def test___init__super_attrs(self):
"""super() arguments are properly passed and set as attributes."""
nt = NumericalTransformer(dtype='int', nan='mode', null_column=False)
assert nt.dtype == 'int'
assert nt.nan == 'mode'
assert nt.null_column is False
def test_reverse_transform_rounding_none_with_nulls_dtype_int(self):
"""Test ``reverse_transform`` when rounding is None, dtype is int and there are nulls.
The data should be rounded to 0 decimals and returned as float values with
nulls in the right place.
Input:
- 2d Array of multiple float values with decimals and a column setting at least 1 null.
Output:
- First column of the input array rounded, replacing the indicated value with a Nan,
and kept as float values.
"""
# Setup
data = np.array([
[0., 0.],
[1.2, 0.],
[3.45, 1.],
[6.789, 0.],
])
# Run
transformer = NumericalTransformer()
null_transformer = Mock()
null_transformer.reverse_transform.return_value = np.array(
[0., 1.2, np.nan, 6.789])
transformer.null_transformer = null_transformer
transformer._rounding_digits = None
transformer._dtype = np.int
result = transformer.reverse_transform(data)
# Assert
expected = np.array([0., 1., np.nan, 7.])
np.testing.assert_array_equal(result, expected)
def test_reverse_transform_rounding_negative_rounding_float(self):
"""Test ``reverse_transform`` when ``rounding`` is a negative int
The data should round to the number set in the ``_rounding_digits``
attribute and remain floats.
Input:
- Array with with larger numbers
Output:
- Same array rounded to the provided number of 0s
- Array should be of type float
"""
# Setup
data = np.array([2000.0, 120.0, 3100.0, 40100.0])
# Run
transformer = NumericalTransformer(dtype=np.float, nan=None)
transformer._rounding_digits = -3
result = transformer.reverse_transform(data)
# Assert
expected_data = np.array([2000.0, 0.0, 3000.0, 40000.0])
np.testing.assert_array_equal(result, expected_data)
assert result.dtype == np.float
def test_fit_min_max_auto(self):
"""Test fit min and max parameters with ``'auto'``
If the min or max parameters are set to ``'auto'``
the ``fit`` method should learn them from the
fitted data.
Input:
- Array of floats and null values
Side Effect:
- ``_min_value`` and ``_max_value`` are learned
"""
# Setup
data = np.array([-100, -5000, 0, None, 100, 4000])
# Run
transformer = NumericalTransformer(dtype=np.float,
nan='nan',
min_value='auto',
max_value='auto')
transformer.fit(data)
# Asserts
assert transformer._min_value == -5000
assert transformer._max_value == 4000
def test_reverse_transform_min_an_max_with_nulls(self):
"""Test reverse_transform with nulls and ``min_value`` and ``max_value`` set
The ``reverse_transform`` method should clip any values above
the ``max_value`` and any values below the ``min_value``. Null values
should be replaced with ``np.nan``.
Input:
- 2d array where second column has some values over 0.5 representing null values
Output:
- Array with out of bounds values clipped and null values injected
"""
# Setup
data = np.array([[-np.inf, 0], [-5000, 0.1], [-301, 0.8], [-250, 0.4],
[0, 0], [125, 1], [401, 0.2], [np.inf, 0.5]])
clipped_data = np.array([[-300, 0], [-300, 0.1], [-300, 0.8],
[-250, 0.4], [0, 0], [125, 1], [400, 0.2],
[400, 0.5]])
expected_data = np.array(
[-300, -300, np.nan, -250, 0, np.nan, 400, 400])
# Run
transformer = NumericalTransformer(dtype=np.float, nan='nan')
transformer._max_value = 400
transformer._min_value = -300
transformer.null_transformer = Mock()
transformer.null_transformer.reverse_transform.return_value = expected_data
result = transformer.reverse_transform(data)
# Asserts
null_transformer_calls = transformer.null_transformer.reverse_transform.mock_calls
np.testing.assert_array_equal(null_transformer_calls[0][1][0],
clipped_data)
np.testing.assert_array_equal(result, expected_data)
def test_fit_min_max_none(self):
"""Test fit min and max parameters with ``None``
If the min and max parameters are set to ``None``,
the ``fit`` method should not set its ``min`` or ``max``
instance variables.
Input:
- Array of floats and null values
Side Effect:
- ``_min_value`` and ``_max_value`` stay ``None``
"""
# Setup
data = np.array([1.5, None, 2.5])
# Run
transformer = NumericalTransformer(dtype=np.float,
nan='nan',
min_value=None,
max_value=None)
transformer.fit(data)
# Asserts
assert transformer._min_value is None
assert transformer._max_value is None
def test_fit_rounding_auto_max_inf(self):
"""Test fit rounding parameter with ``'auto'``
If the ``rounding`` parameter is set to ``'auto'``,
and the data contains infinite values, ``fit`` should
learn the ``_rounding_digits`` to be the min
number of decimal places seen in the data with
the infinite values filtered out.
Input:
- Array with ``np.inf`` as a value
Side Effect:
- ``_rounding_digits`` is set to max seen in rest of data
"""
# Setup
data = np.array([15000, 4000, 60000, np.inf])
# Run
transformer = NumericalTransformer(dtype=np.float,
nan='nan',
rounding='auto')
transformer.fit(data)
# Asserts
assert transformer._rounding_digits == -3
def test_fit_rounding_auto_large_numbers(self):
"""Test fit rounding parameter with ``'auto'``
If the ``rounding`` parameter is set to ``'auto'``
and the data is very large, ``fit`` should learn
``_rounding_digits`` to be the biggest number of 0s
to round to that keeps the data the same.
