from psycopg.types.json import Jsonb
from psycopg import sql as pysql
-from ..db.connection import connect, Connection, Cursor, server_version_tuple,\
+from ..db.connection import connect, Connection, Cursor, \
drop_tables, table_exists, execute_scalar
from ..config import Configuration
from ..db.sql_preprocessor import SQLPreprocessor
LOG = logging.getLogger()
-WORD_TYPES =(('country_names', 'C'),
- ('postcodes', 'P'),
- ('full_word', 'W'),
- ('housenumbers', 'H'))
+WORD_TYPES = (('country_names', 'C'),
+ ('postcodes', 'P'),
+ ('full_word', 'W'),
+ ('housenumbers', 'H'))
+
def create(dsn: str, data_dir: Path) -> 'ICUTokenizer':
""" Create a new instance of the tokenizer provided by this module.
self.data_dir = data_dir
self.loader: Optional[ICURuleLoader] = None
-
def init_new_db(self, config: Configuration, init_db: bool = True) -> None:
""" Set up a new tokenizer for the database.
self._setup_db_tables(config)
self._create_base_indices(config, 'word')
-
def init_from_project(self, config: Configuration) -> None:
""" Initialise the tokenizer from the project directory.
"""
with connect(self.dsn) as conn:
self.loader.load_config_from_db(conn)
-
def finalize_import(self, config: Configuration) -> None:
""" Do any required postprocessing to make the tokenizer data ready
for use.
"""
self._create_lookup_indices(config, 'word')
-
def update_sql_functions(self, config: Configuration) -> None:
""" Reimport the SQL functions for this tokenizer.
"""
sqlp = SQLPreprocessor(conn, config)
sqlp.run_sql_file(conn, 'tokenizer/icu_tokenizer.sql')
-
def check_database(self, config: Configuration) -> None:
""" Check that the tokenizer is set up correctly.
"""
# Will throw an error if there is an issue.
self.init_from_project(config)
-
def update_statistics(self, config: Configuration, threads: int = 2) -> None:
""" Recompute frequencies for all name words.
"""
cur.execute(pysql.SQL('SET max_parallel_workers_per_gather TO {}')
.format(pysql.Literal(min(threads, 6),)))
- if server_version_tuple(conn) < (12, 0):
- LOG.info('Computing word frequencies')
- drop_tables(conn, 'word_frequencies', 'addressword_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)')
- cur.execute("""CREATE TEMP TABLE addressword_frequencies AS
- SELECT unnest(nameaddress_vector) as id, count(*)
- FROM search_name GROUP BY id""")
- cur.execute('CREATE INDEX ON addressword_frequencies(id)')
- cur.execute("""CREATE OR REPLACE FUNCTION word_freq_update(wid INTEGER,
- INOUT info JSONB)
- AS $$
- DECLARE rec RECORD;
- BEGIN
- IF info is null THEN
- info = '{}'::jsonb;
- END IF;
- FOR rec IN SELECT count FROM word_frequencies WHERE id = wid
- LOOP
- info = info || jsonb_build_object('count', rec.count);
- END LOOP;
- FOR rec IN SELECT count FROM addressword_frequencies WHERE id = wid
- LOOP
- info = info || jsonb_build_object('addr_count', rec.count);
- END LOOP;
- IF info = '{}'::jsonb THEN
- info = null;
- END IF;
- END;
- $$ LANGUAGE plpgsql IMMUTABLE;
- """)
- LOG.info('Update word table with recomputed frequencies')
- drop_tables(conn, 'tmp_word')
- cur.execute("""CREATE TABLE tmp_word AS
- SELECT word_id, word_token, type, word,
- word_freq_update(word_id, info) as info
- FROM word
- """)
- drop_tables(conn, 'word_frequencies', 'addressword_frequencies')
- else:
- LOG.info('Computing word frequencies')
- drop_tables(conn, 'word_frequencies')
- cur.execute("""
- CREATE TEMP TABLE word_frequencies AS
- WITH word_freq AS MATERIALIZED (
- SELECT unnest(name_vector) as id, count(*)
- FROM search_name GROUP BY id),
- addr_freq AS MATERIALIZED (
- SELECT unnest(nameaddress_vector) as id, count(*)
- FROM search_name GROUP BY id)
- SELECT coalesce(a.id, w.id) as id,
- (CASE WHEN w.count is null THEN '{}'::JSONB
- ELSE jsonb_build_object('count', w.count) END
- ||
- CASE WHEN a.count is null THEN '{}'::JSONB
- ELSE jsonb_build_object('addr_count', a.count) END) as info
- FROM word_freq w FULL JOIN addr_freq a ON a.id = w.id;
- """)
- cur.execute('CREATE UNIQUE INDEX ON word_frequencies(id) INCLUDE(info)')
- cur.execute('ANALYSE word_frequencies')
- LOG.info('Update word table with recomputed frequencies')
- drop_tables(conn, 'tmp_word')
- cur.execute("""CREATE TABLE tmp_word AS
