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[osm] Separating tagged from untagged output

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This commit is contained in:
Al
2015-09-19 14:11:47 -04:00
parent 2940cc15b8
commit 6731395ca0
1 changed files with 45 additions and 17 deletions
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62
scripts/geodata/osm/osm_address_training_data.py
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@@ -490,16 +490,39 @@ def strip_keys(value, ignore_keys):
value.pop(key, None)
def build_address_format_training_data(language_rtree, infile, out_dir):
def build_address_format_training_data(language_rtree, infile, out_dir, tag_components=True):
'''
Creates formatted address training data for supervised sequence labeling (or potentially
for unsupervised learning e.g. for word vectors) using addr:* tags in OSM. The tagged
version produces a TSV file that looks like:
cs cz Gorkého/road ev.2459/house_number | 40004/postcode Trmice/city | CZ/country
The field structure is similar to other training data created by this script i.e.
{language, country, data}. The data field here is a sequence of labeled tokens similar
to what we might see in part-of-speech tagging.
This format uses a special character "|" to denote possible breaks in the input (comma, newline).
This information can potentially be used downstream by the sequence model as these
breaks may be present at prediction time.
For the untagged version, lines simply look like:
The Dignity | 363 Regents Park Road | London N3 1DH
This may be useful in learning word representations, statistical phrases, morphology
or other models requiring only the sequence of words.
'''
i = 0
formatter = AddressFormatter()
formatted_file = open(os.path.join(out_dir, ADDRESS_FORMAT_DATA_FILENAME), 'w')
formatted_writer = csv.writer(formatted_file, 'tsv_no_quote')
formatted_tagged_file = open(os.path.join(out_dir, ADDRESS_FORMAT_DATA_TAGGED_FILENAME), 'w')
formatted_tagged_writer = csv.writer(formatted_tagged_file, 'tsv_no_quote')
if tag_components:
formatted_tagged_file = open(os.path.join(out_dir, ADDRESS_FORMAT_DATA_TAGGED_FILENAME), 'w')
writer = csv.writer(formatted_tagged_file, 'tsv_no_quote')
else:
formatted_file = open(os.path.join(out_dir, ADDRESS_FORMAT_DATA_FILENAME), 'w')
writer = csv.writer(formatted_file, 'tsv_no_quote')
remove_keys = OSM_IGNORE_KEYS
@@ -509,24 +532,29 @@ def build_address_format_training_data(language_rtree, infile, out_dir):
except Exception:
continue
country, default_languages, language_props = country_and_languages(language_rtree, latitude, longitude)
if not (country and default_languages):
country, candidate_languages, language_props = country_and_languages(language_rtree, latitude, longitude)
if not (country and candidate_languages):
continue
for key in remove_keys:
_ = value.pop(key, None)
formatted_address_tagged = formatter.format_address(country, value)
formatted_address_untagged = formatter.format_address(country, value, tag_components=False)
if formatted_address_tagged is not None:
formatted_address_tagged = tsv_string(formatted_address_tagged)
formatted_tagged_writer.writerow((default_languages[0]['lang'], country, formatted_address_tagged))
if len(candidate_languages) == 1:
language = candidate_languages[0]['lang']
else:
language = disambiguate_language(v, [(l['lang'], l['default']) for l in candidate_languages])
if formatted_address_untagged is not None:
formatted_address_untagged = tsv_string(formatted_address_untagged)
formatted_writer.writerow((default_languages[0]['lang'], country, formatted_address_untagged))
formatted_address = formatter.format_address(country, value, tag_components=tag_components)
if formatted_address is not None:
formatted_address = tsv_string(formatted_address)
if tag_components:
row = (language, country, formatted_address)
else:
row = (formatted_address,)
if formatted_address_tagged is not None or formatted_address_untagged is not None:
writer.writerow(row)
if formatted_address is not None:
i += 1
if i % 1000 == 0 and i > 0:
print 'did', i, 'formatted addresses'