2 Tokenizer implementing normalisation as used before Nominatim 4 but using
3 libICU instead of the PostgreSQL module.
5 from collections import Counter
9 from textwrap import dedent
10 from pathlib import Path
12 from nominatim.db.connection import connect
13 from nominatim.db.properties import set_property, get_property
14 from nominatim.db.utils import CopyBuffer
15 from nominatim.db.sql_preprocessor import SQLPreprocessor
16 from nominatim.tokenizer.icu_rule_loader import ICURuleLoader
17 from nominatim.tokenizer.icu_name_processor import ICUNameProcessor, ICUNameProcessorRules
19 DBCFG_MAXWORDFREQ = "tokenizer_maxwordfreq"
20 DBCFG_TERM_NORMALIZATION = "tokenizer_term_normalization"
22 LOG = logging.getLogger()
24 def create(dsn, data_dir):
25 """ Create a new instance of the tokenizer provided by this module.
27 return LegacyICUTokenizer(dsn, data_dir)
30 class LegacyICUTokenizer:
31 """ This tokenizer uses libICU to covert names and queries to ASCII.
32 Otherwise it uses the same algorithms and data structures as the
33 normalization routines in Nominatim 3.
36 def __init__(self, dsn, data_dir):
38 self.data_dir = data_dir
39 self.naming_rules = None
40 self.term_normalization = None
41 self.max_word_frequency = None
44 def init_new_db(self, config, init_db=True):
45 """ Set up a new tokenizer for the database.
47 This copies all necessary data in the project directory to make
48 sure the tokenizer remains stable even over updates.
50 if config.TOKENIZER_CONFIG:
51 cfgfile = Path(config.TOKENIZER_CONFIG)
53 cfgfile = config.config_dir / 'legacy_icu_tokenizer.yaml'
55 loader = ICURuleLoader(cfgfile)
56 self.naming_rules = ICUNameProcessorRules(loader=loader)
57 self.term_normalization = config.TERM_NORMALIZATION
58 self.max_word_frequency = config.MAX_WORD_FREQUENCY
60 self._install_php(config.lib_dir.php)
61 self._save_config(config)
64 self.update_sql_functions(config)
65 self._init_db_tables(config)
68 def init_from_project(self):
69 """ Initialise the tokenizer from the project directory.
71 with connect(self.dsn) as conn:
72 self.naming_rules = ICUNameProcessorRules(conn=conn)
73 self.term_normalization = get_property(conn, DBCFG_TERM_NORMALIZATION)
74 self.max_word_frequency = get_property(conn, DBCFG_MAXWORDFREQ)
77 def finalize_import(self, config):
78 """ Do any required postprocessing to make the tokenizer data ready
81 with connect(self.dsn) as conn:
82 sqlp = SQLPreprocessor(conn, config)
83 sqlp.run_sql_file(conn, 'tokenizer/legacy_tokenizer_indices.sql')
86 def update_sql_functions(self, config):
87 """ Reimport the SQL functions for this tokenizer.
89 with connect(self.dsn) as conn:
90 max_word_freq = get_property(conn, DBCFG_MAXWORDFREQ)
91 sqlp = SQLPreprocessor(conn, config)
92 sqlp.run_sql_file(conn, 'tokenizer/legacy_icu_tokenizer.sql',
93 max_word_freq=max_word_freq)
96 def check_database(self):
97 """ Check that the tokenizer is set up correctly.
99 self.init_from_project()
101 if self.naming_rules is None:
102 return "Configuration for tokenizer 'legacy_icu' are missing."
107 def name_analyzer(self):
108 """ Create a new analyzer for tokenizing names and queries
109 using this tokinzer. Analyzers are context managers and should
113 with tokenizer.name_analyzer() as analyzer:
117 When used outside the with construct, the caller must ensure to
118 call the close() function before destructing the analyzer.
120 Analyzers are not thread-safe. You need to instantiate one per thread.
122 return LegacyICUNameAnalyzer(self.dsn, ICUNameProcessor(self.naming_rules))
124 # pylint: disable=missing-format-attribute
125 def _install_php(self, phpdir):
126 """ Install the php script for the tokenizer.