- SELECT word_id, word_token, type, word,
- (CASE WHEN wf.info is null THEN word.info
- ELSE coalesce(word.info, '{}'::jsonb) || wf.info
- END) as info
- FROM word LEFT JOIN word_frequencies wf
- ON word.word_id = wf.id
- ORDER BY word_id
- """)
- drop_tables(conn, 'word_frequencies')
+ LOG.info('Computing word frequencies')
+ drop_tables(conn, 'word_frequencies')
+ cur.execute("""
+ CREATE TEMP TABLE word_frequencies AS
+ WITH word_freq AS MATERIALIZED (
+ SELECT unnest(name_vector) as id, count(*)
+ FROM search_name GROUP BY id),
+ addr_freq AS MATERIALIZED (
+ SELECT unnest(nameaddress_vector) as id, count(*)
+ FROM search_name GROUP BY id)
+ SELECT coalesce(a.id, w.id) as id,
+ (CASE WHEN w.count is null THEN '{}'::JSONB
+ ELSE jsonb_build_object('count', w.count) END
+ ||
+ CASE WHEN a.count is null THEN '{}'::JSONB
+ ELSE jsonb_build_object('addr_count', a.count) END) as info
+ FROM word_freq w FULL JOIN addr_freq a ON a.id = w.id;
+ """)
+ cur.execute('CREATE UNIQUE INDEX ON word_frequencies(id) INCLUDE(info)')
+ cur.execute('ANALYSE word_frequencies')
+ LOG.info('Update word table with recomputed frequencies')
+ drop_tables(conn, 'tmp_word')
+ cur.execute("""CREATE TABLE tmp_word AS
+ SELECT word_id, word_token, type, word,
+ (CASE WHEN wf.info is null THEN word.info
+ ELSE coalesce(word.info, '{}'::jsonb) || wf.info
+ END) as info
+ FROM word LEFT JOIN word_frequencies wf
+ ON word.word_id = wf.id
+ ORDER BY word_id
+ """)
+ drop_tables(conn, 'word_frequencies')
with conn.cursor() as cur:
cur.execute('SET max_parallel_workers_per_gather TO 0')
self._create_lookup_indices(config, 'tmp_word')
self._move_temporary_word_table('tmp_word')
-
-
def _cleanup_housenumbers(self) -> None:
""" Remove unused house numbers.
"""
(list(candidates.values()), ))
conn.commit()
-
-
def update_word_tokens(self) -> None:
""" Remove unused tokens.
"""
self._cleanup_housenumbers()
LOG.warning("Tokenizer house-keeping done.")
-
def name_analyzer(self) -> 'ICUNameAnalyzer':
""" Create a new analyzer for tokenizing names and queries
using this tokinzer. Analyzers are context managers and should
return ICUNameAnalyzer(self.dsn, self.loader.make_sanitizer(),
self.loader.make_token_analysis())
-
def most_frequent_words(self, conn: Connection, num: int) -> List[str]:
""" Return a list of the `num` most frequent full words
in the database.
ORDER BY count DESC LIMIT %s""", (num,))
return list(s[0].split('@')[0] for s in cur)
-
def _save_config(self) -> None:
""" Save the configuration that needs to remain stable for the given
database as database properties.
with connect(self.dsn) as conn:
self.loader.save_config_to_db(conn)
-
def _setup_db_tables(self, config: Configuration) -> None:
""" Set up the word table and fill it with pre-computed word
frequencies.
""")
conn.commit()
-
def _create_base_indices(self, config: Configuration, table_name: str) -> None:
""" Set up the word table and fill it with pre-computed word
frequencies.
column_type=ctype)
conn.commit()
-
def _create_lookup_indices(self, config: Configuration, table_name: str) -> None:
""" Create additional indexes used when running the API.
"""
with connect(self.dsn) as conn:
sqlp = SQLPreprocessor(conn, config)
# Index required for details lookup.
- sqlp.run_string(conn, """
+ sqlp.run_string(
+ conn,
+ """
CREATE INDEX IF NOT EXISTS idx_{{table_name}}_word_id
ON {{table_name}} USING BTREE (word_id) {{db.tablespace.search_index}}
- """,
- table_name=table_name)
+ """,
+ table_name=table_name)
conn.commit()
-
def _move_temporary_word_table(self, old: str) -> None:
""" Rename all tables and indexes used by the tokenizer.
"""
conn.commit()
-
-
class ICUNameAnalyzer(AbstractAnalyzer):
""" The ICU analyzer uses the ICU library for splitting names.
self._cache = _TokenCache()
-
def close(self) -> None:
""" Free all resources used by the analyzer.
"""
self.conn.close()
self.conn = None
-
def _search_normalized(self, name: str) -> str:
""" Return the search token transliteration of the given name.
"""
return cast(str, self.token_analysis.search.transliterate(name)).strip()
-
def _normalized(self, name: str) -> str:
""" Return the normalized version of the given name with all
non-relevant information removed.
