918 lines
42 KiB
Python
918 lines
42 KiB
Python
# -*- coding: utf-8 -*-
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import os
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import random
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import six
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import sys
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import yaml
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from collections import OrderedDict
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from six import itertools
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this_dir = os.path.realpath(os.path.dirname(__file__))
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sys.path.append(os.path.realpath(os.path.join(os.pardir, os.pardir)))
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from geodata.address_expansions.gazetteers import *
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from geodata.address_expansions.abbreviations import abbreviate
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from geodata.address_formatting.aliases import Aliases
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from geodata.address_formatting.formatter import AddressFormatter
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from geodata.addresses.config import address_config
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from geodata.addresses.components import AddressComponents
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from geodata.categories.config import category_config
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from geodata.categories.query import Category, NULL_CATEGORY_QUERY
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from geodata.chains.query import Chain, NULL_CHAIN_QUERY
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from geodata.coordinates.conversion import *
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from geodata.configs.utils import nested_get
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from geodata.countries.country_names import *
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from geodata.language_id.disambiguation import *
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from geodata.language_id.sample import INTERNET_LANGUAGE_DISTRIBUTION
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from geodata.i18n.languages import *
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from geodata.intersections.query import Intersection, IntersectionQuery
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from geodata.address_formatting.formatter import AddressFormatter
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from geodata.osm.components import osm_address_components
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from geodata.osm.extract import *
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from geodata.osm.intersections import OSMIntersectionReader
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from geodata.polygons.language_polys import *
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from geodata.polygons.reverse_geocode import *
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from geodata.i18n.unicode_paths import DATA_DIR
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from geodata.text.utils import is_numeric
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from geodata.csv_utils import *
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from geodata.file_utils import *
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OSM_PARSER_DATA_DEFAULT_CONFIG = os.path.join(this_dir, os.pardir, os.pardir, os.pardir,
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'resources', 'parser', 'data_sets', 'osm.yaml')
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FORMATTED_ADDRESS_DATA_TAGGED_FILENAME = 'formatted_addresses_tagged.tsv'
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FORMATTED_ADDRESS_DATA_FILENAME = 'formatted_addresses.tsv'
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FORMATTED_ADDRESS_DATA_LANGUAGE_FILENAME = 'formatted_addresses_by_language.tsv'
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FORMATTED_PLACE_DATA_TAGGED_FILENAME = 'formatted_places_tagged.tsv'
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FORMATTED_PLACE_DATA_FILENAME = 'formatted_places.tsv'
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INTERSECTIONS_FILENAME = 'intersections.tsv'
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INTERSECTIONS_TAGGED_FILENAME = 'intersections_tagged.tsv'
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ALL_LANGUAGES = 'all'
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JAPAN = 'jp'
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JAPANESE = 'ja'
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JAPANESE_ROMAJI = 'ja_rm'
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class OSMAddressFormatter(object):
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aliases = Aliases(
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OrderedDict([
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('name', AddressFormatter.HOUSE),
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('addr:housename', AddressFormatter.HOUSE),
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('addr:housenumber', AddressFormatter.HOUSE_NUMBER),
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('addr:house_number', AddressFormatter.HOUSE_NUMBER),
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('addr:street', AddressFormatter.ROAD),
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('addr:place', AddressFormatter.ROAD),
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('addr:suburb', AddressFormatter.SUBURB),
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('is_in:suburb', AddressFormatter.SUBURB),
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('addr:neighbourhood', AddressFormatter.SUBURB),
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('is_in:neighbourhood', AddressFormatter.SUBURB),
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('addr:neighborhood', AddressFormatter.SUBURB),
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('is_in:neighborhood', AddressFormatter.SUBURB),
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('addr:barangay', AddressFormatter.SUBURB),
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# Used in the UK for civil parishes, sometimes others
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('addr:locality', AddressFormatter.SUBURB),
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# This is actually used for suburb
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('suburb', AddressFormatter.SUBURB),
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('addr:city', AddressFormatter.CITY),
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('is_in:city', AddressFormatter.CITY),
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('addr:locality', AddressFormatter.CITY),
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('is_in:locality', AddressFormatter.CITY),
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('addr:municipality', AddressFormatter.CITY),
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('is_in:municipality', AddressFormatter.CITY),
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('addr:hamlet', AddressFormatter.CITY),
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('is_in:hamlet', AddressFormatter.CITY),
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('addr:quarter', AddressFormatter.CITY_DISTRICT),
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('addr:county', AddressFormatter.STATE_DISTRICT),
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('addr:district', AddressFormatter.STATE_DISTRICT),
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('is_in:district', AddressFormatter.STATE_DISTRICT),
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('addr:state', AddressFormatter.STATE),
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('is_in:state', AddressFormatter.STATE),
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('addr:province', AddressFormatter.STATE),
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('is_in:province', AddressFormatter.STATE),
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('addr:region', AddressFormatter.STATE),
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('is_in:region', AddressFormatter.STATE),
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# Used in Tunisia
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('addr:governorate', AddressFormatter.STATE),
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('addr:postal_code', AddressFormatter.POSTCODE),
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('addr:postcode', AddressFormatter.POSTCODE),
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('addr:zipcode', AddressFormatter.POSTCODE),
