu' are missing."
return None
+ def update_statistics(self):
+ """ Recompute frequencies for all name words.
+ """
+ with connect(self.dsn) as conn:
+ 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 info = info || jsonb_build_object('count', count)
+ FROM word_frequencies WHERE 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
Analyzers are not thread-safe. You need to instantiate one per thread.
"""
- return LegacyICUNameAnalyzer(self.dsn, ICUNameProcessor(self.naming_rules))
+ return LegacyICUNameAnalyzer(self.dsn, self.loader.make_sanitizer(),
+ self.loader.make_token_analysis())
def _install_php(self, phpdir):
php_file = self.data_dir / "tokenizer.php"
php_file.write_text(dedent(f"""\
<?php
- @define('CONST_Max_Word_Frequency', {self.max_word_frequency});
+ @define('CONST_Max_Word_Frequency', 10000000);
@define('CONST_Term_Normalization_Rules', "{self.term_normalization}");
- @define('CONST_Transliteration', "{self.naming_rules.search_rules}");
+ @define('CONST_Transliteration', "{self.loader.get_search_rules()}");
require_once('{phpdir}/tokenizer/icu_tokenizer.php');"""))
- def _save_config(self, config):
+ def _save_config(self):
""" Save the configuration that needs to remain stable for the given
database as database properties.
"""
with connect(self.dsn) as conn:
- self.naming_rules.save_rules(conn)
-
- set_property(conn, DBCFG_MAXWORDFREQ, config.MAX_WORD_FREQUENCY)
+ self.loader.save_config_to_db(conn)
set_property(conn, DBCFG_TERM_NORMALIZATION, self.term_normalization)
sqlp.run_sql_file(conn, 'tokenizer/icu_tokenizer_tables.sql')
conn.commit()
- LOG.warning("Precomputing word tokens")
-
- # get partial words and their frequencies
- words = self._count_partial_terms(conn)
-
- # copy them back into the word table
- with CopyBuffer() as copystr:
- for term, cnt in words.items():
- copystr.add('w', term, json.dumps({'count': cnt}))
-
- with conn.cursor() as cur:
- copystr.copy_out(cur, 'word',
- columns=['type', 'word_token', 'info'])
- cur.execute("""UPDATE word SET word_id = nextval('seq_word')
- WHERE word_id is null and type = 'w'""")
-
- conn.commit()
-
- def _count_partial_terms(self, conn):
- """ Count the partial terms from the names in the place table.
- """
- words = Counter()
- name_proc = ICUNameProcessor(self.naming_rules)
-
- with conn.cursor(name="words") as cur:
- cur.execute(""" SELECT v, count(*) FROM
- (SELECT svals(name) as v FROM place)x
- WHERE length(v) < 75 GROUP BY v""")
-
- for name, cnt in cur:
- terms = set()
- for word in name_proc.get_variants_ascii(name_proc.get_normalized(name)):
- if ' ' in word:
- terms.update(word.split())
- for term in terms:
- words[term] += cnt
-
- return words
-
class LegacyICUNameAnalyzer(AbstractAnalyzer):
""" The legacy analyzer uses the ICU library for splitting names.
normalization.
"""
- def __init__(self, dsn, name_proc):
+ def __init__(self, dsn, sanitizer, token_analysis):
self.conn = connect(dsn).connection
self.conn.autocommit = True
- self.name_processor = name_proc
+ self.sanitizer = sanitizer
+ self.token_analysis = token_analysis
self._cache = _TokenCache()
self.conn = None
+ def _search_normalized(self, name):
+ """ Return the search token transliteration of the given 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.normalizer.transliterate(name).strip()
+
+
def get_word_token_info(self, words):
""" Return token information for the given list of words.
If a word starts with # it is assumed to be a full name
partial_tokens = {}
for word in words:
if word.startswith('#'):
- full_tokens[word] = self.name_processor.get_search_normalized(word[1:])
+ full_tokens[word] = self._search_normalized(word[1:])
else:
- partial_tokens[word] = self.name_processor.get_search_normalized(word)
+ partial_tokens[word] = self._search_normalized(word)
with self.conn.cursor() as cur:
cur.execute("""SELECT word_token, word_id
This function takes minor shortcuts on transliteration.
"""
- return self.name_processor.get_search_normalized(hnr)
+ return self._search_normalized(hnr)
def update_postcodes_from_db(self):
""" Update postcode tokens in the word table from the location_postcode
if postcode is None:
to_delete.append(word)
else:
- copystr.add(self.name_processor.get_search_normalized(postcode),
+ copystr.add(self._search_normalized(postcode),
'P', postcode)
if to_delete:
completely replaced. Otherwise the phrases are added to the
already existing ones.
"""
- norm_phrases = set(((self.name_processor.get_normalized(p[0]), p[1], p[2], p[3])
+ norm_phrases = set(((self._normalized(p[0]), p[1], p[2], p[3])
for p in phrases))
with self.conn.cursor() as cur:
added = 0
with CopyBuffer() as copystr:
for word, cls, typ, oper in to_add:
- term = self.name_processor.get_search_normalized(word)
+ term = self._search_normalized(word)
if term:
copystr.add(term, 'S', word,
json.dumps({'class': cls, 'type': typ,
def add_country_names(self, country_code, names):
""" Add names for the given country to the search index.
"""
+ # Make sure any name preprocessing for country names applies.