128 php_file = self.data_dir / "tokenizer.php"
129 php_file.write_text(dedent("""\
131 @define('CONST_Max_Word_Frequency', {0.max_word_frequency});
132 @define('CONST_Term_Normalization_Rules', "{0.term_normalization}");
133 @define('CONST_Transliteration', "{0.naming_rules.search_rules}");
134 require_once('{1}/tokenizer/legacy_icu_tokenizer.php');
135 """.format(self, phpdir)))
138 def _save_config(self, config):
139 """ Save the configuration that needs to remain stable for the given
140 database as database properties.
142 with connect(self.dsn) as conn:
143 self.naming_rules.save_rules(conn)
145 set_property(conn, DBCFG_MAXWORDFREQ, config.MAX_WORD_FREQUENCY)
146 set_property(conn, DBCFG_TERM_NORMALIZATION, self.term_normalization)
149 def _init_db_tables(self, config):
150 """ Set up the word table and fill it with pre-computed word
153 with connect(self.dsn) as conn:
154 sqlp = SQLPreprocessor(conn, config)
155 sqlp.run_sql_file(conn, 'tokenizer/icu_tokenizer_tables.sql')
158 LOG.warning("Precomputing word tokens")
160 # get partial words and their frequencies
162 name_proc = ICUNameProcessor(self.naming_rules)
163 with conn.cursor(name="words") as cur:
164 cur.execute(""" SELECT v, count(*) FROM
165 (SELECT svals(name) as v FROM place)x
166 WHERE length(v) < 75 GROUP BY v""")
168 for name, cnt in cur:
170 for word in name_proc.get_variants_ascii(name_proc.get_normalized(name)):
172 terms.update(word.split())
176 # copy them back into the word table
177 with CopyBuffer() as copystr:
178 for args in words.items():
181 with conn.cursor() as cur:
182 copystr.copy_out(cur, 'word',
183 columns=['word_token', 'search_name_count'])
184 cur.execute("""UPDATE word SET word_id = nextval('seq_word')
185 WHERE word_id is null""")
190 class LegacyICUNameAnalyzer:
191 """ The legacy analyzer uses the ICU library for splitting names.
193 Each instance opens a connection to the database to request the
197 def __init__(self, dsn, name_proc):
198 self.conn = connect(dsn).connection
199 self.conn.autocommit = True
200 self.name_processor = name_proc
202 self._cache = _TokenCache()
209 def __exit__(self, exc_type, exc_value, traceback):
214 """ Free all resources used by the analyzer.
221 def get_word_token_info(self, words):
222 """ Return token information for the given list of words.
223 If a word starts with # it is assumed to be a full name
224 otherwise is a partial name.
226 The function returns a list of tuples with
227 (original word, word token, word id).
229 The function is used for testing and debugging only
230 and not necessarily efficient.
234 if word.startswith('#'):
235 tokens[word] = ' ' + self.name_processor.get_search_normalized(word[1:])
237 tokens[word] = self.name_processor.get_search_normalized(word)
239 with self.conn.cursor() as cur:
240 cur.execute("""SELECT word_token, word_id
241 FROM word, (SELECT unnest(%s::TEXT[]) as term) t
242 WHERE word_token = t.term
243 and class is null and country_code is null""",
244 (list(tokens.values()), ))
245 ids = {r[0]: r[1] for r in cur}
247 return [(k, v, ids.get(v, None)) for k, v in tokens.items()]
251 def normalize_postcode(postcode):
252 """ Convert the postcode to a standardized form.
254 This function must yield exactly the same result as the SQL function
255 'token_normalized_postcode()'.
257 return postcode.strip().upper()
260 def _make_standard_hnr(self, hnr):
261 """ Create a normalised version of a housenumber.
263 This function takes minor shortcuts on transliteration.
265 return self.name_processor.get_search_normalized(hnr)
267 def update_postcodes_from_db(self):
268 """ Update postcode tokens in the word table from the location_postcode
272 with self.conn.cursor() as cur:
273 # This finds us the rows in location_postcode and word that are
274 # missing in the other table.
