def predict(address: List[str], model_dir: str = None) -> List[Dict[str, str]]:
"""
Segments a set of addresses into their components and attempts to normalise categorical components,
e.g. state, street type
:param address: the input list of address strings
:param model_dir: path to trained model
:return: a list of dictionaries with the address components separated
"""
if model_dir is None:
model_dir = os.path.join(os.path.dirname(__file__), 'pretrained')
assert os.path.isdir(model_dir), "invalid model_dir provided: %s" % model_dir
address_net_estimator = _get_estimator(model_fn, model_dir)
result = address_net_estimator.predict(predict_input_fn(address))
class_names = [l.replace("_code", "") for l in labels_list]
class_names = [l.replace("_abbreviation", "") for l in class_names]
for addr, res in zip(address, result):
mappings = dict()
for char, class_id in zip(addr.upper(), res['class_ids']):
if class_id == 0:
continue
cls = class_names[class_id - 1]
mappings[cls] = mappings.get(cls, "") + char
if 'state' in mappings:
mappings['state'] = normalise_state(mappings['state'])
if 'street_type' in mappings:
mappings['street_type'] = normalise_street_type(mappings['street_type'])
if 'street_suffix' in mappings:
mappings['street_suffix'] = normalise_street_suffix(mappings['street_suffix'])
if 'flat_type' in mappings:
mappings['flat_type'] = normalise_flat_type(mappings['flat_type'])
if 'level_type' in mappings:
mappings['level_type'] = normalise_level_type(mappings['level_type'])
yield mappings
def predict(address: List[str], model_dir: str = None) -> List[Dict[str, str]]:
"""
Segments a set of addresses into their components and attempts to normalise categorical components,
e.g. state, street type
:param address: the input list of address strings
:param model_dir: path to trained model
:return: a list of dictionaries with the address components separated
"""
if model_dir is None:
model_dir = os.path.join(os.path.dirname(__file__), 'pretrained')
assert os.path.isdir(
model_dir), "invalid model_dir provided: %s" % model_dir
address_net_estimator = _get_estimator(model_fn, model_dir)
result = address_net_estimator.predict(predict_input_fn(address))
class_names = [l.replace("_code", "") for l in labels_list]
class_names = [l.replace("_abbreviation", "") for l in class_names]