def init_disambiguation():
global char_scripts, script_languages
char_scripts[:] = []
char_scripts.extend(get_chars_by_script())
script_languages.update({
script: set(langs)
for script, langs in get_script_languages().iteritems()
})
def create_from_osm_and_quattroshapes(cls, filename, quattroshapes_dir, output_dir, scratch_dir=SCRATCH_DIR):
'''
Given an OSM file (planet or some other bounds) containing neighborhoods
as points (some suburbs have boundaries)
and their dependencies, create an R-tree index for coarse-grained
reverse geocoding.
Note: the input file is expected to have been created using
osmfilter. Use fetch_osm_address_data.sh for planet or copy the
admin borders commands if using other geometries.
'''
index = cls(save_dir=output_dir)
ensure_dir(scratch_dir)
logger = logging.getLogger('neighborhoods')
logger.setLevel(logging.INFO)
qs_scratch_dir = os.path.join(scratch_dir, 'qs_neighborhoods')
ensure_dir(qs_scratch_dir)
logger.info('Creating Quattroshapes neighborhoods')
qs = QuattroshapesReverseGeocoder.create_neighborhoods_index(quattroshapes_dir, qs_scratch_dir)
logger.info('Creating Zetashapes neighborhoods')
zs = cls.create_zetashapes_neighborhoods_index()
logger.info('Creating IDF index')
idf = IDFIndex()
char_scripts = get_chars_by_script()
for idx in (zs, qs):
for i, (props, poly) in enumerate(idx.polygons):
name = props.get('name')
if name is not None:
doc = cls.count_words(name)
idf.update(doc)
for key, attrs, deps in parse_osm(filename):
for k, v in attrs.iteritems():
if any((k.startswith(name_key) for name_key in OSM_NAME_TAGS)):
doc = cls.count_words(v)
idf.update(doc)
qs.matched = [False] * qs.i
zs.matched = [False] * zs.i
logger.info('Matching OSM points to neighborhood polygons')
# Parse OSM and match neighborhood/suburb points to Quattroshapes/Zetashapes polygons
num_polys = 0
for node_id, attrs, deps in parse_osm(filename):
try:
lat, lon = latlon_to_decimal(attrs['lat'], attrs['lon'])
except ValueError:
continue
osm_name = attrs.get('name')
if not osm_name:
continue
is_neighborhood = attrs.get('place') == 'neighbourhood'
ranks = []
osm_names = []
for key in OSM_NAME_TAGS:
name = attrs.get(key)
if name:
osm_names.append(name)
for name_key in OSM_NAME_TAGS:
osm_names.extend([v for k, v in attrs.iteritems() if k.startswith('{}:'.format(name_key))])
for idx in (zs, qs):
candidates = idx.get_candidate_polygons(lat, lon, all_levels=True)
if candidates:
max_sim = 0.0
arg_max = None
normalized_qs_names = {}
for osm_name in osm_names:
contains_ideographs = any(((char_scripts[ord(c)] or '').lower() in ideographic_scripts
for c in safe_decode(osm_name)))
for i in candidates:
props, poly = idx.polygons[i]
name = normalized_qs_names.get(i)
if not name:
name = props.get('name')
if not name:
continue
for pattern, repl in cls.regex_replacements:
name = pattern.sub(repl, name)
normalized_qs_names[i] = name
level = props.get(QuattroshapesReverseGeocoder.LEVEL)
if is_neighborhood and level != 'neighborhood':
continue
if not contains_ideographs:
sim = NeighborhoodDeduper.compare(osm_name, name, idf)
else:
# Many Han/Hangul characters are common, shouldn't use IDF
sim = NeighborhoodDeduper.compare_ideographs(osm_name, name)
if sim > max_sim:
max_sim = sim
arg_max = (max_sim, props, poly.context, idx, i)
if arg_max:
ranks.append(arg_max)
ranks.sort(key=operator.itemgetter(0), reverse=True)
if ranks and ranks[0][0] >= cls.DUPE_THRESHOLD:
score, props, poly, idx, i = ranks[0]
if idx is zs:
attrs['polygon_type'] = 'neighborhood'
else:
level = props.get(QuattroshapesReverseGeocoder.LEVEL, None)
