def process(input_file, output_name, var_map, calc=None, agg_areas=True):
def _add_pct(data_frame):
var_list = data_frame.columns.tolist()
for var in GEO_COLUMNS + ['area']:
if var in var_list:
var_list.remove(var)
return pct.add_percentages(data_frame, var_list, var_list[0])
def _export(data_frame, suffix, include_index=False):
full_name = output_name + '_' + suffix + '.csv'
data_frame.to_csv(full_name, index=include_index)
print('Saved file: ' + full_name)
return
# Clean municipality data
data = cd.clean_data(input_file)
data_new = data[GEO_COLUMNS + sorted(var_map.keys())]
data_new = data_new.rename(columns=var_map)
# Perform any extra necessary calculations
if calc:
data_new = calc(data_new)
# Aggregate
if agg_areas:
data_agg = agg.aggregate(data_new)
data_ri = agg.aggregate(data_new, agg_var=(lambda x: True))
# Calculate percentages
data_new_w_pct = _add_pct(data_new)
if agg_areas:
data_agg_w_pct = _add_pct(data_agg)
data_ri_w_pct = _add_pct(data_ri.drop('area', axis=1))
# Export to CSV
_export(data_new_w_pct, 'munis')
if agg_areas:
_export(data_agg_w_pct, 'areas', include_index=True)
_export(data_ri_w_pct, 'state')
return (data_new_w_pct, data_agg_w_pct, data_ri_w_pct)
else:
return (data_new_w_pct,)
import csv
import pandas as pd
import numpy as np
import kanonymize as ka
import cleandata as cd
df = pd.read_csv('HoustonCrimeData.csv', encoding='utf_8_sig', engine='python')
df = df.drop([
'Occurrence Date', 'Occurrence Hour', 'NIBRS Class', 'Beat',
'Offense Count', 'Suffix'
],
axis=1)
df = cd.clean_data(df)
#print(df.head())
print(df.groupby('NIBRS Description').count().sort_values(['Incident']))
#IN algorithm, manually create address field yourself.
#print(df.groupby('Address').count())
print('df')
ka.k_anonymize(df, 5)
df.to_csv("anonymized_data.csv")
import pandas as pd
import sys
sys.path.append('../')
import cleandata as cd
# Export municipality data
data = cd.clean_data('ACS_14_5YR_B19013_with_ann.csv')
data = data.drop('HD02_VD01', axis=1)
data = data.rename(columns={'HD01_VD01': 'med_hh_inc'})
data.to_csv('income_munis.csv', index=False)
import pandas as pd
import sys
sys.path.append('../')
import cleandata as cd
# Export municipality data
data = cd.clean_data('ACS_14_5YR_B19013_with_ann.csv')
data = data.drop('HD02_VD01', axis=1)
data = data.rename(columns={'HD01_VD01': 'med_hh_inc'})
data.to_csv('income_munis.csv', index=False)
run = Run.get_context()
client = ExplanationClient.from_run(run)
ds = TabularDatasetFactory.from_delimited_files(
"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv",
validate=True,
include_path=False,
infer_column_types=True,
set_column_types=None,
separator=',',
header=True,
partition_format=None,
support_multi_line=False,
empty_as_string=False)
x, y = clean_data(ds)
feature_names = list(x.columns)
x_train, x_test, y_train, y_test = train_test_split(x,
y,
test_size=0.33,
random_state=42)
def main():
# Add arguments to script
parser = argparse.ArgumentParser()
parser.add_argument(
'--C',
