959 lines
36 KiB
Python
959 lines
36 KiB
Python
# -*- coding: utf-8 -*-
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'''
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osm_address_training_data.py
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----------------------------
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This script generates several training sets from OpenStreetMap addresses,
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streets, venues and toponyms.
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Note: the combined size of all the files created by this script exceeds 100GB
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so if training these models, it is wise to use a server-grade machine with
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plenty of disk space. The following commands can be used in parallel to create
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all the training sets:
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Ways:
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python osm_address_training_data.py -s $(OSM_DIR)/planet-ways.osm --language-rtree-dir=$(RTREE_DIR) -o $(OUT_DIR)
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Venues:
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python osm_address_training_data.py -v $(OSM_DIR)/planet-venues.osm --language-rtree-dir=$(RTREE_DIR) -o $(OUT_DIR)
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Address streets:
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python osm_address_training_data.py -a $(OSM_DIR)/planet-addresses.osm --language-rtree-dir=$(LANG_RTREE_DIR) -o $(OUT_DIR)
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Limited formatted addresses:
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python osm_address_training_data.py -a -l $(OSM_DIR)/planet-addresses.osm --language-rtree-dir=$(LANG_RTREE_DIR) --rtree-dir=$(RTREE_DIR) --neighborhoods-rtree-dir=$(NEIGHBORHOODS_RTREE_DIR) -o $(OUT_DIR)
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Formatted addresses (tagged):
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python osm_address_training_data.py -a $(OSM_DIR)/planet-addresses.osm -f --language-rtree-dir=$(LANG_RTREE_DIR) --neighborhoods-rtree-dir=$(NEIGHBORHOODS_RTREE_DIR) --rtree-dir=$(RTREE_DIR) -o $(OUT_DIR)
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Formatted addresses (untagged):
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python osm_address_training_data.py -a $(OSM_DIR)/planet-addresses.osm -f -u --language-rtree-dir=$(LANG_RTREE_DIR) --neighborhoods-rtree-dir=$(NEIGHBORHOODS_RTREE_DIR) --rtree-dir=$(RTREE_DIR) -o $(OUT_DIR)
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Toponyms:
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python osm_address_training_data.py -b $(OSM_DIR)/planet-borders.osm --language-rtree-dir=$(LANG_RTREE_DIR) -o $(OUT_DIR)
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'''
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import argparse
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import csv
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import os
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import operator
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import random
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import re
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import sys
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import tempfile
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import urllib
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import ujson as json
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import HTMLParser
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from collections import defaultdict, OrderedDict
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from lxml import etree
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from itertools import ifilter, chain, combinations
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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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sys.path.append(os.path.realpath(os.path.join(os.pardir, os.pardir, os.pardir, 'python')))
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from geodata.coordinates.conversion import *
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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 sample_random_language
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from geodata.states.state_abbreviations import STATE_ABBREVIATIONS, STATE_EXPANSIONS
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from geodata.language_id.polygon_lookup import country_and_languages
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from geodata.i18n.languages import *
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from geodata.address_formatting.formatter import AddressFormatter
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from geodata.osm.extract import *
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from geodata.polygons.language_polys import *
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from geodata.polygons.reverse_geocode import OSMReverseGeocoder, NeighborhoodReverseGeocoder
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from geodata.i18n.unicode_paths import DATA_DIR
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from geodata.csv_utils import *
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from geodata.file_utils import *
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this_dir = os.path.realpath(os.path.dirname(__file__))
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# Input files
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PLANET_ADDRESSES_INPUT_FILE = 'planet-addresses.osm'
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PLANET_WAYS_INPUT_FILE = 'planet-ways.osm'
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PLANET_VENUES_INPUT_FILE = 'planet-venues.osm'
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PLANET_BORDERS_INPUT_FILE = 'planet-borders.osm'
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# Output files
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WAYS_LANGUAGE_DATA_FILENAME = 'streets_by_language.tsv'
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ADDRESS_LANGUAGE_DATA_FILENAME = 'address_streets_by_language.tsv'
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ADDRESS_FORMAT_DATA_TAGGED_FILENAME = 'formatted_addresses_tagged.tsv'
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ADDRESS_FORMAT_DATA_FILENAME = 'formatted_addresses.tsv'
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ADDRESS_FORMAT_DATA_LANGUAGE_FILENAME = 'formatted_addresses_by_language.tsv'
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TOPONYM_LANGUAGE_DATA_FILENAME = 'toponyms_by_language.tsv'
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class AddressComponent(object):
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'''
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Declare an address component and its dependencies e.g.
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a house_numer cannot be used in the absence of a road name.
