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uszipcode_features_database.py
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uszipcode_features_database.py
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"""Transformer to parse and augment US zipcodes with info from zipcode database."""
from h2oaicore.transformer_utils import CustomTransformer
from h2oaicore.systemutils import make_experiment_logger, loggerinfo, loggerwarning
import datatable as dt
import numpy as np
_global_modules_needed_by_name = ['pycodestyle==2.11.1', 'uszipcode==1.0.1']
from uszipcode import SearchEngine
class USZipcodeDBTransformer(CustomTransformer):
_unsupervised = True
_allow_transform_to_modify_output_feature_names = True
_numeric_output = True
@staticmethod
def do_acceptance_test():
return False
@staticmethod
def get_default_properties():
return dict(col_type="categorical", min_cols=1, max_cols=1, relative_importance=1)
@staticmethod
def to_dict_values(data, name):
result = dict()
data = data[name]
if data is None or len(data) == 0:
return result
for k in range(len(data)):
key = data[k]['key']
values = data[k]['values']
names = [d['x'] for d in values]
if len(data) > 1:
keys = [name + '_' + key + '_' + str(y) for y in names]
else:
keys = [name + '_' + str(y) for y in names]
vals = [d['y'] for d in values]
result = {**result, **dict(zip(keys, vals))}
return result
@staticmethod
def replaceBannedCharacters(str):
return str.replace('<', ' less ').replace('[', '(').replace(']', ')')
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.transformer_name = self.__class__.__name__
if self.transformer_name.endswith("Transformer"):
self.transformer_name = self.transformer_name[:-len("Transformer")]
self.search = SearchEngine(simple_zipcode=False)
def get_zipcode_features(self, value):
if value is None or not value:
return self.get_zipcode_null_features()
elif len(value) < 3:
# raise ValueError("Value '{}' too short for zip code.".format(value))
return self.get_zipcode_null_features()
elif value[:5] in ['000', '0000', '00000', ' ', ' ', ' ']:
return self.get_zipcode_null_features()
else:
lookup_value = value[:5] # US zipcode only
zip_data = self.search.by_zipcode(lookup_value)
if (zip_data.zipcode_type == None):
return self.get_zipcode_null_features()
# raise ValueError("Value '{}' not a zipcode.".format(value))
else:
zip_dict = zip_data.to_dict()
result = { # 'zip_key': value,
# 'zipcode_type': zip_dict['zipcode_type'],
# 'major_city': zip_dict['major_city'],
# 'post_office_city': zip_dict['post_office_city'],
# 'common_city_list': zip_dict['common_city_list'][0],
# 'county': zip_dict['county'],
# 'state': zip_dict['state'],
'lat': zip_dict['lat'],
'lng': zip_dict['lng'],
# 'timezone': zip_dict['timezone'],
'radius_in_miles': zip_dict['radius_in_miles'],
# 'area_code_list': ['469', '972'],
'population': zip_dict['population'],
'population_density': zip_dict['population_density'],
'land_area_in_sqmi': zip_dict['land_area_in_sqmi'],
'water_area_in_sqmi': zip_dict['water_area_in_sqmi'],
'housing_units': zip_dict['housing_units'],
'occupied_housing_units': zip_dict['occupied_housing_units'],
'median_home_value': zip_dict['median_home_value'],
'median_household_income': zip_dict['median_household_income'],
'bounds_west': zip_dict['bounds_west'],
'bounds_east': zip_dict['bounds_east'],
'bounds_north': zip_dict['bounds_north'],
'bounds_south': zip_dict['bounds_south'],
# 'zipcode': zip_dict['zipcode']
}
return {**result,