"""
return cast(str, self.token_analysis.normalizer.transliterate(name)).strip()
-
def get_word_token_info(self, words: Sequence[str]) -> List[Tuple[str, str, int]]:
""" Return token information for the given list of words.
If a word starts with # it is assumed to be a full name
part_ids = {r[0]: r[1] for r in cur}
return [(k, v, full_ids.get(v, None)) for k, v in full_tokens.items()] \
- + [(k, v, part_ids.get(v, None)) for k, v in partial_tokens.items()]
-
+ + [(k, v, part_ids.get(v, None)) for k, v in partial_tokens.items()]
def normalize_postcode(self, postcode: str) -> str:
""" Convert the postcode to a standardized form.
"""
return postcode.strip().upper()
-
def update_postcodes_from_db(self) -> None:
""" Update postcode tokens in the word table from the location_postcode
table.
with self.conn.cursor() as cur:
cur.executemany("""SELECT create_postcode_word(%s, %s)""", terms)
-
-
-
def update_special_phrases(self, phrases: Iterable[Tuple[str, str, str, str]],
should_replace: bool) -> None:
""" Replace the search index for special phrases with the new phrases.
LOG.info("Total phrases: %s. Added: %s. Deleted: %s",
len(norm_phrases), added, deleted)
-
def _add_special_phrases(self, cursor: Cursor,
new_phrases: Set[Tuple[str, str, str, str]],
existing_phrases: Set[Tuple[str, str, str, str]]) -> int:
return added
-
def _remove_special_phrases(self, cursor: Cursor,
- new_phrases: Set[Tuple[str, str, str, str]],
- existing_phrases: Set[Tuple[str, str, str, str]]) -> int:
+ new_phrases: Set[Tuple[str, str, str, str]],
+ existing_phrases: Set[Tuple[str, str, str, str]]) -> int:
""" Remove all phrases from the database that are no longer in the
new phrase list.
"""
return len(to_delete)
-
def add_country_names(self, country_code: str, names: Mapping[str, str]) -> None:
""" Add default names for the given country to the search index.
"""
self.sanitizer.process_names(info)[0],
internal=True)
-
def _add_country_full_names(self, country_code: str, names: Sequence[PlaceName],
internal: bool = False) -> None:
""" Add names for the given country from an already sanitized
"""
cur.execute(sql, (country_code, list(new_tokens)))
-
def process_place(self, place: PlaceInfo) -> Mapping[str, Any]:
""" Determine tokenizer information about the given place.
return token_info.to_dict()
-
def _process_place_address(self, token_info: '_TokenInfo',
address: Sequence[PlaceName]) -> None:
for item in address:
elif item.kind == 'place':
if not item.suffix:
token_info.add_place(itertools.chain(*self._compute_name_tokens([item])))
- elif not item.kind.startswith('_') and not item.suffix and \
- item.kind not in ('country', 'full', 'inclusion'):
+ elif (not item.kind.startswith('_') and not item.suffix and
+ item.kind not in ('country', 'full', 'inclusion')):
token_info.add_address_term(item.kind,
itertools.chain(*self._compute_name_tokens([item])))
-
def _compute_housenumber_token(self, hnr: PlaceName) -> Tuple[Optional[int], Optional[str]]:
""" Normalize the housenumber and return the word token and the
canonical form.
return result
-
def _retrieve_full_tokens(self, name: str) -> List[int]:
""" Get the full name token for the given name, if it exists.
The name is only retrieved for the standard analyser.
return full
-
def _compute_name_tokens(self, names: Sequence[PlaceName]) -> Tuple[Set[int], Set[int]]:
""" Computes the full name and partial name tokens for the given
dictionary of names.
return full_tokens, partial_tokens
-
def _add_postcode(self, item: PlaceName) -> Optional[str]:
""" Make sure the normalized postcode is present in the word table.
"""
self.address_tokens: Dict[str, str] = {}
self.postcode: Optional[str] = None
-
def _mk_array(self, tokens: Iterable[Any]) -> str:
return f"{{{','.join((str(s) for s in tokens))}}}"
-
def to_dict(self) -> Dict[str, Any]:
""" Return the token information in database importable format.
"""
return out
-
def set_names(self, fulls: Iterable[int], partials: Iterable[int]) -> None:
""" Adds token information for the normalised names.
"""
self.names = self._mk_array(itertools.chain(fulls, partials))
-
def add_housenumber(self, token: Optional[int], hnr: Optional[str]) -> None:
""" Extract housenumber information from a list of normalised
housenumbers.
self.housenumbers.add(hnr)
self.housenumber_tokens.add(token)
-
def add_street(self, tokens: Iterable[int]) -> None:
""" Add addr:street match terms.
"""
self.street_tokens = set()
self.street_tokens.update(tokens)
-
def add_place(self, tokens: Iterable[int]) -> None:
""" Add addr:place search and match terms.
"""
self.place_tokens.update(tokens)
-
def add_address_term(self, key: str, partials: Iterable[int]) -> None:
""" Add additional address terms.
"""
projects / nominatim.git / blobdiff