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'''
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ANY = 'any'
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def __init__(self, name, dependencies=tuple(), method=ANY):
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self.name = name
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self.dependencies = dependencies
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def __hash__(self):
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return hash(self.name)
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def __cmp__(self, other):
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return cmp(self.name, other.name)
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OSM_ADDRESS_COMPONENTS = OrderedDict.fromkeys([
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AddressComponent(AddressFormatter.HOUSE),
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AddressComponent(AddressFormatter.ROAD, dependencies=(AddressFormatter.HOUSE,
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AddressFormatter.HOUSE_NUMBER,
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AddressFormatter.SUBURB,
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AddressFormatter.CITY,
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AddressFormatter.POSTCODE)),
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AddressComponent(AddressFormatter.HOUSE_NUMBER, dependencies=(AddressFormatter.ROAD,)),
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AddressComponent(AddressFormatter.SUBURB, dependencies=(AddressFormatter.CITY, AddressFormatter.STATE,
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AddressFormatter.POSTCODE)),
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AddressComponent(AddressFormatter.CITY),
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AddressComponent(AddressFormatter.STATE, dependencies=(AddressFormatter.SUBURB, AddressFormatter.CITY,
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AddressFormatter.POSTCODE, AddressFormatter.COUNTRY)),
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AddressComponent(AddressFormatter.POSTCODE),
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AddressComponent(AddressFormatter.COUNTRY),
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])
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def num_deps(c):
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return len(c.dependencies)
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OSM_ADDRESS_COMPONENTS_SORTED = sorted(OSM_ADDRESS_COMPONENTS, key=num_deps)
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OSM_ADDRESS_COMPONENT_COMBINATIONS = []
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'''
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The following statements create a bitset of address components
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for quickly checking testing whether or not a candidate set of
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address components can be considered a full geographic string
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suitable for formatting (i.e. would be a valid geocoder query).
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For instance, a house number by itself is not sufficient
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to be considered a valid address for this purpose unless it
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has a road name as well. Using bitsets we can easily answer
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questions like "Is house/house_number/road/city valid?"
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'''
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OSM_ADDRESS_COMPONENT_VALUES = {
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c.name: 1 << i
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for i, c in enumerate(OSM_ADDRESS_COMPONENTS.keys())
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}
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OSM_ADDRESS_COMPONENTS_VALID = set()
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def component_bitset(components):
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return reduce(operator.or_, [OSM_ADDRESS_COMPONENT_VALUES[c] for c in components])
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for i in xrange(1, len(OSM_ADDRESS_COMPONENTS.keys())):
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for perm in combinations(OSM_ADDRESS_COMPONENTS.keys(), i):
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perm_set = set([p.name for p in perm])
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valid = all((not p.dependencies or any(d in perm_set for d in p.dependencies) for p in perm))
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if valid:
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components = [c.name for c in perm]
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OSM_ADDRESS_COMPONENT_COMBINATIONS.append(tuple(components))
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OSM_ADDRESS_COMPONENTS_VALID.add(component_bitset(components))
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class OSMField(object):
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def __init__(self, name, c_constant, alternates=None):
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self.name = name
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self.c_constant = c_constant
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self.alternates = alternates
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osm_fields = [
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# Field if alternate_names present, default field name if not, C header constant
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OSMField('addr:housename', 'OSM_HOUSE_NAME'),
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OSMField('addr:housenumber', 'OSM_HOUSE_NUMBER'),
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OSMField('addr:block', 'OSM_BLOCK'),
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OSMField('addr:street', 'OSM_STREET_ADDRESS'),
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OSMField('addr:place', 'OSM_PLACE'),
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OSMField('addr:city', 'OSM_CITY', alternates=['addr:locality', 'addr:municipality', 'addr:hamlet']),
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OSMField('addr:suburb', 'OSM_SUBURB'),
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OSMField('addr:neighborhood', 'OSM_NEIGHBORHOOD', alternates=['addr:neighbourhood']),
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OSMField('addr:district', 'OSM_DISTRICT'),
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OSMField('addr:subdistrict', 'OSM_SUBDISTRICT'),
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OSMField('addr:ward', 'OSM_WARD'),
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OSMField('addr:state', 'OSM_STATE'),
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OSMField('addr:province', 'OSM_PROVINCE'),
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OSMField('addr:postcode', 'OSM_POSTAL_CODE', alternates=['addr:postal_code']),
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OSMField('addr:country', 'OSM_COUNTRY'),
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]
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def write_osm_json(filename, out_filename):
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out = open(out_filename, 'w')
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writer = csv.writer(out, 'tsv_no_quote')
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for key, attrs, deps in parse_osm(filename):
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writer.writerow((key, json.dumps(attrs)))
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out.close()
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def read_osm_json(filename):