**self.to_dict_values(zip_dict, 'population_by_year'),
**self.to_dict_values(zip_dict, 'population_by_age'),
**self.to_dict_values(zip_dict, 'population_by_gender'),
**self.to_dict_values(zip_dict, 'population_by_race'),
**self.to_dict_values(zip_dict, 'head_of_household_by_age'),
**self.to_dict_values(zip_dict, 'families_vs_singles'),
**self.to_dict_values(zip_dict, 'households_with_kids'),
**self.to_dict_values(zip_dict, 'children_by_age'),
**self.to_dict_values(zip_dict, 'housing_type'),
**self.to_dict_values(zip_dict, 'year_housing_was_built'),
**self.to_dict_values(zip_dict, 'housing_occupancy'),
**self.to_dict_values(zip_dict, 'vancancy_reason'),
**self.to_dict_values(zip_dict, 'owner_occupied_home_values'),
**self.to_dict_values(zip_dict, 'rental_properties_by_number_of_rooms'),
**self.to_dict_values(zip_dict, 'monthly_rent_including_utilities_studio_apt'),
**self.to_dict_values(zip_dict, 'monthly_rent_including_utilities_1_b'),
**self.to_dict_values(zip_dict, 'monthly_rent_including_utilities_2_b'),
**self.to_dict_values(zip_dict, 'monthly_rent_including_utilities_3plus_b'),
**self.to_dict_values(zip_dict, 'employment_status'),
**self.to_dict_values(zip_dict, 'average_household_income_over_time'),
**self.to_dict_values(zip_dict, 'household_income'),
**self.to_dict_values(zip_dict, 'annual_individual_earnings'),
**self.to_dict_values(zip_dict,
'sources_of_household_income____percent_of_households_receiving_income'),
**self.to_dict_values(zip_dict,
'sources_of_household_income____average_income_per_household_by_income_source'),
**self.to_dict_values(zip_dict,
'household_investment_income____percent_of_households_receiving_investment_income'),
**self.to_dict_values(zip_dict,
'household_investment_income____average_income_per_household_by_income_source'),
**self.to_dict_values(zip_dict,
'household_retirement_income____percent_of_households_receiving_retirement_incom'),
**self.to_dict_values(zip_dict,
'household_retirement_income____average_income_per_household_by_income_source'),
**self.to_dict_values(zip_dict, 'source_of_earnings'),
**self.to_dict_values(zip_dict, 'means_of_transportation_to_work_for_workers_16_and_over'),
**self.to_dict_values(zip_dict, 'travel_time_to_work_in_minutes'),
**self.to_dict_values(zip_dict, 'educational_attainment_for_population_25_and_over'),
**self.to_dict_values(zip_dict, 'school_enrollment_age_3_to_17')
}
def get_zipcode_null_features(self):
null_dict = self.get_zipcode_features('79936')
for key, value in null_dict.items():
null_dict[key] = None
return null_dict
def get_zipcode_null_result(self, X, original_zip_column_name):
X[:, 'zip_key'] = '79936'
zip_list = ['79936']
zip_features = [self.get_zipcode_null_features() for x in zip_list]
X_g = dt.Frame({"zip_key": zip_list})
X_g.cbind(dt.Frame(zip_features))
X_g.key = 'zip_key'
X_result = X[:, :, dt.join(X_g)]
self._output_feature_names = ["{}:{}.{}".format(self.transformer_name,
original_zip_column_name, self.replaceBannedCharacters(f)) for f
in list(X_result[:, 1:].names)]
self._feature_desc = ["Property '{}' of zipcode column ['{}'] from US zipcode database (recipe '{}')".format(
f, original_zip_column_name, self.transformer_name) for f in list(X_result[:, 1:].names)]
return X_result[:, 1:]
def fit_transform(self, X: dt.Frame, y: np.array = None):
return self.transform(X)
def transform(self, X: dt.Frame):
logger = None
if self.context and self.context.experiment_id:
logger = make_experiment_logger(experiment_id=self.context.experiment_id,
tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir)
X = dt.Frame(X)
original_zip_column_name = X.names[0]
X = X[:, dt.str64(dt.f[0])]
X.names = ['zip_key']
try:
zip_list = dt.unique(X[~dt.isna(dt.f.zip_key), 0]).to_list()[0] + ['79936']
zip_features = [self.get_zipcode_features(x) for x in zip_list]
X_g = dt.Frame({"zip_key": zip_list})
X_g.cbind(dt.Frame(zip_features))
X_g.key = 'zip_key'
X_result = X[:, :, dt.join(X_g)]
self._output_feature_names = ["{}:{}.{}".format(self.transformer_name,
original_zip_column_name, self.replaceBannedCharacters(f))
for f in list(X_result[:, 1:].names)]
self._feature_desc = [
"Property '{}' of zipcode column ['{}'] from US zipcode database (recipe '{}')".format(
f, original_zip_column_name, self.transformer_name) for f in list(X_result[:, 1:].names)]
return X_result[:, 1:]
except ValueError as ve:
loggerinfo(logger, "Column '{}' is not a zipcode: {}".format(original_zip_column_name, str(ve)))
return self.get_zipcode_null_result(X, original_zip_column_name)
except TypeError as te:
loggerwarning(logger, "Column '{}' triggered TypeError: {}".format(original_zip_column_name, str(te)))
raise te