275 cur.execute("""SELECT * FROM
276 (SELECT pc, word FROM
277 (SELECT distinct(postcode) as pc FROM location_postcode) p
279 (SELECT info->>'postcode' as word FROM word WHERE type = 'P') w
281 WHERE pc is null or word is null""")
283 with CopyBuffer() as copystr:
284 for postcode, word in cur:
286 to_delete.append(word)
288 copystr.add(self.name_processor.get_search_normalized(postcode),
289 'P', {'postcode': postcode})
292 cur.execute("""DELETE FROM WORD
293 WHERE class ='P' and info->>'postcode' = any(%s)
296 copystr.copy_out(cur, 'word',
297 columns=['word_token', 'type', 'info'])
300 def update_special_phrases(self, phrases, should_replace):
301 """ Replace the search index for special phrases with the new phrases.
303 norm_phrases = set(((self.name_processor.get_normalized(p[0]), p[1], p[2], p[3])
306 with self.conn.cursor() as cur:
307 # Get the old phrases.
308 existing_phrases = set()
309 cur.execute("""SELECT word, class, type, operator FROM word
310 WHERE class != 'place'
311 OR (type != 'house' AND type != 'postcode')""")
312 for label, cls, typ, oper in cur:
313 existing_phrases.add((label, cls, typ, oper or '-'))
315 added = self._add_special_phrases(cur, norm_phrases, existing_phrases)
317 deleted = self._remove_special_phrases(cur, norm_phrases,
322 LOG.info("Total phrases: %s. Added: %s. Deleted: %s",
323 len(norm_phrases), added, deleted)
326 def _add_special_phrases(self, cursor, new_phrases, existing_phrases):
327 """ Add all phrases to the database that are not yet there.
329 to_add = new_phrases - existing_phrases
332 with CopyBuffer() as copystr:
333 for word, cls, typ, oper in to_add:
334 term = self.name_processor.get_search_normalized(word)
336 copystr.add(word, ' ' + term, cls, typ,
337 oper if oper in ('in', 'near') else None, 0)
340 copystr.copy_out(cursor, 'word',
341 columns=['word', 'word_token', 'class', 'type',
342 'operator', 'search_name_count'])
348 def _remove_special_phrases(cursor, new_phrases, existing_phrases):
349 """ Remove all phrases from the databse that are no longer in the
352 to_delete = existing_phrases - new_phrases
355 cursor.execute_values(
356 """ DELETE FROM word USING (VALUES %s) as v(name, in_class, in_type, op)
357 WHERE word = name and class = in_class and type = in_type
358 and ((op = '-' and operator is null) or op = operator)""",
361 return len(to_delete)
364 def add_country_names(self, country_code, names):
365 """ Add names for the given country to the search index.
368 for name in self._compute_full_names(names):
369 norm_name = self.name_processor.get_search_normalized(name)
371 word_tokens.add(norm_name)
373 with self.conn.cursor() as cur:
375 cur.execute("""SELECT word_token FROM word
376 WHERE type = 'C' and info->>'cc'= %s""",
378 word_tokens.difference_update((t[0] for t in cur))
380 # Only add those names that are not yet in the list.
382 cur.execute("""INSERT INTO word (word_token, type, info)
383 (SELECT token, 'C', json_build_object('cc', %s)
384 FROM unnest(%s) as token)
385 """, (country_code, list(word_tokens)))
387 # No names are deleted at the moment.
388 # If deletion is made possible, then the static names from the
389 # initial 'country_name' table should be kept.
392 def process_place(self, place):
393 """ Determine tokenizer information about the given place.
395 Returns a JSON-serialisable structure that will be handed into
396 the database via the token_info field.