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reader = csv.reader(open(filename), delimiter='\t')
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for key, attrs in reader:
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yield key, json.loads(attrs)
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def normalize_osm_name_tag(tag, script=False):
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norm = tag.rsplit(':', 1)[-1]
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if not script:
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return norm
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return norm.split('_', 1)[0]
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def get_language_names(language_rtree, key, value, tag_prefix='name'):
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if not ('lat' in value and 'lon' in value):
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return None, None
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has_colon = ':' in tag_prefix
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tag_first_component = tag_prefix.split(':')[0]
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tag_last_component = tag_prefix.split(':')[-1]
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try:
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latitude, longitude = latlon_to_decimal(value['lat'], value['lon'])
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except Exception:
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return None, None
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country, candidate_languages, language_props = country_and_languages(language_rtree, latitude, longitude)
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if not (country and candidate_languages):
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return None, None
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num_langs = len(candidate_languages)
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default_langs = set([l['lang'] for l in candidate_languages if l.get('default')])
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num_defaults = len(default_langs)
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name_language = defaultdict(list)
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alternate_langs = []
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equivalent_alternatives = defaultdict(list)
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for k, v in value.iteritems():
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if k.startswith(tag_prefix + ':') and normalize_osm_name_tag(k, script=True) in languages:
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lang = k.rsplit(':', 1)[-1]
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alternate_langs.append((lang, v))
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equivalent_alternatives[v].append(lang)
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has_alternate_names = len(alternate_langs)
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# Some countries like Lebanon list things like name:en == name:fr == "Rue Abdel Hamid Karame"
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# Those addresses should be disambiguated rather than taken for granted
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ambiguous_alternatives = set([k for k, v in equivalent_alternatives.iteritems() if len(v) > 1])
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regional_defaults = 0
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country_defaults = 0
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regional_langs = set()
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country_langs = set()
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for p in language_props:
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if p['admin_level'] > 0:
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regional_defaults += sum((1 for lang in p['languages'] if lang.get('default')))
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regional_langs |= set([l['lang'] for l in p['languages']])
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else:
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country_defaults += sum((1 for lang in p['languages'] if lang.get('default')))
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country_langs |= set([l['lang'] for l in p['languages']])
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ambiguous_already_seen = set()
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for k, v in value.iteritems():
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if k.startswith(tag_prefix + ':'):
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if v not in ambiguous_alternatives:
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norm = normalize_osm_name_tag(k)
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norm_sans_script = normalize_osm_name_tag(k, script=True)
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if norm in languages or norm_sans_script in languages:
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name_language[norm].append(v)
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elif v not in ambiguous_already_seen:
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langs = [(lang, lang in default_langs) for lang in equivalent_alternatives[v]]
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lang = disambiguate_language(v, langs)
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if lang != AMBIGUOUS_LANGUAGE and lang != UNKNOWN_LANGUAGE:
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name_language[lang].append(v)
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ambiguous_already_seen.add(v)
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elif not has_alternate_names and k.startswith(tag_first_component) and (has_colon or ':' not in k) and normalize_osm_name_tag(k, script=True) == tag_last_component:
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if num_langs == 1:
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name_language[candidate_languages[0]['lang']].append(v)
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else:
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lang = disambiguate_language(v, [(l['lang'], l['default']) for l in candidate_languages])
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default_lang = candidate_languages[0]['lang']
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if lang == AMBIGUOUS_LANGUAGE:
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return None, None
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elif lang == UNKNOWN_LANGUAGE and num_defaults == 1:
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name_language[default_lang].append(v)
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elif lang != UNKNOWN_LANGUAGE:
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if lang != default_lang and lang in country_langs and country_defaults > 1 and regional_defaults > 0 and lang in WELL_REPRESENTED_LANGUAGES:
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return None, None
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name_language[lang].append(v)
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else:
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return None, None
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return country, name_language
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def build_ways_training_data(language_rtree, infile, out_dir):
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'''
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Creates a training set for language classification using most OSM ways
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(streets) under a fairly lengthy osmfilter definition which attempts to
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identify all roads/ways designated for motor vehicle traffic, which
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is more-or-less what we'd expect to see in addresses.