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('addr:country', AddressFormatter.COUNTRY),
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('addr:country_code', AddressFormatter.COUNTRY),
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('country_code', AddressFormatter.COUNTRY),
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('is_in:country_code', AddressFormatter.COUNTRY),
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('is_in:country', AddressFormatter.COUNTRY),
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])
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)
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sub_building_aliases = Aliases(
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OrderedDict([
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('level', AddressFormatter.LEVEL),
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('addr:floor', AddressFormatter.LEVEL),
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('addr:unit', AddressFormatter.UNIT),
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('addr:flats', AddressFormatter.UNIT),
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('addr:door', AddressFormatter.UNIT),
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('addr:suite', AddressFormatter.UNIT),
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])
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)
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zones = {
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'landuse': {
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'retail': AddressComponents.zones.COMMERCIAL,
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'commercial': AddressComponents.zones.COMMERCIAL,
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'industrial': AddressComponents.zones.INDUSTRIAL,
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'residential': AddressComponents.zones.RESIDENTIAL,
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},
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'amenity': {
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'university': AddressComponents.zones.UNIVERSITY,
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'college': AddressComponents.zones.UNIVERSITY,
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}
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}
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boundary_component_priorities = {k: i for i, k in enumerate(AddressFormatter.BOUNDARY_COMPONENTS_ORDERED)}
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def __init__(self, components, subdivisions_rtree=None, buildings_rtree=None):
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# Instance of AddressComponents, contains structures for reverse geocoding, etc.
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self.components = components
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self.language_rtree = components.language_rtree
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self.subdivisions_rtree = subdivisions_rtree
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self.buildings_rtree = buildings_rtree
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self.config = yaml.load(open(OSM_PARSER_DATA_DEFAULT_CONFIG))
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self.formatter = AddressFormatter()
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def namespaced_language(self, tags, candidate_languages):
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language = None
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pick_namespaced_language_prob = float(nested_get(self.config, ('languages', 'pick_namespaced_language_probability')))
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if len(candidate_languages) > 1:
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street = tags.get('addr:street', None)
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namespaced = [l['lang'] for l in candidate_languages if 'addr:street:{}'.format(l['lang']) in tags]
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if namespaced and random.random() < pick_namespaced_language_prob:
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language = random.choice(namespaced)
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lang_suffix = ':{}'.format(language)
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for k in tags:
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if k.endswith(lang_suffix):
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tags[k.rstrip(lang_suffix)] = tags[k]
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return language
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def normalize_address_components(self, tags):
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address_components = {k: v for k, v in six.iteritems(tags) if self.aliases.get(k)}
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self.aliases.replace(address_components)
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address_components = {k: v for k, v in six.iteritems(address_components) if k in AddressFormatter.address_formatter_fields}
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return address_components
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def normalize_sub_building_components(self, tags):
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sub_building_components = {k: v for k, v in six.iteritems(tags) if self.sub_building_aliases.get(k) and is_numeric(v)}
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self.aliases.replace(sub_building_components)
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sub_building_components = {k: v for k, v in six.iteritems(sub_building_components) if k in AddressFormatter.address_formatter_fields}
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return sub_building_components
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def subdivision_components(self, latitude, longitude):
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return self.subdivisions_rtree.point_in_poly(latitude, longitude, return_all=True)
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def zone(self, subdivisions):
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for subdiv in subdivisions:
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for k, v in six.iteritems(self.zones):
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zone = v.get(subdiv.get(k))
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if zone:
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return zone
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return None
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def building_components(self, latitude, longitude):
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return self.buildings_rtree.point_in_poly(latitude, longitude, return_all=True)
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def num_floors(self, buildings, key='building:levels'):
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max_floors = None
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for b in buildings:
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num_floors = b.get(key)
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if num_floors is not None:
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try:
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num_floors = int(num_floors)
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except (ValueError, TypeError):
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try:
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num_floors = int(float(num_floors))
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except (ValueError, TypeError):
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continue
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if max_floors is None or num_floors > max_floors:
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max_floors = num_floors
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return max_floors
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def abbreviated_street(self, street, language):
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'''
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Street abbreviations
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--------------------
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Use street and unit type dictionaries to probabilistically abbreviate
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phrases. Because the abbreviation is picked at random, this should
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help bridge the gap between OSM addresses and user input, in addition
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to capturing some non-standard abbreviations/surface forms which may be
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missing or sparse in OSM.