+ info = PlaceInfo({'name': names, 'country_code': country_code,
+ 'rank_address': 4, 'class': 'boundary',
+ 'type': 'administrative'})
+ self._add_country_full_names(country_code,
+ self.sanitizer.process_names(info)[0])
+
+
+ def _add_country_full_names(self, country_code, names):
+ """ Add names for the given country from an already sanitized
+ name list.
+ """
word_tokens = set()
- for name in self._compute_full_names(names):
- norm_name = self.name_processor.get_search_normalized(name)
+ for name in names:
+ norm_name = self._search_normalized(name.name)
if norm_name:
word_tokens.add(norm_name)
def process_place(self, place):
""" Determine tokenizer information about the given place.
- Returns a JSON-serialisable structure that will be handed into
+ Returns a JSON-serializable structure that will be handed into
the database via the token_info field.
"""
token_info = _TokenInfo(self._cache)
- names = place.get('name')
+ names, address = self.sanitizer.process_names(place)
if names:
fulls, partials = self._compute_name_tokens(names)
token_info.add_names(fulls, partials)
- 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_full_names(place.country_code, names)
- address = place.get('address')
if address:
self._process_place_address(token_info, address)
def _process_place_address(self, token_info, address):
hnrs = []
addr_terms = []
- for key, value in address.items():
- if key == 'postcode':
- self._add_postcode(value)
- elif key in ('housenumber', 'streetnumber', 'conscriptionnumber'):
- hnrs.append(value)
- elif key == 'street':
- token_info.add_street(*self._compute_name_tokens({'name': value}))
- elif key == 'place':
- token_info.add_place(*self._compute_name_tokens({'name': value}))
- elif not key.startswith('_') and \
- key not in ('country', 'full'):
- addr_terms.append((key, *self._compute_name_tokens({'name': value})))
+ for item in address:
+ if item.kind == 'postcode':
+ self._add_postcode(item.name)
+ elif item.kind in ('housenumber', 'streetnumber', 'conscriptionnumber'):
+ hnrs.append(item.name)
+ elif item.kind == 'street':
+ token_info.add_street(self._compute_partial_tokens(item.name))
+ elif item.kind == 'place':
+ token_info.add_place(self._compute_partial_tokens(item.name))
+ elif not item.kind.startswith('_') and \
+ item.kind not in ('country', 'full'):
+ addr_terms.append((item.kind, self._compute_partial_tokens(item.name)))
if hnrs:
hnrs = self._split_housenumbers(hnrs)
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.
+ """
+ norm_name = self._search_normalized(name)
+
+ tokens = []
+ need_lookup = []
+ for partial in norm_name.split():
+ token = self._cache.partials.get(partial)
+ if token:
+ tokens.append(token)
+ else:
+ need_lookup.append(partial)
+
+ if need_lookup:
+ with self.conn.cursor() as cur:
+ cur.execute("""SELECT word, getorcreate_partial_word(word)
+ FROM unnest(%s) word""",
+ (need_lookup, ))
+
+ for partial, token in cur:
+ tokens.append(token)
+ self._cache.partials[partial] = token
+
+ return tokens
+
+
def _compute_name_tokens(self, names):
""" Computes the full name and partial name tokens for the given
dictionary of names.
"""
- full_names = self._compute_full_names(names)
full_tokens = set()
partial_tokens = set()
- for name in full_names:
- norm_name = self.name_processor.get_normalized(name)
- full, part = self._cache.names.get(norm_name, (None, None))
+ for name in names:
+ analyzer_id = name.get_attr('analyzer')
+ norm_name = self._normalized(name.name)
+ 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.name_processor.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)
return full_tokens, partial_tokens
- @staticmethod
- def _compute_full_names(names):
- """ Return the set of all full name word ids to be used with the
- given dictionary of names.
- """
- full_names = set()
- for name in (n.strip() for ns in names.values() for n in re.split('[;,]', ns)):
- if name:
- full_names.add(name)
-
- brace_idx = name.find('(')
- if brace_idx >= 0:
- full_names.add(name[:brace_idx].strip())
-
- return full_names
-
-
def _add_postcode(self, postcode):
""" Make sure the normalized postcode is present in the word table.
"""
postcode = self.normalize_postcode(postcode)
if postcode not in self._cache.postcodes:
- term = self.name_processor.get_search_normalized(postcode)
+ term = self._search_normalized(postcode)
if not term:
return
self.data['hnr'] = ';'.join(hnrs)
- def add_street(self, fulls, _):
+ def add_street(self, tokens):
""" Add addr:street match terms.
"""
- if fulls:
- self.data['street'] = self._mk_array(fulls)
+ if tokens:
+ self.data['street'] = self._mk_array(tokens)
- def add_place(self, fulls, partials):
+ def add_place(self, tokens):
""" Add addr:place search and match terms.
"""
- if fulls:
- self.data['place_search'] = self._mk_array(itertools.chain(fulls, partials))
- self.data['place_match'] = self._mk_array(fulls)
+ if tokens:
+ self.data['place'] = self._mk_array(tokens)
def add_address_terms(self, terms):
""" Add additional address terms.
"""
- tokens = {}
-
- for key, fulls, partials in terms:
- if fulls:
- tokens[key] = [self._mk_array(itertools.chain(fulls, partials)),
- self._mk_array(fulls)]
+ tokens = {key: self._mk_array(partials)
+ for key, partials in terms if partials}
if tokens:
self.data['addr'] = tokens
"""
def __init__(self):
self.names = {}
+ self.partials = {}
self.postcodes = set()
self.housenumbers = {}
projects / nominatim.git / blobdiff