536 lines
20 KiB
Python
536 lines
20 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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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) --quattroshapes-rtree-dir=$(QS_TREE_DIR) --geonames-db=$(GEONAMES_DB_PATH) -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) --quattroshapes-rtree-dir=$(QS_TREE_DIR) --geonames-db=$(GEONAMES_DB_PATH) -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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from geodata.address_expansions.abbreviations import abbreviate
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from geodata.address_expansions.gazetteers import *
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from geodata.addresses.components import AddressComponents
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from geodata.coordinates.conversion import *
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from geodata.geonames.db import GeoNamesDB
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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.i18n.languages import *
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from geodata.neighborhoods.reverse_geocode import NeighborhoodReverseGeocoder
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from geodata.osm.extract import *
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from geodata.osm.formatter import OSMAddressFormatter
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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.csv_utils import *
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from geodata.file_utils import *
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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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TOPONYM_LANGUAGE_DATA_FILENAME = 'toponyms_by_language.tsv'
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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 = language_rtree.country_and_languages(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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ALL_LANGUAGES = 'all'
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def build_ways_training_data(language_rtree, infile, out_dir, abbreviate_streets=True):
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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 lang, val in name_language.iteritems():
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for v in val:
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for s in v.split(';'):
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if lang in languages:
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writer.writerow((lang, country, tsv_string(s)))
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if not abbreviate_streets:
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continue
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abbrev = abbreviate(street_and_synonyms_gazetteer, s, lang)
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if abbrev != s:
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writer.writerow((lang, country, tsv_string(abbrev)))
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if i % 1000 == 0 and i > 0:
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print('did {} ways'.format(i))
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i += 1
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f.close()
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NAME_KEYS = (
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'name',
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'addr:housename',
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)
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HOUSE_NUMBER_KEYS = (
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'addr:house_number',
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'addr:housenumber',
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'house_number'
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)
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COUNTRY_KEYS = (
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'country',
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'country_name',
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'addr:country',
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'is_in:country',
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'addr:country_code',
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'country_code',
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'is_in:country_code'
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)
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POSTAL_KEYS = (
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'postcode',
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'postal_code',
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'addr:postcode',
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'addr:postal_code',
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)
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def build_toponym_training_data(language_rtree, infile, out_dir):
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'''
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Data set of toponyms by language and country which should assist
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in language classification. OSM tends to use the native language
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by default (e.g. Москва instead of Moscow). Toponyms get messy
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due to factors like colonialism, historical names, name borrowing
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and the shortness of the names generally. In these cases
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we're more strict as to what constitutes a valid language for a
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given country.
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Example:
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ja jp 東京都
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'''
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i = 0
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f = open(os.path.join(out_dir, TOPONYM_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):
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if not any((k.startswith('name') for k, v in value.iteritems())):
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continue
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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 = language_rtree.country_and_languages(latitude, longitude)
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if not (country and candidate_languages):
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continue
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name_language = defaultdict(list)
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official = official_languages[country]
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default_langs = set([l for l, default in official.iteritems() if default])
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regional_langs = list(chain(*(p['languages'] for p in language_props if p.get('admin_level', 0) > 0)))
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top_lang = None
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if len(official) > 0:
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top_lang = official.iterkeys().next()
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# E.g. Hindi in India, Urdu in Pakistan
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if top_lang is not None and top_lang not in WELL_REPRESENTED_LANGUAGES and len(default_langs) > 1:
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default_langs -= WELL_REPRESENTED_LANGUAGES
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valid_languages = set([l['lang'] for l in candidate_languages])
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'''
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WELL_REPRESENTED_LANGUAGES are languages like English, French, etc. for which we have a lot of data
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WELL_REPRESENTED_LANGUAGE_COUNTRIES are more-or-less the "origin" countries for said languages where
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we can take the place names as examples of the language itself (e.g. place names in France are examples
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of French, whereas place names in much of Francophone Africa tend to get their names from languages
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other than French, even though French is the official language.
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'''
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valid_languages -= set([lang for lang in valid_languages if lang in WELL_REPRESENTED_LANGUAGES and country not in WELL_REPRESENTED_LANGUAGE_COUNTRIES[lang]])
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valid_languages |= default_langs
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if not valid_languages:
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continue
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have_qualified_names = False
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for k, v in value.iteritems():
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if not k.startswith('name:'):
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continue
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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:
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lang = norm
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elif norm_sans_script in languages:
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lang = norm_sans_script
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else:
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continue
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if lang in valid_languages:
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have_qualified_names = True
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name_language[lang].append(v)
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if not have_qualified_names and len(regional_langs) <= 1 and 'name' in value and len(valid_languages) == 1:
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name_language[top_lang].append(value['name'])
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for k, v in name_language.iteritems():
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for s in v:
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s = s.strip()
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if not s:
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continue
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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 {} toponyms'.format(i))
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i += 1
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f.close()
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def build_address_training_data(langauge_rtree, infile, out_dir, format=False):
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'''
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Creates training set similar to the ways data but using addr:street tags instead.
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These may be slightly closer to what we'd see in real live addresses, containing
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variations, some abbreviations (although this is discouraged in OSM), etc.