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'''
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abbreviate_prob = float(nested_get(self.config, ('streets', 'abbreviate_probability'), default=0.0))
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separate_prob = float(nested_get(self.config, ('streets', 'separate_probability'), default=0.0))
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return abbreviate(street_and_synonyms_gazetteer, street, language,
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abbreviate_prob=abbreviate_prob, separate_prob=separate_prob)
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def abbreviated_venue_name(self, name, language):
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'''
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Venue abbreviations
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-------------------
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Use street and unit type dictionaries to probabilistically abbreviate
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phrases. Because the abbreviation is picked at random, this should
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help bridge the gap between OSM addresses and user input, in addition
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to capturing some non-standard abbreviations/surface forms which may be
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missing or sparse in OSM.
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'''
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abbreviate_prob = float(nested_get(self.config, ('venues', 'abbreviate_probability'), default=0.0))
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separate_prob = float(nested_get(self.config, ('venues', 'separate_probability'), default=0.0))
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return abbreviate(names_gazetteer, name, language,
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abbreviate_prob=abbreviate_prob, separate_prob=separate_prob)
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def combine_street_name(self, props):
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'''
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Combine street names
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--------------------
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In the UK sometimes streets have "parent" streets and
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both will be listed in the address.
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Example: http://www.openstreetmap.org/node/2933503941
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'''
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# In the UK there's sometimes the notion of parent and dependent streets
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if 'addr:parentstreet' not in props or 'addr:street' not in props:
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return False
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street = safe_decode(props['addr:street'])
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parent_street = props.pop('addr:parentstreet', None)
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if parent_street:
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props['addr:street'] = six.u(', ').join([street, safe_decode(parent_street)])
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return True
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return False
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def combine_japanese_house_number(self, address_components, language):
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'''
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Japanese house numbers
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----------------------
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Addresses in Japan are pretty unique.
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There are no street names in most of the country, and so buildings
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are addressed by the following:
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1. the neighborhood (丁目 or chōme), usually numberic e.g. 4-chōme
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2. the block number (OSM uses addr:block_number for this)
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3. the house number
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Sometimes only the block number and house number are abbreviated.
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For libpostal, we want to parse:
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2丁目3-5 as {'suburb': '2丁目', 'house_number': '3-5'}
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and the abbreviated "2-3-5" as simply house_number and leave
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it up to the end user to split up that number or not.
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At this stage we're still working with the original OSM tags,
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so only combine addr_block_number with addr:housenumber
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See: https://en.wikipedia.org/wiki/Japanese_addressing_system
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'''
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house_number = address_components.get('addr:housenumber')
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if not house_number or not house_number.isdigit():
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return
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block = address_components.get('addr:block_number')
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if not block or not block.isdigit():
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return
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separator = six.u('-')
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combine_probability = float(nested_get(self.config, ('countries', 'jp', 'combine_block_house_number_probability'), default=0.0))
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if random.random() < combine_probability:
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if random.random() < float(nested_get(self.config, ('countries', 'jp', 'block_phrase_probability'), default=0.0)):
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block = Block.phrase(language, block_number)
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house_number = HouseNumber.phrase(house_number, language)
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if block is None or house_number is None:
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return
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separator = six.u(' ') if language == JAPANESE_ROMAJI else six.u('')
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house_number = separator.join([block, house_number])
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address_components['addr:housenumber'] = house_number
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def venue_names(self, props, languages):
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'''
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Venue names
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-----------
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Some venues have multiple names listed in OSM, grab them all
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With a certain probability, add None to the list so we drop the name
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'''
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return self.components.all_names(props, languages, keys=('name', 'alt_name', 'loc_name', 'int_name', 'old_name'))
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def formatted_addresses_with_venue_names(self, address_components, venue_names, country, language=None, tag_components=True, minimal_only=False):
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# Since venue names are only one-per-record, this wrapper will try them all (name, alt_name, etc.)