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The fields are {language, country, street name}. Example:
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ar ma ﺵﺍﺮﻋ ﻑﺎﻟ ﻮﻟﺩ ﻊﻤﻳﺭ
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'''
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i = 0
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f = open(os.path.join(out_dir, WAYS_LANGUAGE_DATA_FILENAME), 'w')
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writer = csv.writer(f, 'tsv_no_quote')
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for key, value, deps in parse_osm(infile, allowed_types=WAYS_RELATIONS):
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country, name_language = get_language_names(language_rtree, key, value, tag_prefix='name')
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if not name_language:
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continue
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for k, v in name_language.iteritems():
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for s in v:
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if k in languages:
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writer.writerow((k, country, tsv_string(s)))
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if i % 1000 == 0 and i > 0:
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print 'did', i, 'ways'
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i += 1
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f.close()
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OSM_IGNORE_KEYS = (
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'house',
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)
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def strip_keys(value, ignore_keys):
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for key in ignore_keys:
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value.pop(key, None)
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def osm_reverse_geocoded_components(admin_rtree, country, latitude, longitude):
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ret = defaultdict(list)
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for props in admin_rtree.point_in_poly(latitude, longitude, return_all=True):
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name = props.get('name')
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if not name:
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continue
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for k, v in props.iteritems():
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normalized_key = osm_address_components.get_component(country, k, v)
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if normalized_key:
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ret[normalized_key].append(props)
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return ret
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def build_address_format_training_data(admin_rtree, language_rtree, neighborhoods_rtree, infile, out_dir, tag_components=True):
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'''
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Creates formatted address training data for supervised sequence labeling (or potentially
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for unsupervised learning e.g. for word vectors) using addr:* tags in OSM.
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Example:
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cs cz Gorkého/road ev.2459/house_number | 40004/postcode Trmice/city | CZ/country
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The field structure is similar to other training data created by this script i.e.
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{language, country, data}. The data field here is a sequence of labeled tokens similar
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to what we might see in part-of-speech tagging.
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This format uses a special character "|" to denote possible breaks in the input (comma, newline).
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Note that for the address parser, we'd like it to be robust to many different types
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of input, so we may selectively eleminate components
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This information can potentially be used downstream by the sequence model as these
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breaks may be present at prediction time.
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Example:
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sr rs Crkva Svetog Arhangela Mihaila | Vukov put BB | 15303 Trsic
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This may be useful in learning word representations, statistical phrases, morphology
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or other models requiring only the sequence of words.
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'''
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i = 0
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formatter = AddressFormatter()
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osm_address_components.configure()
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if tag_components:
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formatted_tagged_file = open(os.path.join(out_dir, ADDRESS_FORMAT_DATA_TAGGED_FILENAME), 'w')
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writer = csv.writer(formatted_tagged_file, 'tsv_no_quote')
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else:
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formatted_file = open(os.path.join(out_dir, ADDRESS_FORMAT_DATA_FILENAME), 'w')
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writer = csv.writer(formatted_file, 'tsv_no_quote')
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remove_keys = OSM_IGNORE_KEYS
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for key, value, deps in parse_osm(infile):
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try:
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latitude, longitude = latlon_to_decimal(value['lat'], value['lon'])
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except Exception:
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continue
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country, candidate_languages, language_props = country_and_languages(language_rtree, latitude, longitude)
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if not (country and candidate_languages):
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continue
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for key in remove_keys:
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_ = value.pop(key, None)
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language = None
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if tag_components:
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if len(candidate_languages) == 1:
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language = candidate_languages[0]['lang']
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else:
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street = value.get('addr:street', None)
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if street is None:
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continue
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language = disambiguate_language(street, [(l['lang'], l['default']) for l in candidate_languages])
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address_components = {k: v for k, v in value.iteritems() if k.startswith('addr:') or k == 'name'}
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formatter.replace_aliases(address_components)
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address_country = address_components.get(AddressFormatter.COUNTRY)
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'''
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Country names
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-------------
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In OSM, addr:country is almost always an ISO-3166 alpha-2 country code.
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However, we'd like to expand these to include natural language forms
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of the country names we might be likely to encounter in a geocoder or
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handwritten address.
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These splits are somewhat arbitrary but could potentially be fit to data
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from OpenVenues or other sources on the usage of country name forms.
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If the address includes a country, the selection procedure proceeds as follows:
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1. With probability a, select the country name in the language of the address
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(determined above), or with the localized country name if the language is
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undtermined or ambiguous.
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2. With probability b(1-a), sample a language from the distribution of
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languages on the Internet and use the country's name in that language.