if level == 'neighborhood':
attrs['polygon_type'] = 'neighborhood'
else:
attrs['polygon_type'] = 'local_admin'
attrs['source'] = 'osm'
index.index_polygon(poly)
index.add_polygon(poly, attrs)
idx.matched[i] = True
num_polys += 1
if num_polys % 1000 == 0 and num_polys > 0:
logger.info('did {} neighborhoods'.format(num_polys))
for idx, source in ((zs, 'zetashapes'), (qs, 'quattroshapes')):
for i, (props, poly) in enumerate(idx.polygons):
if idx.matched[i]:
continue
props['source'] = source
if idx is zs or props.get(QuattroshapesReverseGeocoder.LEVEL, None) == 'neighborhood':
props['polygon_type'] = 'neighborhood'
else:
# We don't actually care about local admin polygons unless they match OSM
continue
index.index_polygon(poly.context)
index.add_polygon(poly.context, props)
return index
def create_from_osm_and_quattroshapes(cls, filename, quattroshapes_dir, country_rtree_dir, osm_rtree_dir, osm_neighborhood_borders_file, output_dir):
'''
Given an OSM file (planet or some other bounds) containing neighborhoods
as points (some suburbs have boundaries)
and their dependencies, create an R-tree index for coarse-grained
reverse geocoding.
Note: the input file is expected to have been created using
osmfilter. Use fetch_osm_address_data.sh for planet or copy the
admin borders commands if using other geometries.
'''
index = cls(save_dir=output_dir)
logger = logging.getLogger('neighborhoods')
qs_scratch_dir = os.path.join(quattroshapes_dir, 'qs_neighborhoods')
ensure_dir(qs_scratch_dir)
logger.info('Creating ClickThatHood neighborhoods')
cth = ClickThatHoodReverseGeocoder.create_neighborhoods_index()
logger.info('Creating OSM neighborhoods')
osmn = OSMNeighborhoodReverseGeocoder.create_neighborhoods_index(osm_neighborhood_borders_file)
logger.info('Creating Quattroshapes neighborhoods')
qs = QuattroshapesNeighborhoodsReverseGeocoder.create_neighborhoods_index(quattroshapes_dir, qs_scratch_dir)
country_rtree = OSMCountryReverseGeocoder.load(country_rtree_dir)
osm_admin_rtree = OSMReverseGeocoder.load(osm_rtree_dir)
osm_admin_rtree.cache_size = 1000
logger.info('Creating IDF index')
idf = IDFIndex()
char_scripts = get_chars_by_script()
for idx in (cth, qs, osmn):
for i in xrange(idx.i):
props = idx.get_properties(i)
name = props.get('name')
if name is not None:
doc = cls.count_words(name)
idf.update(doc)
for key, attrs, deps in parse_osm(filename):
for k, v in six.iteritems(attrs):
if any((k.startswith(name_key) for name_key in OSM_NAME_TAGS)):
doc = cls.count_words(v)
idf.update(doc)
for i in six.moves.xrange(osmn.i):
props = osmn.get_properties(i)
poly = osmn.get_polygon(i)
props['source'] = 'osm'
props['component'] = AddressFormatter.SUBURB
props['polygon_type'] = 'neighborhood'
index.index_polygon(poly.context)
index.add_polygon(poly.context, props)
qs.matched = [False] * qs.i
cth.matched = [False] * cth.i
logger.info('Matching OSM points to neighborhood polygons')
# Parse OSM and match neighborhood/suburb points to Quattroshapes/ClickThatHood polygons
num_polys = 0
for element_id, attrs, deps in parse_osm(filename):
try:
lat, lon = latlon_to_decimal(attrs['lat'], attrs['lon'])
except ValueError:
continue
osm_name = attrs.get('name')
if not osm_name:
continue
id_type, element_id = element_id.split(':')
element_id = long(element_id)
props['type'] = id_type
props['id'] = element_id
possible_neighborhood = osm_definitions.meets_definition(attrs, osm_definitions.EXTENDED_NEIGHBORHOOD)
is_neighborhood = osm_definitions.meets_definition(attrs, osm_definitions.NEIGHBORHOOD)