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formatted_addresses = []
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if AddressFormatter.HOUSE not in address_components or not venue_names:
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return [self.formatter.format_address(address_components, country, language=language,
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tag_components=tag_components, minimal_only=minimal_only)]
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address_prob = float(nested_get(self.config, ('venues', 'address_probability'), default=0.0))
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if random.random() < address_prob:
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address_components.pop(AddressFormatter.HOUSE)
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formatted_address = self.formatter.format_address(address_components, country, language=language,
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tag_components=tag_components, minimal_only=minimal_only)
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formatted_addresses.append(formatted_address)
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for venue_name in venue_names:
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if venue_name:
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address_components[AddressFormatter.HOUSE] = venue_name
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formatted_address = self.formatter.format_address(address_components, country, language=language,
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tag_components=tag_components, minimal_only=minimal_only)
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formatted_addresses.append(formatted_address)
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return formatted_addresses
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def formatted_places(self, address_components, country, language, tag_components=True):
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formatted_addresses = []
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place_components = self.components.drop_address(address_components)
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formatted_address = self.formatter.format_address(place_components, country, language=language,
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tag_components=tag_components, minimal_only=False)
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formatted_addresses.append(formatted_address)
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if AddressFormatter.POSTCODE in address_components:
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drop_postcode_prob = float(nested_get(self.config, ('places', 'drop_postcode_probability'), default=0.0))
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if random.random() < drop_postcode_prob:
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place_components = self.components.drop_postcode(place_components)
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formatted_address = self.formatter.format_address(place_components, country, language=language,
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tag_components=tag_components, minimal_only=False)
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formatted_addresses.append(formatted_address)
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return formatted_addresses
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def node_place_tags(self, tags):
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try:
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latitude, longitude = latlon_to_decimal(tags['lat'], tags['lon'])
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except Exception:
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return None, None, None
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osm_components = self.components.osm_reverse_geocoded_components(latitude, longitude)
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country, candidate_languages, language_props = self.language_rtree.country_and_languages(latitude, longitude)
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if country and candidate_languages:
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local_languages = [(l['lang'], bool(int(l['default']))) for l in candidate_languages]
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else:
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for c in reversed(osm_components):
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country = c.get('ISO3166-1:alpha2')
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if country:
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country = country.lower()
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break
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else:
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return None, None, None
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local_languages = [(lang, bool(int(default))) for lang, default in get_country_languages(country).iteritems()]
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all_local_languages = set([l for l, d in local_languages])
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random_languages = set(INTERNET_LANGUAGE_DISTRIBUTION)
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more_than_one_official_language = len([lang for lang, default in local_languages if default]) > 1
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containing_ids = [(b['type'], b['id']) for b in osm_components]
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component_name = osm_address_components.component_from_properties(country, tags, containing=containing_ids)
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component_index = self.boundary_component_priorities.get(component_name)
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if component_index:
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osm_components = [c for i, c in enumerate(osm_components)
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if self.boundary_component_priorities.get(osm_address_components.component_from_properties(country, c, containing=containing_ids[i + 1:]), -1) >= component_index and
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(c['type'], c['id']) != (tags['type'], tags['id'])]
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# Do addr:postcode, postcode, postal_code, etc.
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revised_tags = self.normalize_address_components(tags)
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place_tags = []
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postal_code = revised_tags.get(AddressFormatter.POSTCODE, None)