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3. This is implicit, but with probability (1-b)(1-a), keep the country code
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'''
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non_local_language = None
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r = random.random()
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# 1. 60% of the time: use the country name in the current language or the country's local language
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if address_country and r < 0.6:
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localized = None
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if language and language not in (AMBIGUOUS_LANGUAGE, UNKNOWN_LANGUAGE):
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localized = language_country_names.get(language, {}).get(address_country.upper())
|
|
|
|
if not localized:
|
|
localized = country_localized_display_name(address_country.lower())
|
|
|
|
if localized:
|
|
address_components[AddressFormatter.COUNTRY] = localized
|
|
# 2. 10% of the time: country's name in a language samples from the distribution of languages on the Internet
|
|
elif address_country and r < 0.7:
|
|
non_local_language = sample_random_language()
|
|
lang_country = language_country_names.get(non_local_language, {}).get(address_country.upper())
|
|
if lang_country:
|
|
address_components[AddressFormatter.COUNTRY] = lang_country
|
|
# 3. Implicit: the rest of the time keep the country code
|
|
|
|
'''
|
|
States
|
|
------
|
|
|
|
Primarily for the US, Canada and Australia, OSM tends to use the abbreviated state name
|
|
whereas we'd like to include both forms, so wtih some probability, replace the abbreviated
|
|
name with the unabbreviated one e.g. CA => California
|
|
'''
|
|
address_state = address_components.get(AddressFormatter.STATE)
|
|
|
|
if address_state:
|
|
state_full_name = STATE_ABBREVIATIONS.get(country.upper(), {}).get(address_state.upper(), {}).get(language)
|
|
|
|
if state_full_name and random.random() < 0.3:
|
|
address_components[AddressFormatter.STATE] = state_full_name
|
|
|
|
'''
|
|
OSM boundaries
|
|
--------------
|
|
|
|
For many addresses, the city, district, region, etc. are all implicitly
|
|
generated by the reverse geocoder e.g. we do not need an addr:city tag
|
|
to identify that 40.74, -74.00 is in New York City as well as its parent
|
|
geographies (New York county, New York state, etc.)
|
|
|
|
Where possible we augment the addr:* tags with some of the reverse-geocoded
|
|
relations from OSM.
|
|
|
|
Since addresses found on the web may have the same properties, we
|
|
include these qualifiers in the training data.
|
|
'''
|
|
|
|
osm_components = osm_reverse_geocoded_components(admin_rtree, country, latitude, longitude)
|
|
if osm_components:
|
|
if non_local_language is not None:
|
|
suffix = ':{}'.format(non_local_language)
|
|
else:
|
|
suffix = ''
|
|
|
|
name_key = ''.join(('name', suffix))
|
|
raw_name_key = 'name'
|
|
short_name_key = ''.join(('short_name', suffix))
|
|
raw_short_name_key = 'short_name'
|
|
alt_name_key = ''.join(('alt_name', suffix))
|
|
raw_alt_name_key = 'alt_name'
|
|
official_name_key = ''.join(('official_name', suffix))
|
|
raw_official_name_key = 'official_name'
|
|
|
|
poly_components = defaultdict(list)
|
|
|
|
for component, values in osm_components.iteritems():
|
|
seen = set()
|
|
|
|
# Choose which name to use with given probabilities
|
|
r = random.random()
|
|
if r < 0.1:
|
|
# 10% of the time use the short name
|
|
key = short_name_key
|
|
raw_key = raw_short_name_key
|
|
elif r < 0.2:
|
|
# 10% of the time use the official name
|
|
key = official_name_key
|
|
raw_key = raw_official_name_key
|
|
elif r < 0.3:
|
|
# 10% of the time use the official name
|
|
key = alt_name_key
|
|
raw_key = raw_alt_name_key
|
|
else:
|
|
# 70% of the time use the name tag
|
|
key = name_key
|
|
raw_key = raw_name_key
|
|
|
|
for value in values:
|
|
name = value.get(key, value.get(raw_key))
|
|
|
|
if not name:
|
|
name = value.get(name_key, value.get(raw_name_key))
|
|
|
|
if not name:
|
|
continue
|
|
|
|
if (component, name) not in seen:
|
|
poly_components[component].append(name)
|
|
seen.add((component, name))
|
|
|
|
for component, vals in poly_components.iteritems():
|
|
if component not in address_components:
|
|
value = u', '.join(vals)
|
|
if component == AddressFormatter.STATE and random.random() < 0.7:
|
|
value = STATE_EXPANSIONS.get(address_country, {}).get(value, value)
|
|
address_components[component] = value
|
|
|
|
'''
|
|
Neighborhoods
|
|
-------------
|
|
|
|
In some cities, neighborhoods may be included in a free-text address.
|
|
|
|
OSM includes many neighborhoods but only as points, rather than the polygons
|
|
needed to perform reverse-geocoding. We use a hybrid index containing
|
|
Quattroshapes/Zetashapes polygons matched fuzzily with OSM names (which are
|
|
on the whole of better quality).
