def get_population(self, code, year, given_population):
code_without_year = code[0:5]
try:
pop_this_year = float(self.counties[code_without_year][year])
pop_2010 = float(self.counties[code_without_year]['2010'])
ratio = (pop_this_year / pop_2010)
return float(given_population) * ratio
except (KeyError):
# set up personal logger
logger = Logger()
current_path = os.getcwd()
logger.define_issue_log(os.path.join(current_path, 'files/issues.log'))
logger.record_issue('No population data for county:', code)
return given_population
import numpy, pandas
import statistics, csv, os
from sklearn.metrics import r2_score
from sklearn import linear_model
from sklearn.model_selection import train_test_split
from Utilities import Logger
# set up personal logger
logger = Logger()
current_path = os.getcwd()
logger.define_issue_log(os.path.join(current_path, 'files/issues.log'))
logger.log('(b = bankruptcy or bankruptcies)')
def partition(data_set, training_set_portion):
data_set.shuffle()
size_of_training_set = round(
len(data_set) / float(1 - training_set_portion))
training_set = data_set[:size_of_training_set]
test_set = data_set[size_of_training_set:]
return {'train': training_set, 'test': test_set}
# def learn(training_inputs, training_outputs, test_inputs, test_outputs):
df = pandas.read_csv("./files/puma-output.csv")
all_X = df[[
'Divorce', 'Age', 'Education', 'Insurance', 'Black', 'Disabled', 'Veteran',
'Immigrant', 'Unemployed'
]]
all_y = df['Bankruptcy']
def get_acs_person_data(filepath, year):
# set up personal logger
logger = Logger()
current_path = os.getcwd()
logger.define_issue_log(os.path.join(current_path, 'files/issues.log'))
dictionary_of_people = dict()
with open(filepath) as csvfile:
file_reader = csv.reader(csvfile)
line = next(file_reader)
class Column(Enum):
SERIALNO = line.index('SERIALNO')
ST = line.index('ST')
PUMA = line.index('PUMA')
AGEP = line.index('AGEP')
SCHL = line.index('SCHL')
MAR = line.index('MAR')
HICOV = line.index('HICOV')
RACBLK = line.index('RACBLK') # 0 = not black; 1 = black
DIS = line.index('DIS') # 1 = disabled; 2 = not disabled
MIL = line.index('MIL') # 1-3 = veteran
WAOB = line.index('WAOB') # 1-2 = non-immigrant
NWAB = line.index('NWAB') # 1 = temp work absence; 2 = no
try:
i = 0
while True:
acs_row = next(file_reader)
serial_number = acs_row[Column.SERIALNO.value]
person = Person(serial_number)
state = acs_row[Column.ST.value]
person.state = state # TODO make an enum of state names and use it here
person.puma = state + acs_row[Column.PUMA.value] + year
person.age = acs_row[Column.AGEP.value]
person.education = acs_row[Column.SCHL.value]
if acs_row[Column.MAR.value] == '3':
person.divorced = True
else:
person.divorced = False
if acs_row[Column.HICOV.value] == '1':
person.insured = True
else:
person.insured = False
if acs_row[Column.RACBLK.value] == '1':
person.black = True
else:
person.black = False
if acs_row[Column.DIS.value] == '1':
person.disabled = True
else:
person.disabled = False
mil = acs_row[Column.MIL.value]
if mil == '1' or mil == '2' or mil == '3':
person.veteran = True
else:
person.veteran = False
if acs_row[Column.WAOB.value] == '1' or acs_row[
Column.WAOB.value] == '2':
person.immigrant = False
else:
person.immigrant = True
nwab = acs_row[Column.NWAB.value]
if nwab == '1':
person.unemployed = True
elif nwab == '2' or nwab == 'b':
person.unemployed = False
else:
person.unemployed = 'NA'
id = serial_number + state + year
dictionary_of_people[id] = person
# i += 1
# logger.log('Setting up person #', format(i, ',d'), erase=True)
except StopIteration:
pass
logger.log('Created', format(len(dictionary_of_people), ',d'),
'people from', filepath)
return dictionary_of_people