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postal_codes = []
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if postal_code:
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postal_codes = parse_osm_number_range(postal_code, parse_letter_range=False)
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try:
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population = int(tags.get('population', 0))
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except (ValueError, TypeError):
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population = 0
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num_references = population / 10000 + 1
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for name_tag in ('name', 'alt_name', 'loc_name', 'short_name'):
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if more_than_one_official_language:
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name = tags.get(name_tag)
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language_suffix = ''
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if name and name.strip():
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address_components = {component_name: name.strip()}
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self.components.add_admin_boundaries(address_components, osm_components, country, language,
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language_suffix=language_suffix)
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self.components.normalize_place_names(address_components, osm_components, country=country, languages=all_local_languages)
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place_tags.append((address_components, None, True))
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for language, is_default in local_languages:
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if is_default and not more_than_one_official_language:
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language_suffix = ''
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name = tags.get(name_tag)
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else:
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language_suffix = ':{}'.format(language)
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name = tags.get('{}{}'.format(name_tag, language_suffix))
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if not name or not name.strip():
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continue
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address_components = {component_name: name.strip()}
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self.components.add_admin_boundaries(address_components, osm_components, country, language,
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language_suffix=language_suffix)
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self.components.normalize_place_names(address_components, osm_components, country=country, languages=all_local_languages)
|
|
|
|
place_tags.append((address_components, language, is_default))
|
|
|
|
for language in random_languages - all_local_languages:
|
|
language_suffix = ':{}'.format(language)
|
|
name = tags.get('{}{}'.format(name_tag, language_suffix))
|
|
if not name or not name.strip():
|
|
continue
|
|
|
|
address_components = {component_name: name.strip()}
|
|
self.components.add_admin_boundaries(address_components, osm_components, country, language,
|
|
non_local_language=language,
|
|
language_suffix=language_suffix)
|
|
|
|
self.components.normalize_place_names(address_components, osm_components, country=country, languages=set([language]))
|
|
place_tags.append((address_components, language, False))
|
|
|
|
if postal_codes:
|
|
for address_components in place_tags:
|
|
address_components[AddressFormatter.POSTCODE] = random.choice(postal_codes)
|
|
|
|
return place_tags, num_references, country
|
|
|
|
def category_queries(self, tags, address_components, language, country=None, tag_components=True):
|
|
formatted_addresses = []
|
|
possible_category_keys = category_config.has_keys(language, tags)
|
|
|
|
plural_prob = float(nested_get(self.config, ('categories', 'plural_probability'), default=0.0))
|
|
place_only_prob = float(nested_get(self.config, ('categories', 'place_only_probability'), default=0.0))
|
|
|
|
for key in possible_category_keys:
|
|
value = tags[key]
|
|
phrase = Category.phrase(language, key, value, country=country, is_plural=random.random() < plural_prob)
|
|
if phrase is not NULL_CATEGORY_QUERY:
|
|
if phrase.add_place_name or phrase.add_address:
|
|
address_components = self.components.drop_names(address_components)
|
|
|
|
if phrase.add_place_name and random.random() < place_only_prob:
|
|
address_components = self.components.drop_address(address_components)
|
|
|
|
formatted_address = self.formatter.format_category_query(phrase, address_components, country, language, tag_components=tag_components)
|
|
if formatted_address:
|
|
formatted_addresses.append(formatted_address)
|
|
return formatted_addresses
|
|
|
|
def chain_queries(self, venue_name, address_components, language, country=None, tag_components=True):
|
|
'''
|
|
Chain queries
|
|
-------------
|
|
|
|
Generates strings like "Duane Reades in Brooklyn NY"
|
|
'''
|
|
is_chain, phrases = Chain.extract(venue_name)
|
|
formatted_addresses = []
|
|
|
|
if is_chain:
|
|
sample_probability = float(nested_get(self.config, ('chains', 'sample_probability'), default=0.0))
|
|
place_only_prob = float(nested_get(self.config, ('chains', 'place_only_probability'), default=0.0))
|
|
|
|
for t, c, l, vals in phrases:
|
|
for d in vals:
|
|
lang, dictionary, is_canonical, canonical = safe_decode(d).split(six.u('|'))
|
|
name = canonical
|
|
if random.random() < sample_probability:
|
|
names = address_config.sample_phrases.get((language, dictionary), {}).get(canonical, [])
|
|
if not names:
|
|
names = address_config.sample_phrases.get((ALL_LANGUAGES, dictionary), {}).get(canonical, [])
|
|
if names:
|
|
name = random.choice(names)
|
|
phrase = Chain.phrase(name, language, country)
|
|
if phrase is not NULL_CHAIN_QUERY:
|
|
if phrase.add_place_name or phrase.add_address:
|
|
address_components = self.components.drop_names(address_components)
|
|
|
|
if phrase.add_place_name and random.random() < place_only_prob:
|
|
address_components = self.components.drop_address(address_components)
|
|
|
|
formatted_address = self.formatter.format_chain_query(phrase, address_components, country, language, tag_components=tag_components)
|
|
if formatted_address:
|
|
formatted_addresses.append(formatted_address)
|
|
return formatted_addresses
|
|
|
|
def formatted_addresses(self, tags, tag_components=True):
|
|
'''
|
|
Formatted addresses
|
|
-------------------
|
|
|
|