398 token_info = _TokenInfo(self._cache)
400 names = place.get('name')
403 fulls, partials = self._compute_name_tokens(names)
405 token_info.add_names(fulls, partials)
407 country_feature = place.get('country_feature')
408 if country_feature and re.fullmatch(r'[A-Za-z][A-Za-z]', country_feature):
409 self.add_country_names(country_feature.lower(), names)
411 address = place.get('address')
413 self._process_place_address(token_info, address)
415 return token_info.data
418 def _process_place_address(self, token_info, address):
421 for key, value in address.items():
422 if key == 'postcode':
423 self._add_postcode(value)
424 elif key in ('housenumber', 'streetnumber', 'conscriptionnumber'):
426 elif key == 'street':
427 token_info.add_street(*self._compute_name_tokens({'name': value}))
429 token_info.add_place(*self._compute_name_tokens({'name': value}))
430 elif not key.startswith('_') and \
431 key not in ('country', 'full'):
432 addr_terms.append((key, *self._compute_name_tokens({'name': value})))
435 hnrs = self._split_housenumbers(hnrs)
436 token_info.add_housenumbers(self.conn, [self._make_standard_hnr(n) for n in hnrs])
439 token_info.add_address_terms(addr_terms)
442 def _compute_name_tokens(self, names):
443 """ Computes the full name and partial name tokens for the given
446 full_names = self._compute_full_names(names)
448 partial_tokens = set()
450 for name in full_names:
451 norm_name = self.name_processor.get_normalized(name)
452 full, part = self._cache.names.get(norm_name, (None, None))
454 variants = self.name_processor.get_variants_ascii(norm_name)
458 with self.conn.cursor() as cur:
459 cur.execute("SELECT (getorcreate_full_word(%s, %s)).*",
460 (norm_name, variants))
461 full, part = cur.fetchone()
463 self._cache.names[norm_name] = (full, part)
465 full_tokens.add(full)
466 partial_tokens.update(part)
468 return full_tokens, partial_tokens
472 def _compute_full_names(names):
473 """ Return the set of all full name word ids to be used with the
474 given dictionary of names.
477 for name in (n.strip() for ns in names.values() for n in re.split('[;,]', ns)):
481 brace_idx = name.find('(')
483 full_names.add(name[:brace_idx].strip())
488 def _add_postcode(self, postcode):
489 """ Make sure the normalized postcode is present in the word table.
491 if re.search(r'[:,;]', postcode) is None:
492 postcode = self.normalize_postcode(postcode)
494 if postcode not in self._cache.postcodes:
495 term = self.name_processor.get_search_normalized(postcode)
499 with self.conn.cursor() as cur:
500 # no word_id needed for postcodes
501 cur.execute("""INSERT INTO word (word_token, type, info)
502 (SELECT %s, 'P', json_build_object('postcode', pc)
503 FROM (VALUES (%s)) as v(pc)
506 WHERE type = 'P' and info->>postcode = pc))
507 """, (term, postcode))
508 self._cache.postcodes.add(postcode)
512 def _split_housenumbers(hnrs):
513 if len(hnrs) > 1 or ',' in hnrs[0] or ';' in hnrs[0]:
514 # split numbers if necessary
517 simple_list.extend((x.strip() for x in re.split(r'[;,]', hnr)))
519 if len(simple_list) > 1:
520 hnrs = list(set(simple_list))
530 """ Collect token information to be sent back to the database.
532 def __init__(self, cache):
537 def _mk_array(tokens):
538 return '{%s}' % ','.join((str(s) for s in tokens))
541 def add_names(self, fulls, partials):
542 """ Adds token information for the normalised names.
544 self.data['names'] = self._mk_array(itertools.chain(fulls, partials))
547 def add_housenumbers(self, conn, hnrs):
548 """ Extract housenumber information from a list of normalised
551 self.data['hnr_tokens'] = self._mk_array(self._cache.get_hnr_tokens(conn, hnrs))
552 self.data['hnr'] = ';'.join(hnrs)
555 def add_street(self, fulls, _):
556 """ Add addr:street match terms.
559 self.data['street'] = self._mk_array(fulls)
562 def add_place(self, fulls, partials):
563 """ Add addr:place search and match terms.
566 self.data['place_search'] = self._mk_array(itertools.chain(fulls, partials))
567 self.data['place_match'] = self._mk_array(fulls)
570 def add_address_terms(self, terms):
571 """ Add additional address terms.
575 for key, fulls, partials in terms:
577 tokens[key] = [self._mk_array(itertools.chain(fulls, partials)),
578 self._mk_array(fulls)]
581 self.data['addr'] = tokens
585 """ Cache for token information to avoid repeated database queries.
587 This cache is not thread-safe and needs to be instantiated per
592 self.postcodes = set()
593 self.housenumbers = {}
596 def get_hnr_tokens(self, conn, terms):
597 """ Get token ids for a list of housenumbers, looking them up in the
598 database if necessary. `terms` is an iterable of normalized
605 token = self.housenumbers.get(term)
612 with conn.cursor() as cur:
613 cur.execute("SELECT nr, getorcreate_hnr_id(nr) FROM unnest(%s) as nr",
615 for term, tid in cur:
616 self.housenumbers[term] = tid