def prepare(mode, inp, out):
""" Transform the given dataset file """
if mode == 'help':
# log("Cognito CLI", color="blue", figlet=True)
click.echo(custom_fig.renderText('cognito'))
if mode == 'prepare':
df = Table(inp)
response, encoder = df.generate()
click.echo(save_to(out, response, encoder))
if mode == 'autoML':
df = Table(inp)
click.echo(df.total_columns())
if mode == 'report':
df = Table(inp)
table = PrettyTable([
'Features', 'Feature Type', 'Outliers', '% of outliers', 'Missing',
'%of missing'
])
for col in df.columns():
table.add_row([col, '', '', '', '', ''])
click.echo(table)
if mode == 'decode':
with trange(11) as t:
for i in t:
t.set_description('C(x) decoding %i' % i)
sleep(0.1)
click.echo('Completed decoding')
click.echo(get_all_files())
def test_table_total_columns_three():
data = Table(
os.path.join(os.path.dirname(__file__), 'data', 'Freedman.csv'))
assert data.total_columns() == 5
def test_table_total_columns_two():
data = Table(os.path.join(os.path.dirname(__file__), 'data',
'student.csv'))
assert data.total_columns() == 4
def test_table_total_columns_one():
data = Table(os.path.join(os.path.dirname(__file__), 'data', 'cereal.csv'))
assert data.total_columns() == 6
