def test_bin_indexes_datetimes():
dates = ['05/29/1988', '06/22/1988', '07/30/1992', '07/30/1992',
'11/12/2000', '01/02/2001', '01/02/2001', '12/03/2001',
'07/09/2002', '10/22/2002']
attr = Attribute(Series(dates, name='DateTime'))
indexes = attr.bin_indexes()
assert len(indexes) == len(dates)
def test_random_datetimes():
datetimes = ['07/15/2019', '07/24/2019', '07/23/2019', '07/22/2019',
'07/21/2019', '07/22/2019', '07/23/2019', '07/24/2019',
'07/23/2019', '07/22/2019', '07/15/2019']
attr = Attribute(datetimes, name='Date')
randoms = attr.random()
assert len(randoms) == len(datetimes)
def test_choice_datetimes():
dates = ['05/29/1988', '06/22/1988', '07/30/1992', '01/02/2001',
'11/12/2000', '07/09/2002', '08/30/1998', '06/03/1997',
'10/22/2002', '12/03/2001']
attr = Attribute(Series(dates, name='DateTime'))
choices = attr.choice()
assert len(choices) == len(dates)
def test_encode_datetime_attributes():
from pandas import DataFrame
from .testdata import adults01
frame = DataFrame(adults01)
attr = Attribute(frame['birth'])
# assert other information
assert len(attr.encode()) == len(attr)
def test_to_pseudonym_dates():
ints = Series(['07/15/2019', '07/24/2019', '07/23/2019', '07/22/2019',
'07/21/2019', '07/22/2019', '07/23/2019', '07/24/2019',
'07/23/2019', '07/22/2019', '07/15/2019'])
attr = Attribute(ints, name='Date')
pseudonyms = attr.pseudonymize()
assert array_equal(ints.value_counts().values,
pseudonyms.value_counts().values)
def test_retain_ints():
ints = [3, 5, 7, 8, 7, 1, 10, 30, 16, 19]
attr = Attribute(ints, name='Integer')
retains = attr.retain()
assert len(retains) == len(ints)
retains = attr.retain(size=15)
assert array_equal(retains.head(len(ints)).tolist(), ints)
def test_set_domain_for_integer_attribute():
ints = random.randint(1, 100, size)
attr = Attribute(Series(ints, name='Integer'))
assert attr._min >= 1
assert attr._max <= 100
attr.domain = [-2, 120]
assert attr._min == -2
assert attr._max == 120
def test_set_domain_for_integer_categorical_attribute():
ints = random.randint(1, 100, size)
attr = Attribute(Series(ints, name='Integer'), categorical=True)
assert attr.bins[0] >= 1
assert attr.bins[-1] <= 100
attr.domain = [-2, 120]
assert attr.bins[0] == -2
assert attr.bins[-1] == 120
def test_set_domain_for_float_attribute():
floats = random.uniform(1, 100, size)
attr = Attribute(Series(floats, name='Float'))
assert attr._min >= 1
assert attr._max <= 100
attr.domain = [-2, 120]
assert attr._min == -2
assert attr._max == 120
def test_set_domain_for_datetime_attribute():
dates = ['05/29/1988', '06/22/1988', '07/30/1992', '07/30/1992',
'11/12/2000', '01/02/2001', '01/02/2001', '12/03/2001',
'07/09/2002', '10/22/2002']
attr = Attribute(Series(dates, name='String'), categorical=True)
bins = attr.bins
attr.domain = ['07/01/1997', '12/20/1999', '01/01/2004']
assert len(bins) + 3 == len(attr.bins)
def test_encode_categorical_attributes():
from pandas import DataFrame
from .testdata import adults01
frame = DataFrame(adults01)
attr = Attribute(frame['education'], categorical=True)
columns = ['11th', '7th-8th', '9th', 'Assoc-acdm', 'Bachelors', 'Doctorate',
'HS-grad', 'Masters', 'Some-college']
assert array_equal(attr.bins, columns)
assert array_equal(attr.encode().columns, columns)
def test_encode_numerical_attributes():
from .testdata import adults01
attr = Attribute(adults01['age'])
assert attr.bins[0] <= 19
assert attr.bins[-1] >= 56
assert len(attr.encode()) == len(attr)
from sklearn.model_selection import train_test_split
train, test = train_test_split(adults01['age'])
assert len(attr.encode(data=train)) == len(train)
def test_counts_datetimes():
dates = ['05/29/1988', '06/22/1988', '07/30/1992', '07/30/1992',
'11/12/2000', '01/02/2001', '01/02/2001', '12/03/2001',
'07/09/2002', '10/22/2002']
attr = Attribute(Series(dates, name='DateTime'), categorical=True)
counts = attr.counts(normalize=False)
assert sum(counts) == len(dates)
assert array_equal(counts, [1, 1, 2, 1, 2, 1, 1, 1])
counts = attr.counts(bins=['12/03/2001', '10/22/2002'], normalize=False)
