def query(days: int = 1) -> pd.DataFrame:
entry_limit = 24 * days
orig, anon, props = None, None, None
with mock.patch('sys.argv', DEFAULT_ARGS +
['--entry_limit', str(entry_limit)]):
orig, anon, props = main_script()
return (orig, anon, props)
def run_experiment(write_csv: callable = lambda _: None) -> pd.DataFrame:
df_list = []
ls = np.logspace(np.log10(LS_START), np.log10(LS_STOP), num=100, dtype=int)
ls = list(dict.fromkeys(ls))
progressbar = tqdm(total=len(ls) * NUM_P)
for entry_limit in ls:
all_props = []
for _ in range(NUM_P):
with mock.patch(
'sys.argv', DEFAULT_ARGS +
['--entry_limit', str(entry_limit)]):
_, _, props = main_script()
props = props[props['column_name'] == 'sensor02']
del props['column_name']
all_props.append(props)
progressbar.update()
all_props: pd.DataFrame = pd.concat(all_props, ignore_index=True)
all_props['util_min'], all_props['util_max'], all_props[
'util_mean'] = [all_props['utility']] * 3
all_props['priv_min'], all_props['priv_max'], all_props[
'priv_mean'] = [all_props['privacy']] * 3
all_props = all_props.groupby('userid', as_index=False).agg(AGGREGATOR)
all_props['entry_limit'] = entry_limit
all_props = all_props.filter(COLUMNS)
df_list.append(all_props)
write_csv(
all_props.to_csv(header=False, index=False, float_format='%.4f'))
progressbar.close()
return pd.concat(df_list, ignore_index=True)
def test_001(self):
dat = "tests/001.dat"
ans = "tests/001.ans"
main_script(dat, 'output.txt')
self.assertEqual(open(ans).read(), open('output.txt').read())
import main
if __name__ == '__main__':
new_args1 = main.modify_arguments()
# new_args1.dataset_dir = r'C:\Users\Amruta\Desktop\Project\Datasets\CamVid_Norm'
# new_args1.color_space = "GS"
print(new_args1)
main.main_script(new_args1)