Produces one or more formatted addresses (tagged/untagged)
|
|
from the given dictionary of OSM tags and values.
|
|
|
|
Here we also apply component dropout meaning we produce several
|
|
different addresses with various components removed at random.
|
|
That way the parser will have many examples of queries that are
|
|
just city/state or just house_number/street. The selected
|
|
components still have to make sense i.e. a lone house_number will
|
|
not be used without a street name. The dependencies are listed
|
|
above, see: OSM_ADDRESS_COMPONENTS.
|
|
|
|
If there is more than one venue name (say name and alt_name),
|
|
addresses using both names and the selected components are
|
|
returned.
|
|
'''
|
|
|
|
try:
|
|
latitude, longitude = latlon_to_decimal(tags['lat'], tags['lon'])
|
|
except Exception:
|
|
return None, None, None
|
|
|
|
country, candidate_languages, language_props = self.language_rtree.country_and_languages(latitude, longitude)
|
|
if not (country and candidate_languages):
|
|
return None, None, None
|
|
|
|
combined_street = self.combine_street_name(tags)
|
|
|
|
namespaced_language = self.namespaced_language(tags, candidate_languages)
|
|
language = None
|
|
|
|
if country == JAPAN:
|
|
language = JAPANESE
|
|
if random.random() < float(nested_get(self.config, ('countries', 'jp', 'romaji_probability'), default=0.0)):
|
|
language = JAPANESE_ROMAJI
|
|
self.combine_japanese_house_number(tags, language)
|
|
|
|
revised_tags = self.normalize_address_components(tags)
|
|
sub_building_tags = self.normalize_sub_building_components(tags)
|
|
revised_tags.update(sub_building_tags)
|
|
|
|
num_floors = None
|
|
num_basements = None
|
|
zone = None
|
|
|
|
building_components = self.building_components(latitude, longitude)
|
|
if building_components:
|
|
num_floors = self.num_floors(building_components)
|
|
num_basements = self.num_floors(building_components, key='building:levels:underground')
|
|
|
|
building_tags = self.normalize_address_components(building_components)
|
|
|
|
for k, v in six.iteritems(building_tags):
|
|
if k not in revised_tags and k in (AddressFormatter.HOUSE_NUMBER, AddressFormatter.ROAD, AddressFormatter.HOUSE):
|
|
revised_tags[k] = v
|
|
|
|
subdivision_components = self.subdivision_components(latitude, longitude)
|
|
if subdivision_components:
|
|
zone = self.zone(subdivision_components)
|
|
|
|
add_sub_building_components = AddressFormatter.HOUSE_NUMBER in revised_tags
|
|
|
|
address_components, country, language = self.components.expanded(revised_tags, latitude, longitude, language=namespaced_language,
|
|
num_floors=num_floors, num_basements=num_basements,
|
|
zone=zone, add_sub_building_components=add_sub_building_components)
|
|
|
|
if not address_components:
|
|
return None, None, None
|
|
|
|
languages = country_languages[country].keys()
|
|
venue_names = self.venue_names(tags, languages) or []
|
|
|
|
# Abbreviate the street name with random probability
|
|
street_name = address_components.get(AddressFormatter.ROAD)
|
|
|
|
if street_name:
|
|
address_components[AddressFormatter.ROAD] = self.abbreviated_street(street_name, language)
|
|
|
|
# Ditto for venue names
|
|
for venue_name in venue_names:
|
|
abbreviated_venue = self.abbreviated_venue_name(venue_name, language)
|
|
if abbreviated_venue != venue_name and abbreviated_venue not in set(venue_names):
|
|
venue_names.append(abbreviated_venue)
|
|
|
|
formatted_addresses = self.formatted_addresses_with_venue_names(address_components, venue_names, country, language=language,
|
|
tag_components=tag_components, minimal_only=not tag_components)
|
|
|
|
formatted_addresses.extend(self.formatted_places(address_components, country, language))
|
|
|
|
# Generate a PO Box address at random (only returns non-None values occasionally) and add it to the list
|
|
po_box_components = self.components.po_box_address(address_components, language, country=country)
|
|
if po_box_components:
|
|
formatted_addresses.extend(self.formatted_addresses_with_venue_names(po_box_components, venue_names, country, language=language,
|
|
tag_components=tag_components, minimal_only=False))
|
|
|
|
formatted_addresses.extend(self.category_queries(tags, address_components, language, country, tag_components=tag_components))
|
|
|
|
venue_name = tags.get('name')
|
|
if venue_name:
|
|
formatted_addresses.extend(self.chain_queries(venue_name, address_components, language, country, tag_components=tag_components))
|
|
|
|
if tag_components:
|
|
|
|
if not address_components:
|
|
return []
|
|
|
|
# Pick a random dropout order
|
|
dropout_order = self.components.address_level_dropout_order(address_components)
|
|
|
|
for component in dropout_order:
|
|
address_components.pop(component, None)
|
|
formatted_addresses.extend(self.formatted_addresses_with_venue_names(address_components, venue_names, country, language=language,
|
|
tag_components=tag_components, minimal_only=False))
|
|
|
|
return OrderedDict.fromkeys(formatted_addresses).keys(), country, language
|
|
|
|
def formatted_address_limited(self, tags):
|
|
try:
|
|
latitude, longitude = latlon_to_decimal(tags['lat'], tags['lon'])
|
|
except Exception:
|
|
return None, None, None
|
|
|
|
country, candidate_languages, language_props = self.language_rtree.country_and_languages(latitude, longitude)
|
|
if not (country and candidate_languages):
|
|
return None, None, None
|
|
|
|
namespaced_language = self.namespaced_language(tags, candidate_languages)
|
|
|
|
revised_tags = self.normalize_address_components(tags)
|
|
|
|
admin_dropout_prob = float(nested_get(self.config, ('limited', 'admin_dropout_prob'), default=0.0))
|
|
|
|
address_components, country, language = self.components.limited(revised_tags, latitude, longitude, language=namespaced_language)
|
|
|
|
if not address_components:
|
|
return None, None, None
|
|
|
|
address_components = {k: v for k, v in address_components.iteritems() if k in OSM_ADDRESS_COMPONENT_VALUES}
|
|
if not address_components:
|
|
return []
|
|
|
|
for component in (AddressFormatter.COUNTRY, AddressFormatter.STATE,
|
|
AddressFormatter.STATE_DISTRICT, AddressFormatter.CITY,
|
|
AddressFormatter.CITY_DISTRICT, AddressFormatter.SUBURB):
|
|
if random.random() < admin_dropout_prob:
|
|
_ = address_components.pop(component, None)
|
|
|
|
if not address_components:
|
|
return None, None, None
|
|
|
|
# Version with all components
|
|
formatted_address = self.formatter.format_address(address_components, country, language, tag_components=False, minimal_only=False)
|
|
|
|
return formatted_address, country, language
|
|
|
|
def build_training_data(self, infile, out_dir, tag_components=True):
|
|
'''
|
|
Creates formatted address training data for supervised sequence labeling (or potentially
|
|
for unsupervised learning e.g. for word vectors) using addr:* tags in OSM.