assert array_equal(counts, [1, 1])
def test_integer_attribute():
ints = random.randint(1, 100, size)
attr = Attribute(Series(ints), name='ID', categorical=False)
assert attr.atype == 'integer'
assert attr.name == 'ID'
assert attr._min >= 1
assert attr._max <= 100
assert len(attr.bins) == 20
assert isclose(sum(attr.prs), 1.0)
from .testdata import adults01
attr = Attribute(adults01['age'])
assert attr.atype == 'integer'
def test_float_attribute():
floats = random.uniform(1, 100, size)
attr = Attribute(Series(floats, name='Float'))
assert attr.atype == 'float'
assert attr._min >= 1
assert attr._max <= 100
assert len(attr.bins) == 20
assert isclose(sum(attr.prs), 1.0)
def test_counts_numerical_attribute():
ints = random.randint(1, 100, size)
attr = Attribute(Series(ints, name='Integer'))
counts = attr.counts(normalize=False)
assert sum(counts) == 30
assert len(counts) == 20
counts = attr.counts(bins=[0, 10, 20, 30, 100], normalize=False)
assert sum(counts) == 30
assert len(counts) == 4
# categorical ints
attr = Attribute(Series([1, 10, 11, 10, 20, 15, 16, 25], name='Integer'),
categorical=True)
counts = attr.counts(normalize=False)
assert sum(counts) == 8
assert len(counts) == 7
counts = attr.counts(bins=[5, 10, 15], normalize=False)
assert sum(counts) == 3
assert len(counts) == 3
def test_choice_integers():
ints = random.randint(1, 100, size)
attr = Attribute(Series(ints, name='Integer'))
assert len(attr.bins) == 20
choices = attr.choice()
assert len(choices) == size
def test_set_domain_for_string_attribute():
strings = list(map(lambda x: randomize_string(5), range(size)))
attr = Attribute(Series(strings, name='String'), categorical=True)
bins = attr.bins
attr.domain = ['a', 'b', 'China', 'USA']
assert len(bins) + 4 == len(attr.bins)
def test_counts_categorical_attribute():
ints = random.randint(1, 10, size)
attr = Attribute(Series(ints, name='Integer'), categorical=True)
assert sum(attr.counts()) == 30
def test_bin_indexes_ints():
ints = [3, 5, 7, 8, 7, 1, 10, 30, 16, 19]
attr = Attribute(Series(ints), name='ID', categorical=False)
indexes = attr.bin_indexes()
assert len(indexes) == len(ints)
def test_pseudonymize_strings():
strings = Series(['Abc', 'edf', 'Abc', 'take', '中国', 'edf', 'Abc'])
attr = Attribute(strings, name='String')
pseudonyms = attr.pseudonymize()
assert array_equal(strings.value_counts().values,
pseudonyms.value_counts().values)
def test_string_attribute():
strings = list(map(lambda x: randomize_string(5), range(size)))
attr = Attribute(Series(strings, name='String'), categorical=True)
assert attr.atype == 'string'
assert attr._min == 5
assert attr.categorical
def test_pseudonymize_ints():
ints = Series([11, 2, 3, 4, 5, 4, 3, 2, 3, 4, 11])
attr = Attribute(ints, name='Integer')
pseudonyms = attr.pseudonymize()
assert array_equal(ints.value_counts().values,
pseudonyms.value_counts().values)
def test_pseudonymize_floats():
floats = Series([11.5, 2.6, 3.0, 4.3, 5, 4.3, 3.0, 2.6, 3.0, 4.3, 11.6])
attr = Attribute(floats, name='Float')
pseudonyms = attr.pseudonymize()
assert array_equal(floats.value_counts().values,
pseudonyms.value_counts().values)
def test_decimals_float_attribute():
floats = map(lambda v: round(v, 2), random.uniform(1, 10, size))
attr = Attribute(Series(floats, name='Float'))
assert attr.decimals() == 2
def test_choice_strings():
strings = list(map(lambda x: randomize_string(5), range(size)))
attr = Attribute(Series(strings, name='String'))
choices = attr.choice()
assert len(choices) == size
def test_random_ints():
ints = [3, 5, 7, 8, 7, 1, 10, 30, 16, 19]
attr = Attribute(ints, name='Integer')
randoms = attr.random()
assert len(randoms) == len(ints)
def test_random_strings():
strings = list(map(lambda x: randomize_string(5), range(size)))
attr = Attribute(Series(strings, name='String'))
randoms = attr.random()
assert len(randoms) == size
def test_choice_floats():
floats = random.uniform(1, 10, size)
attr = Attribute(Series(floats, name='Float'))
assert len(attr.bins) == 20
choices = attr.choice()
assert len(choices) == size