|
|
'''
|
|
|
|
neighborhood = neighborhoods_rtree.point_in_poly(latitude, longitude)
|
|
if neighborhood and AddressFormatter.SUBURB not in address_components:
|
|
address_components[AddressFormatter.SUBURB] = neighborhood['name']
|
|
|
|
# Version with all components
|
|
formatted_address = formatter.format_address(country, address_components, tag_components=tag_components, minimal_only=not tag_components)
|
|
|
|
if tag_components:
|
|
formatted_addresses = []
|
|
formatted_addresses.append(formatted_address)
|
|
|
|
address_components = {k: v for k, v in address_components.iteritems() if k in OSM_ADDRESS_COMPONENT_VALUES}
|
|
if not address_components:
|
|
continue
|
|
|
|
current_components = component_bitset(address_components.keys())
|
|
|
|
for component in address_components.keys():
|
|
if current_components ^ OSM_ADDRESS_COMPONENT_VALUES[component] in OSM_ADDRESS_COMPONENTS_VALID and random.random() < 0.5:
|
|
address_components.pop(component)
|
|
current_components ^= OSM_ADDRESS_COMPONENT_VALUES[component]
|
|
if not address_components:
|
|
break
|
|
|
|
formatted_address = formatter.format_address(country, address_components, tag_components=tag_components, minimal_only=False)
|
|
formatted_addresses.append(formatted_address)
|
|
|
|
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
|
|
row = (language, country, formatted_address)
|
|
|
|
writer.writerow(row)
|
|
elif formatted_address and formatted_address.strip():
|
|
formatted_address = tsv_string(formatted_address)
|
|
writer.writerow([formatted_address])
|
|
|
|
i += 1
|
|
if i % 1000 == 0 and i > 0:
|
|
print 'did', i, 'formatted addresses'
|
|
|
|
NAME_KEYS = (
|
|
'name',
|
|
'addr:housename',
|
|
)
|
|
COUNTRY_KEYS = (
|
|
'country',
|
|
'country_name',
|
|
'addr:country',
|
|
'is_in:country',
|
|
'addr:country_code',
|
|
'country_code',
|
|
'is_in:country_code'
|
|
)
|
|
|
|
POSTAL_KEYS = (
|
|
'postcode',
|
|
'postal_code',
|
|
'addr:postcode',
|
|
'addr:postal_code',
|
|
)
|
|
|
|
|
|
def build_address_format_training_data_limited(rtree, language_rtree, 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 8 | Oslo
|
|
'''
|
|
i = 0
|
|
|
|
formatter = AddressFormatter()
|
|
|
|
f = open(os.path.join(out_dir, ADDRESS_FORMAT_DATA_LANGUAGE_FILENAME), 'w')
|
|
writer = csv.writer(f, 'tsv_no_quote')
|
|
|
|
remove_keys = NAME_KEYS + COUNTRY_KEYS + POSTAL_KEYS + OSM_IGNORE_KEYS
|
|
|
|
for key, value, deps in parse_osm(infile):
|
|
try:
|
|
latitude, longitude = latlon_to_decimal(value['lat'], value['lon'])
|
|
except Exception:
|
|
continue
|
|
|
|
for k in remove_keys:
|
|
_ = value.pop(k, None)
|
|
|
|
if not value:
|
|
continue
|
|
|
|
country, name_language = get_language_names(language_rtree, key, value, tag_prefix='addr:street')
|
|
if not name_language:
|
|
continue
|
|
|
|
single_language = len(name_language) == 1
|
|
for lang, val in name_language.iteritems():
|
|
if lang not in languages:
|
|
continue
|
|
|
|
address_dict = value.copy()
|
|
for k in address_dict.keys():
|
|
namespaced_val = u'{}:{}'.format(k, lang)
|
|
if namespaced_val in address_dict:
|
|
address_dict[k] = address_dict[namespaced_val]
|
|
elif not single_language:
|
|
address_dict.pop(k)
|
|
|
|
if not address_dict:
|
|
continue
|
|
|
|
formatted_address_untagged = formatter.format_address(country, address_dict, tag_components=False)
|
|
if formatted_address_untagged is not None:
|
|
formatted_address_untagged = tsv_string(formatted_address_untagged)
|
|
|
|
writer.writerow((lang, country, formatted_address_untagged))
|
|
|
|
i += 1
|
|
if i % 1000 == 0 and i > 0:
|
|
print 'did', i, 'formatted addresses'
|
|
|
|
|
|
def build_toponym_training_data(language_rtree, infile, out_dir):
|
|
'''
|
|
Data set of toponyms by language and country which should assist
|
|
in language classification. OSM tends to use the native language
|
|
by default (e.g. Москва instead of Moscow). Toponyms get messy
|
|
due to factors like colonialism, historical names, name borrowing
|
|
and the shortness of the names generally. In these cases
|
|
we're more strict as to what constitutes a valid language for a
|
|
given country.