country, candidate_languages = country_rtree.country_and_languages(lat, lon)
component_name = None
component_name = osm_address_components.component_from_properties(country, attrs)
ranks = []
osm_names = []
for key in OSM_NAME_TAGS:
name = attrs.get(key)
if name:
osm_names.append(name)
for name_key in OSM_NAME_TAGS:
osm_names.extend([v for k, v in six.iteritems(attrs) if k.startswith('{}:'.format(name_key))])
for idx in (cth, qs):
candidates = idx.get_candidate_polygons(lat, lon, return_all=True)
if candidates:
max_sim = 0.0
arg_max = None
normalized_qs_names = {}
for osm_name in osm_names:
contains_ideographs = any(((char_scripts[ord(c)] or '').lower() in ideographic_scripts
for c in safe_decode(osm_name)))
for i in candidates:
props = idx.get_properties(i)
name = normalized_qs_names.get(i)
if not name:
name = props.get('name')
if not name:
continue
for pattern, repl in cls.regex_replacements:
name = pattern.sub(repl, name)
normalized_qs_names[i] = name
if is_neighborhood and idx is qs and props.get(QuattroshapesReverseGeocoder.LEVEL) != 'neighborhood':
continue
if not contains_ideographs:
sim = NeighborhoodDeduper.compare(osm_name, name, idf)
else:
# Many Han/Hangul characters are common, shouldn't use IDF
sim = NeighborhoodDeduper.compare_ideographs(osm_name, name)
if sim > max_sim:
max_sim = sim
poly = idx.get_polygon(i)
arg_max = (max_sim, props, poly.context, idx, i)
if arg_max:
ranks.append(arg_max)
ranks.sort(key=operator.itemgetter(0), reverse=True)
if ranks and ranks[0][0] >= cls.DUPE_THRESHOLD:
score, props, poly, idx, i = ranks[0]
existing_osm_boundaries = osm_admin_rtree.point_in_poly(lat, lon, return_all=True)
existing_neighborhood_boundaries = osmn.point_in_poly(lat, lon, return_all=True)
skip_node = False
for boundaries in (existing_osm_boundaries, existing_neighborhood_boundaries):
for poly_index, osm_props in enumerate(boundaries):
containing_component = None
name = osm_props.get('name')
# Only exact name matches here since we're comparins OSM to OSM
if name and name.lower() != attrs.get('name', '').lower():
continue
if boundaries is existing_neighborhood_boundaries:
containing_component = AddressFormatter.SUBURB
skip_node = True
break
else:
containing_ids = [(boundary['type'], boundary['id']) for boundary in existing_osm_boundaries[poly_index + 1:]]
containing_component = osm_address_components.component_from_properties(country, osm_props, containing=containing_ids)
if containing_component and containing_component != component_name and AddressFormatter.component_order[containing_component] <= AddressFormatter.component_order[AddressFormatter.CITY]:
skip_node = True
break
if skip_node:
break
# Skip this element
if skip_node:
continue
if idx is cth:
if props['component'] == AddressFormatter.SUBURB:
attrs['polygon_type'] = 'neighborhood'
elif props['component'] == AddressFormatter.CITY_DISTRICT:
attrs['polygon_type'] = 'local_admin'
else:
continue
source = 'osm_cth'
else:
level = props.get(QuattroshapesReverseGeocoder.LEVEL, None)
source = 'osm_quattro'
if level == 'neighborhood':
attrs['polygon_type'] = 'neighborhood'
else:
attrs['polygon_type'] = 'local_admin'
containing_ids = [(boundary['type'], boundary['id']) for boundary in existing_osm_boundaries]
component = osm_address_components.component_from_properties(country, attrs, containing=containing_ids)
attrs['component'] = component
attrs['source'] = source
index.index_polygon(poly)
index.add_polygon(poly, attrs)
idx.matched[i] = True
num_polys += 1
if num_polys % 1000 == 0 and num_polys > 0:
logger.info('did {} neighborhoods'.format(num_polys))
for idx, source in ((cth, 'clickthathood'), (qs, 'quattroshapes')):
for i in xrange(idx.i):
props = idx.get_properties(i)
poly = idx.get_polygon(i)
if idx.matched[i]:
continue
props['source'] = source
if idx is cth:
component = props['component']
if component == AddressFormatter.SUBURB:
props['polygon_type'] = 'neighborhood'
elif component == AddressFormatter.CITY_DISTRICT:
props['polygon_type'] = 'local_admin'
else:
continue
elif props.get(QuattroshapesReverseGeocoder.LEVEL, None) == 'neighborhood':
component = AddressFormatter.SUBURB
name = props.get('name')
if not name:
continue
for pattern, repl in cls.regex_replacements:
name = pattern.sub(repl, name)
props['name'] = name
if cls.quattroshapes_city_district_regex.match(name):
component = AddressFormatter.CITY_DISTRICT
props['component'] = component
props['polygon_type'] = 'neighborhood'
else:
# We don't actually care about local admin polygons unless they match OSM
continue
index.index_polygon(poly.context)
index.add_polygon(poly.context, props)
return index
def init_disambiguation():
global char_scripts, script_languages
char_scripts[:] = []
char_scripts.extend(get_chars_by_script())
script_languages.update({script: set(langs) for script, langs in get_script_languages().iteritems()})
def create_from_osm_and_quattroshapes(cls,
filename,
quattroshapes_dir,
output_dir,
scratch_dir=SCRATCH_DIR):
'''
Given an OSM file (planet or some other bounds) containing neighborhoods
as points (some suburbs have boundaries)
and their dependencies, create an R-tree index for coarse-grained
reverse geocoding.
Note: the input file is expected to have been created using
osmfilter. Use fetch_osm_address_data.sh for planet or copy the
admin borders commands if using other geometries.
'''
index = cls(save_dir=output_dir)
ensure_dir(scratch_dir)
logger = logging.getLogger('neighborhoods')
logger.setLevel(logging.INFO)
qs_scratch_dir = os.path.join(scratch_dir, 'qs_neighborhoods')
ensure_dir(qs_scratch_dir)
logger.info('Creating Quattroshapes neighborhoods')
qs = QuattroshapesNeighborhoodsReverseGeocoder.create_neighborhoods_index(
quattroshapes_dir, qs_scratch_dir)
logger.info('Creating Zetashapes neighborhoods')
zs = cls.create_zetashapes_neighborhoods_index()
logger.info('Creating IDF index')
idf = IDFIndex()
char_scripts = get_chars_by_script()
for idx in (zs, qs):
for i, (props, poly) in enumerate(idx.polygons):
name = props.get('name')
if name is not None:
doc = cls.count_words(name)
idf.update(doc)
for key, attrs, deps in parse_osm(filename):
for k, v in attrs.iteritems():
if any((k.startswith(name_key) for name_key in OSM_NAME_TAGS)):
doc = cls.count_words(v)
idf.update(doc)
qs.matched = [False] * qs.i
zs.matched = [False] * zs.i
logger.info('Matching OSM points to neighborhood polygons')
# Parse OSM and match neighborhood/suburb points to Quattroshapes/Zetashapes polygons
num_polys = 0
for node_id, attrs, deps in parse_osm(filename):
try:
lat, lon = latlon_to_decimal(attrs['lat'], attrs['lon'])
except ValueError:
continue
osm_name = attrs.get('name')
if not osm_name:
continue
is_neighborhood = attrs.get('place') == 'neighbourhood'
ranks = []
osm_names = []
for key in OSM_NAME_TAGS:
name = attrs.get(key)
if name:
osm_names.append(name)
for name_key in OSM_NAME_TAGS:
osm_names.extend([
v for k, v in attrs.iteritems()
if k.startswith('{}:'.format(name_key))
])
for idx in (zs, qs):
candidates = idx.get_candidate_polygons(lat,
lon,
return_all=True)
if candidates:
max_sim = 0.0