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Example record:
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eu es Errebal kalea
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'''
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i = 0
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f = open(os.path.join(out_dir, ADDRESS_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):
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country, street_language = get_language_names(language_rtree, key, value, tag_prefix='addr:street')
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if not street_language:
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continue
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for k, v in street_language.iteritems():
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for s in v:
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s = s.strip()
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if not s:
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continue
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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 {} streets'.format(i))
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i += 1
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f.close()
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VENUE_LANGUAGE_DATA_FILENAME = 'names_by_language.tsv'
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def build_venue_training_data(language_rtree, infile, out_dir):
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i = 0
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f = open(os.path.join(out_dir, VENUE_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):
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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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venue_type = None
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for key in (u'amenity', u'building'):
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amenity = value.get(key, u'').strip()
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if amenity in ('yes', 'y'):
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continue
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if amenity:
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venue_type = u':'.join([key, amenity])
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break
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if venue_type is None:
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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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s = s.strip()
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if k in languages:
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writer.writerow((k, country, safe_encode(venue_type), tsv_string(s)))
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if i % 1000 == 0 and i > 0:
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print('did, {} venues'.format(i))
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i += 1
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f.close()
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if __name__ == '__main__':
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# Handle argument parsing here
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parser = argparse.ArgumentParser()
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parser.add_argument('-s', '--streets-file',
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help='Path to planet-ways.osm')
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parser.add_argument('--unabbreviated',
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action='store_true',
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default=False,
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help='Use unabbreviated street names for token counts')
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parser.add_argument('-a', '--address-file',
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help='Path to planet-addresses.osm')
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parser.add_argument('-v', '--venues-file',
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help='Path to planet-venues.osm')
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parser.add_argument('-b', '--borders-file',
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help='Path to planet-borders.osm')
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parser.add_argument('-f', '--format',
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action='store_true',
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default=False,
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help='Save formatted addresses (slow)')
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parser.add_argument('-u', '--untagged',
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action='store_true',
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default=False,
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help='Save untagged formatted addresses (slow)')
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parser.add_argument('-l', '--limited-addresses',
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action='store_true',
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default=False,
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help='Save formatted addresses without house names or country (slow)')
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parser.add_argument('-t', '--temp-dir',
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default=tempfile.gettempdir(),
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help='Temp directory to use')
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parser.add_argument('--language-rtree-dir',
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required=True,
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help='Language RTree directory')
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parser.add_argument('--rtree-dir',
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default=None,
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help='OSM reverse geocoder RTree directory')
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parser.add_argument('--quattroshapes-rtree-dir',
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default=None,
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help='Quattroshapes reverse geocoder RTree directory')
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parser.add_argument('--subdivisions-rtree-dir',
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default=None,
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help='Subdivisions reverse geocoder RTree directory')
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parser.add_argument('--buildings-rtree-dir',
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default=None,
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help='Buildings reverse geocoder RTree directory')
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parser.add_argument('--geonames-db',
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default=None,
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help='GeoNames db file')
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parser.add_argument('--neighborhoods-rtree-dir',
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default=None,
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help='Neighborhoods reverse geocoder RTree directory')
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parser.add_argument('-o', '--out-dir',
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|
default=os.getcwd(),
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help='Output directory')
|
|
|
|
args = parser.parse_args()
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|
|
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language_rtree = LanguagePolygonIndex.load(args.language_rtree_dir)
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|
osm_rtree = None
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|
if args.rtree_dir:
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|
osm_rtree = OSMReverseGeocoder.load(args.rtree_dir)
|
|
|
|
neighborhoods_rtree = None
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|
if args.neighborhoods_rtree_dir:
|
|
neighborhoods_rtree = NeighborhoodReverseGeocoder.load(args.neighborhoods_rtree_dir)
|
|
|
|
quattroshapes_rtree = None
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|
if args.quattroshapes_rtree_dir:
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|
quattroshapes_rtree = QuattroshapesReverseGeocoder.load(args.quattroshapes_rtree_dir)
|
|
|
|
subdivisions_rtree = None
|
|
if args.subdivisions_rtree_dir:
|
|
subdivisions_rtree = OSMSubdivisionReverseGeocoder.load(args.subdivisions_rtree_dir)
|
|
|
|
buildings_rtree = None
|
|
if args.subdivisions_rtree_dir:
|
|
buildings_rtree = OSMBuildingReverseGeocoder.load(args.buildings_rtree)
|
|
|
|
geonames = None
|
|
|
|
if args.geonames_db:
|
|
geonames = GeoNamesDB(args.geonames_db)
|
|
|
|
# Can parallelize
|
|
if args.streets_file:
|
|
build_ways_training_data(language_rtree, args.streets_file, args.out_dir, abbreviate_streets=not args.unabbreviated)
|
|
if args.borders_file:
|
|
build_toponym_training_data(language_rtree, args.borders_file, args.out_dir)
|
|
|
|
if args.address_file:
|
|
if osm_rtree is None:
|
|
parser.error('--rtree-dir required for formatted addresses')
|
|
elif neighborhoods_rtree is None:
|
|
parser.error('--neighborhoods-rtree-dir required for formatted addresses')
|
|
elif quattroshapes_rtree is None:
|
|
parser.error('--quattroshapes-rtree-dir required for formatted addresses')
|
|
elif geonames is None:
|
|
parser.error('--geonames-db required for formatted addresses')
|
|
|
|
if args.address_file and args.format:
|
|
components = AddressComponents(osm_rtree, language_rtree, neighborhoods_rtree, quattroshapes_rtree, geonames)
|
|
osm_formatter = OSMAddressFormatter(components, subdivisions_rtree, buildings_rtree)
|
|
osm_formatter.build_training_data(args.address_file, args.out_dir, tag_components=not args.untagged)
|
|
if args.address_file and args.limited_addresses:
|
|
components = AddressComponents(osm_rtree, language_rtree, neighborhoods_rtree, quattroshapes_rtree, geonames)
|
|
osm_formatter = OSMAddressFormatter(components, subdivisions_rtree, buildings_rtree, splitter=u' ')
|
|
osm_formatter.build_limited_training_data(args.address_file, args.out_dir)
|
|
if args.venues_file:
|
|
build_venue_training_data(language_rtree, args.venues_file, args.out_dir)
|