|
|
|
|
Example:
|
|
|
|
cs cz Gorkého/road ev.2459/house_number | 40004/postcode Trmice/city | CZ/country
|
|
|
|
The field structure is similar to other training data created by this script i.e.
|
|
{language, country, data}. The data field here is a sequence of labeled tokens similar
|
|
to what we might see in part-of-speech tagging.
|
|
|
|
|
|
This format uses a special character "|" to denote possible breaks in the input (comma, newline).
|
|
|
|
Note that for the address parser, we'd like it to be robust to many different types
|
|
of input, so we may selectively eleminate components
|
|
|
|
This information can potentially be used downstream by the sequence model as these
|
|
breaks may be present at prediction time.
|
|
|
|
Example:
|
|
|
|
sr rs Crkva Svetog Arhangela Mihaila | Vukov put BB | 15303 Trsic
|
|
|
|
This may be useful in learning word representations, statistical phrases, morphology
|
|
or other models requiring only the sequence of words.
|
|
'''
|
|
i = 0
|
|
|
|
if tag_components:
|
|
formatted_tagged_file = open(os.path.join(out_dir, FORMATTED_ADDRESS_DATA_TAGGED_FILENAME), 'w')
|
|
writer = csv.writer(formatted_tagged_file, 'tsv_no_quote')
|
|
else:
|
|
formatted_file = open(os.path.join(out_dir, FORMATTED_ADDRESS_DATA_FILENAME), 'w')
|
|
writer = csv.writer(formatted_file, 'tsv_no_quote')
|
|
|
|
for node_id, value, deps in parse_osm(infile):
|
|
formatted_addresses, country, language = self.formatted_addresses(value, tag_components=tag_components)
|
|
if not formatted_addresses:
|
|
continue
|
|
|
|
for formatted_address in formatted_addresses:
|
|
if formatted_address and formatted_address.strip():
|
|
formatted_address = tsv_string(formatted_address)
|
|
if not formatted_address or not formatted_address.strip():
|
|
continue
|
|
|
|
if tag_components:
|
|
row = (language, country, formatted_address)
|
|
else:
|
|
row = (formatted_address,)
|
|
|
|
writer.writerow(row)
|
|
|
|
i += 1
|
|
if i % 1000 == 0 and i > 0:
|
|
print('did {} formatted addresses'.format(i))
|
|
|
|
def build_place_training_data(self, infile, out_dir, tag_components=True):
|
|
i = 0
|
|
|
|
if tag_components:
|
|
formatted_tagged_file = open(os.path.join(out_dir, FORMATTED_PLACE_DATA_TAGGED_FILENAME), 'w')
|
|
writer = csv.writer(formatted_tagged_file, 'tsv_no_quote')
|
|
else:
|
|
formatted_tagged_file = open(os.path.join(out_dir, FORMATTED_PLACE_DATA_FILENAME), 'w')
|
|
writer = csv.writer(formatted_file, 'tsv_no_quote')
|
|
|
|
for node_id, tags, deps in parse_osm(infile):
|
|
place_tags, num_references, country = self.node_place_tags(tags)
|
|
for address_components, language, is_default in place_tags:
|
|
addresses = self.formatted_places(address_components, country, language)
|
|
if language is None:
|
|
language = UNKNOWN_LANGUAGE
|
|
|
|
for address in addresses:
|
|
if not address or not address.strip():
|
|
continue
|
|
|
|
address = tsv_string(address)
|
|
if tag_components:
|
|
row = (language, country, address)
|
|
else:
|
|
row = (address, )
|
|
|
|
for j in xrange(num_references if is_default else 1):
|
|
writer.writerow(row)
|
|
|
|
i += 1
|
|
if i % 1000 == 0 and i > 0:
|
|
print('did {} formatted places'.format(i))
|
|
|
|
def build_intersections_training_data(self, infile, out_dir, way_db_dir, tag_components=True):
|
|
'''
|
|
Intersection addresses like "4th & Main Street" are represented in OSM
|
|
by ways that share at least one node.
|
|
|
|
This creates formatted strings using the name of each way (sometimes the base name
|
|
for US addresses thanks to Tiger tags).