|
|
|
|
Example:
|
|
ja jp 東京都
|
|
'''
|
|
i = 0
|
|
f = open(os.path.join(out_dir, TOPONYM_LANGUAGE_DATA_FILENAME), 'w')
|
|
writer = csv.writer(f, 'tsv_no_quote')
|
|
|
|
for key, value, deps in parse_osm(infile):
|
|
if not sum((1 for k, v in value.iteritems() if k.startswith('name'))) > 0:
|
|
continue
|
|
|
|
try:
|
|
latitude, longitude = latlon_to_decimal(value['lat'], value['lon'])
|
|
except Exception:
|
|
continue
|
|
|
|
country, candidate_languages, language_props = country_and_languages(language_rtree, latitude, longitude)
|
|
if not (country and candidate_languages):
|
|
continue
|
|
|
|
name_language = defaultdict(list)
|
|
|
|
official = official_languages[country]
|
|
|
|
default_langs = set([l for l, default in official.iteritems() if default])
|
|
|
|
regional_langs = list(chain(*(p['languages'] for p in language_props if p.get('admin_level', 0) > 0)))
|
|
|
|
top_lang = None
|
|
if len(official) > 0:
|
|
top_lang = official.iterkeys().next()
|
|
|
|
# E.g. Hindi in India, Urdu in Pakistan
|
|
if top_lang is not None and top_lang not in WELL_REPRESENTED_LANGUAGES and len(default_langs) > 1:
|
|
default_langs -= WELL_REPRESENTED_LANGUAGES
|
|
|
|
valid_languages = set([l['lang'] for l in candidate_languages])
|
|
|
|
'''
|
|
WELL_REPRESENTED_LANGUAGES are languages like English, French, etc. for which we have a lot of data
|
|
WELL_REPRESENTED_LANGUAGE_COUNTRIES are more-or-less the "origin" countries for said languages where
|
|
we can take the place names as examples of the language itself (e.g. place names in France are examples
|
|
of French, whereas place names in much of Francophone Africa tend to get their names from languages
|
|
other than French, even though French is the official language.
|
|
'''
|
|
valid_languages -= set([lang for lang in valid_languages if lang in WELL_REPRESENTED_LANGUAGES and country not in WELL_REPRESENTED_LANGUAGE_COUNTRIES[lang]])
|
|
|
|
valid_languages |= default_langs
|
|
|
|
if not valid_languages:
|
|
continue
|
|
|
|
have_qualified_names = False
|
|
|
|
for k, v in value.iteritems():
|
|
if not k.startswith('name:'):
|
|
continue
|
|
|
|
norm = normalize_osm_name_tag(k)
|
|
norm_sans_script = normalize_osm_name_tag(k, script=True)
|
|
|
|
if norm in languages:
|
|
lang = norm
|
|
elif norm_sans_script in languages:
|
|
lang = norm_sans_script
|
|
else:
|
|
continue
|
|
|
|
if lang in valid_languages:
|
|
have_qualified_names = True
|
|
name_language[lang].append(v)
|
|
|
|
if not have_qualified_names and len(regional_langs) <= 1 and 'name' in value and len(valid_languages) == 1:
|
|
name_language[top_lang].append(value['name'])
|
|
|
|
for k, v in name_language.iteritems():
|
|
for s in v:
|
|
s = s.strip()
|
|
if not s:
|
|
continue
|
|
writer.writerow((k, country, tsv_string(s)))
|
|
if i % 1000 == 0 and i > 0:
|
|
print 'did', i, 'toponyms'
|
|
i += 1
|
|
|
|
f.close()
|
|
|
|
|
|
def build_address_training_data(langauge_rtree, infile, out_dir, format=False):
|
|
'''
|
|
Creates training set similar to the ways data but using addr:street tags instead.
|
|
These may be slightly closer to what we'd see in real live addresses, containing
|
|
variations, some abbreviations (although this is discouraged in OSM), etc.