arg_max = None
normalized_qs_names = {}
for osm_name in osm_names:
contains_ideographs = any(
((char_scripts[ord(c)] or '').lower()
in ideographic_scripts
for c in safe_decode(osm_name)))
for i in candidates:
props, poly = idx.polygons[i]
name = normalized_qs_names.get(i)
if not name:
name = props.get('name')
if not name:
continue
for pattern, repl in cls.regex_replacements:
name = pattern.sub(repl, name)
normalized_qs_names[i] = name
if is_neighborhood and idx is qs and props.get(
QuattroshapesReverseGeocoder.LEVEL
) != 'neighborhood':
continue
if not contains_ideographs:
sim = NeighborhoodDeduper.compare(
osm_name, name, idf)
else:
# Many Han/Hangul characters are common, shouldn't use IDF
sim = NeighborhoodDeduper.compare_ideographs(
osm_name, name)
if sim > max_sim:
max_sim = sim
arg_max = (max_sim, props, poly.context, idx,
i)
if arg_max:
ranks.append(arg_max)
ranks.sort(key=operator.itemgetter(0), reverse=True)
if ranks and ranks[0][0] >= cls.DUPE_THRESHOLD:
score, props, poly, idx, i = ranks[0]
if idx is zs:
attrs['polygon_type'] = 'neighborhood'
source = 'osm_zeta'
else:
level = props.get(QuattroshapesReverseGeocoder.LEVEL, None)
source = 'osm_quattro'
if level == 'neighborhood':
attrs['polygon_type'] = 'neighborhood'
else:
attrs['polygon_type'] = 'local_admin'
attrs['source'] = source
index.index_polygon(poly)
index.add_polygon(poly, attrs)
idx.matched[i] = True
num_polys += 1
if num_polys % 1000 == 0 and num_polys > 0:
logger.info('did {} neighborhoods'.format(num_polys))
for idx, source in ((zs, 'zetashapes'), (qs, 'quattroshapes')):
for i, (props, poly) in enumerate(idx.polygons):
if idx.matched[i]:
continue
props['source'] = source
if idx is zs or props.get(QuattroshapesReverseGeocoder.LEVEL,
None) == 'neighborhood':
props['polygon_type'] = 'neighborhood'
else:
# We don't actually care about local admin polygons unless they match OSM
continue
index.index_polygon(poly.context)
index.add_polygon(poly.context, props)
return index
if prefix_search and self.trie.get(token[:prefix_len]):
yield (token_types.PHRASE, [(c, ) + t], prefix_search)
continue
yield c, t, data
street_types_gazetteer = DictionaryPhraseFilter(
'street_types.txt',
'directionals.txt',
'concatenated_suffixes_separable.txt',
'concatenated_suffixes_inseparable.txt',
'concatenated_prefixes_separable.txt',
'stopwords.txt',
)
char_scripts = get_chars_by_script()
script_languages = {
script: set(langs)
for script, langs in get_script_languages().iteritems()
}
UNKNOWN_SCRIPT = 'Unknown'
COMMON_SCRIPT = 'Common'
MAX_ASCII = 127
def get_string_script(s):
s = safe_decode(s)
str_len = len(s)
script = last_script = UNKNOWN_SCRIPT
is_ascii = True
# For toponyms, we want to limit the countries we consider to those where
# the place names can themselves be considered training examples of the language
WELL_REPRESENTED_LANGUAGE_COUNTRIES = {
'en': set(['gb', 'us', 'ca', 'au', 'nz', 'ie']),
'fr': set(['fr']),
'it': set(['it']),
'de': set(['de', 'at']),
'nl': set(['nl']),
'es': set(['es', 'ar', 'mx', 'cl', 'co', 'pe', 'ec', 'pr', 'uy',
've', 'cu', 'do', 'bo', 'gt', 'cr', 'py', 'sv', 'pa',
'ni', 'hn']),
'pt': set(['pt', 'br']),
}
char_scripts = get_chars_by_script()
script_languages = {script: set(langs) for script, langs in six.iteritems(get_script_languages())}
lang_scripts = defaultdict(set)
for script, langs in six.iteritems(script_languages):
for lang in langs:
lang_scripts[lang].add(script)
lang_scripts = dict(lang_scripts)
UNKNOWN_SCRIPT = 'Unknown'
COMMON_SCRIPT = 'Common'
MAX_ASCII = 127
def get_string_script(s):