|
|
|
|
Example:
|
|
|
|
en us 34th/road Street/road &/intersection 8th/road Ave/road
|
|
'''
|
|
i = 0
|
|
|
|
if tag_components:
|
|
formatted_tagged_file = open(os.path.join(out_dir, INTERSECTIONS_TAGGED_FILENAME), 'w')
|
|
writer = csv.writer(formatted_tagged_file, 'tsv_no_quote')
|
|
else:
|
|
formatted_file = open(os.path.join(out_dir, INTERSECTIONS_FILENAME), 'w')
|
|
writer = csv.writer(formatted_file, 'tsv_no_quote')
|
|
|
|
all_name_tags = set(OSM_NAME_TAGS)
|
|
all_base_name_tags = set(OSM_BASE_NAME_TAGS)
|
|
|
|
replace_with_base_name_prob = float(nested_get(self.config, ('intersections', 'replace_with_base_name_probability'), default=0.0))
|
|
|
|
reader = OSMIntersectionReader(infile, way_db_dir)
|
|
for node_id, latitude, longitude, ways in reader.intersections():
|
|
if not ways or len(ways) < 2:
|
|
continue
|
|
|
|
tags = ways[0]
|
|
namespaced_language = None
|
|
|
|
language_components = {}
|
|
|
|
base_name_tags = [t for t in all_base_name_tags if t in tags]
|
|
if not base_name_tags:
|
|
base_name_tag = None
|
|
else:
|
|
base_name_tag = base_name_tags[0]
|
|
|
|
for tag in tags:
|
|
if tag.rsplit(':', 1)[0] in all_name_tags:
|
|
way_names = [(w[tag], w.get(base_name_tag) if base_name_tag else None) for w in ways if tag in w]
|
|
if len(way_names) < 2:
|
|
continue
|
|
if ':' in tag:
|
|
namespaced_language = tag.rsplit(':')[-1]
|
|
|
|
if namespaced_language not in language_components:
|
|
address_components, country, language = self.components.expanded({}, latitude, longitude, language=namespaced_language)
|
|
language_components[namespaced_language] = (address_components, country, language)
|
|
else:
|
|
address_components, country, language = language_components[namespaced_language]
|
|
|
|
intersection_phrase = Intersection.phrase(language, country=country)
|
|
if not intersection_phrase:
|
|
continue
|
|
|
|
formatted_intersections = []
|
|
|
|
for (w1, w1_base), (w2, w2_base) in itertools.combinations(way_names, 2):
|
|
intersection = IntersectionQuery(road1=w1, intersection_phrase=intersection_phrase, road2=w2)
|
|
formatted = self.formatter.format_intersection(intersection, address_components, country, language, tag_components=tag_components)
|
|
formatted_intersections.append(formatted)
|
|
|
|
if w1_base and random.random() < replace_with_base_name_prob:
|
|
w1 = w1_base
|
|
|
|
intersection = IntersectionQuery(road1=w1, intersection_phrase=intersection_phrase, road2=w2)
|
|
formatted = self.formatter.format_intersection(intersection, address_components, country, language, tag_components=tag_components)
|
|
formatted_intersections.append(formatted)
|
|
|
|
if w2_base and random.random() < replace_with_base_name_prob:
|
|
w2 = w2_base
|
|
|
|
intersection = IntersectionQuery(road1=w1, intersection_phrase=intersection_phrase, road2=w2)
|
|
formatted = self.formatter.format_intersection(intersection, address_components, country, language, tag_components=tag_components)
|
|
formatted_intersections.append(formatted)
|
|
|
|
for formatted in formatted_intersections:
|
|
if not formatted or not formatted.strip():
|
|
continue
|
|
|
|
formatted = tsv_string(formatted)
|
|
if not formatted or not formatted.strip():
|
|
continue
|
|
|
|
if tag_components:
|
|
row = (language, country, formatted)
|
|
else:
|
|
row = (formatted,)
|
|
|
|
writer.writerow(row)
|
|
|
|
i += 1
|
|
if i % 1000 == 0 and i > 0:
|
|
print('did {} intersections'.format(i))
|
|
|
|
def build_limited_training_data(self, infile, out_dir):
|
|
'''
|
|
Creates a special kind of formatted address training data from OSM's addr:* tags
|
|
but are designed for use in language classification. These records are similar
|
|
to the untagged formatted records but include the language and country
|
|
(suitable for concatenation with the rest of the language training data),
|
|
and remove several fields like country which usually do not contain helpful
|
|
information for classifying the language.
|
|
|
|
Example:
|
|
|
|
nb no Olaf Ryes Plass Oslo
|
|
'''
|
|
i = 0
|
|
|
|
f = open(os.path.join(out_dir, FORMATTED_ADDRESS_DATA_LANGUAGE_FILENAME), 'w')
|
|
writer = csv.writer(f, 'tsv_no_quote')
|
|
|
|
for node_id, value, deps in parse_osm(infile):
|
|
formatted_address, country, language = self.formatted_address_limited(value)
|
|
if not formatted_address:
|
|
continue
|
|
|
|
if formatted_address.strip():
|
|
formatted_address = tsv_string(formatted_address.strip())
|
|
if not formatted_address or not formatted_address.strip():
|
|
continue
|
|
|
|
row = (language, country, formatted_address)
|
|
writer.writerow(row)
|
|
|
|
i += 1
|
|
if i % 1000 == 0 and i > 0:
|
|
print('did {} formatted addresses'.format(i))
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