|
|
|
|
Example record:
|
|
eu es Errebal kalea
|
|
'''
|
|
i = 0
|
|
f = open(os.path.join(out_dir, ADDRESS_LANGUAGE_DATA_FILENAME), 'w')
|
|
writer = csv.writer(f, 'tsv_no_quote')
|
|
|
|
for key, value, deps in parse_osm(infile):
|
|
country, street_language = get_language_names(language_rtree, key, value, tag_prefix='addr:street')
|
|
if not street_language:
|
|
continue
|
|
|
|
for k, v in street_language.iteritems():
|
|
for s in v:
|
|
s = s.strip()
|
|
if not s:
|
|
continue
|
|
if k in languages:
|
|
writer.writerow((k, country, tsv_string(s)))
|
|
if i % 1000 == 0 and i > 0:
|
|
print 'did', i, 'streets'
|
|
i += 1
|
|
|
|
f.close()
|
|
|
|
VENUE_LANGUAGE_DATA_FILENAME = 'names_by_language.tsv'
|
|
|
|
|
|
def build_venue_training_data(language_rtree, infile, out_dir):
|
|
i = 0
|
|
|
|
f = open(os.path.join(out_dir, VENUE_LANGUAGE_DATA_FILENAME), 'w')
|
|
writer = csv.writer(f, 'tsv_no_quote')
|
|
|
|
for key, value, deps in parse_osm(infile):
|
|
country, name_language = get_language_names(language_rtree, key, value, tag_prefix='name')
|
|
if not name_language:
|
|
continue
|
|
|
|
venue_type = None
|
|
for key in (u'amenity', u'building'):
|
|
amenity = value.get(key, u'').strip()
|
|
if amenity in ('yes', 'y'):
|
|
continue
|
|
|
|
if amenity:
|
|
venue_type = u':'.join([key, amenity])
|
|
break
|
|
|
|
if venue_type is None:
|
|
continue
|
|
|
|
for k, v in name_language.iteritems():
|
|
for s in v:
|
|
s = s.strip()
|
|
if k in languages:
|
|
writer.writerow((k, country, safe_encode(venue_type), tsv_string(s)))
|
|
if i % 1000 == 0 and i > 0:
|
|
print 'did', i, 'venues'
|
|
i += 1
|
|
|
|
f.close()
|
|
|
|
if __name__ == '__main__':
|
|
# Handle argument parsing here
|
|
parser = argparse.ArgumentParser()
|
|
|
|
parser.add_argument('-s', '--streets-file',
|
|
help='Path to planet-ways.osm')
|
|
|
|
parser.add_argument('-a', '--address-file',
|
|
help='Path to planet-addresses.osm')
|
|
|
|
parser.add_argument('-v', '--venues-file',
|
|
help='Path to planet-venues.osm')
|
|
|
|
parser.add_argument('-b', '--borders-file',
|
|
help='Path to planet-borders.osm')
|
|
|
|
parser.add_argument('-f', '--format-only',
|
|
action='store_true',
|
|
default=False,
|
|
help='Save formatted addresses (slow)')
|
|
|
|
parser.add_argument('-u', '--untagged',
|
|
action='store_true',
|
|
default=False,
|
|
help='Save untagged formatted addresses (slow)')
|
|
|
|
parser.add_argument('-l', '--limited-addresses',
|
|
action='store_true',
|
|
default=False,
|
|
help='Save formatted addresses without house names or country (slow)')
|
|
|
|
parser.add_argument('-t', '--temp-dir',
|
|
default=tempfile.gettempdir(),
|
|
help='Temp directory to use')
|
|
|
|
parser.add_argument('-g', '--language-rtree-dir',
|
|
required=True,
|
|
help='Language RTree directory')
|
|
|
|
parser.add_argument('-r', '--rtree-dir',
|
|
default=None,
|
|
help='OSM reverse geocoder RTree directory')
|
|
|
|
parser.add_argument('-q', '--quattroshapes-rtree-dir',
|
|
default=None,
|
|
help='Quattroshapes reverse geocoder RTree directory')
|
|
|
|
parser.add_argument('-n', '--neighborhoods-rtree-dir',
|
|
default=None,
|
|
help='Neighborhoods reverse geocoder RTree directory')
|
|
|
|
parser.add_argument('-o', '--out-dir',
|
|
default=os.getcwd(),
|
|
help='Output directory')
|
|
|
|
args = parser.parse_args()
|
|
|
|
init_country_names()
|
|
init_languages()
|
|
|
|
language_rtree = LanguagePolygonIndex.load(args.language_rtree_dir)
|
|
osm_rtree = None
|
|
if args.rtree_dir:
|
|
osm_rtree = OSMReverseGeocoder.load(args.rtree_dir)
|
|
|
|
neighborhoods_rtree = None
|
|
if args.neighborhoods_rtree_dir:
|
|
neighborhoods_rtree = NeighborhoodReverseGeocoder.load(args.neighborhoods_rtree_dir)
|
|
|
|
street_types_gazetteer.configure()
|
|
|
|
# Can parallelize
|
|
if args.streets_file:
|
|
build_ways_training_data(language_rtree, args.streets_file, args.out_dir)
|
|
if args.borders_file:
|
|
build_toponym_training_data(language_rtree, args.borders_file, args.out_dir)
|
|
|
|
if args.address_file and not args.format_only and not args.limited_addresses:
|
|
build_address_training_data(language_rtree, args.address_file, args.out_dir)
|
|
elif args.address_file and osm_rtree is None:
|
|
parser.error('--rtree-dir required for formatted addresses')
|
|
|
|
if args.address_file and args.format_only:
|
|
build_address_format_training_data(osm_rtree, language_rtree, neighborhoods_rtree, args.address_file, args.out_dir, tag_components=not args.untagged)
|
|
if args.address_file and args.limited_addresses:
|
|
build_address_format_training_data_limited(osm_rtree, language_rtree, neighborhoods_rtree, args.address_file, args.out_dir)
|
|
if args.venues_file:
|
|
build_venue_training_data(language_rtree, args.venues_file, args.out_dir)
|