Input:
- Array of data with numbers between 10^10 and 10^20
Side Effect:
- ``_rounding_digits`` is set to the minimum exponent seen in the data
"""
# Setup
exponents = [np.random.randint(10, 20) for i in range(10)]
big_numbers = [10**exponents[i] for i in range(10)]
data = np.array(big_numbers)
# Run
transformer = NumericalTransformer(dtype=np.float,
nan='nan',
rounding='auto')
transformer.fit(data)
# Asserts
assert transformer._rounding_digits == -min(exponents)
def test_fit_rounding_auto_max_decimals(self):
"""Test fit rounding parameter with ``'auto'``
If the ``rounding`` parameter is set to ``'auto'``,
``fit`` should learn the ``_rounding_digits`` to be the max
number of decimal places seen in the data. The max
amount of decimals that floats can be accurately compared
with is 15. If the input data has values with more than
14 decimals, we will not be able to accurately learn the
number of decimal places required, so we do not round.
Input:
- Array with a value that has 15 decimals
Side Effect:
- ``_rounding_digits`` is set to ``None``
"""
# Setup
data = np.array([0.000000000000001])
# Run
transformer = NumericalTransformer(dtype=np.float,
nan='nan',
rounding='auto')
transformer.fit(data)
# Asserts
assert transformer._rounding_digits is None
def test_fit_rounding_int(self):
"""Test fit rounding parameter with int
If the rounding parameter is set to ``None``, the ``fit`` method
should not set its ``rounding`` or ``_rounding_digits`` instance
variables.
Input:
- An array with floats rounded to one decimal and a None value
Side Effect:
- ``rounding`` and ``_rounding_digits`` are the provided int
"""
# Setup
data = np.array([1.5, None, 2.5])
expected_digits = 3
# Run
transformer = NumericalTransformer(dtype=np.float,
nan='nan',
rounding=expected_digits)
transformer.fit(data)
# Asserts
assert transformer.rounding == expected_digits
assert transformer._rounding_digits == expected_digits
def test_reverse_transform_rounding_none_with_nulls(self):
"""Test ``reverse_transform`` when ``rounding`` is ``None`` and there are nulls.
The data should not be rounded at all.
Input:
- 2d Array of multiple float values with decimals and a column setting at least 1 null.
Output:
- First column of the input array as entered, replacing the indicated value with a Nan.
"""
# Setup
data = np.array([
[0., 0.],
[1.2, 0.],
[3.45, 1.],
[6.789, 0.],
])
# Run
transformer = NumericalTransformer()
null_transformer = Mock()
null_transformer.reverse_transform.return_value = np.array(
[0., 1.2, np.nan, 6.789])
transformer.null_transformer = null_transformer
transformer._rounding_digits = None
transformer._dtype = np.float
result = transformer.reverse_transform(data)
# Assert
expected = np.array([0., 1.2, np.nan, 6.789])
np.testing.assert_array_equal(result, expected)
def test_int(self):
data = np.array([1, 2, 1, 2, 1])
nt = NumericalTransformer(dtype=int)
transformed = nt.fit_transform(data)
assert isinstance(transformed, np.ndarray)
assert transformed.shape == (5, )
reverse = nt.reverse_transform(transformed)
assert list(reverse) == [1, 2, 1, 2, 1]
def test_int_nan(self):
data = np.array([1, 2, 1, 2, 1, np.nan])
nt = NumericalTransformer(dtype=int)
transformed = nt.fit_transform(data)
assert isinstance(transformed, np.ndarray)
assert transformed.shape == (6, 2)
reverse = nt.reverse_transform(transformed)
np.testing.assert_array_almost_equal(reverse, data, decimal=2)
def test_not_null_column(self):
data = np.array([1, 2, 1, 2, np.nan, 1])
nt = NumericalTransformer(null_column=False)
transformed = nt.fit_transform(data)
assert isinstance(transformed, np.ndarray)
assert transformed.shape == (6, )
reverse = nt.reverse_transform(transformed)
np.testing.assert_array_almost_equal(reverse, data, decimal=2)
def test_fit(self):
"""Test fit nan mean with numpy.array"""
# Setup
data = np.array([1.5, None, 2.5])
# Run
transformer = NumericalTransformer(dtype=np.float, nan='nan')
transformer.fit(data)
# Asserts
expect_fill_value = 'nan'
expect_dtype = np.float
assert transformer.null_transformer.fill_value == expect_fill_value
assert transformer._dtype == expect_dtype
def test_reverse_transform_rounding_none(self):
"""Test ``reverse_transform`` when ``rounding`` is ``None``
The data should not be rounded at all.
Input:
- Random array of floats between 0 and 1
Output:
- Input array
"""
# Setup
data = np.random.random(10)
# Run
transformer = NumericalTransformer(dtype=np.float, nan=None)
transformer._rounding_digits = None
result = transformer.reverse_transform(data)
# Assert
np.testing.assert_array_equal(result, data)
def test_reverse_transform_min_no_max(self):
"""Test reverse_transform with ``min_value`` set
The ``reverse_transform`` method should clip any values below
the ``min_value`` if it is set.
Input:
- Array with values below the min and infinitely high values
Output:
- Array with low values clipped to min
"""
# Setup
data = np.array([-np.inf, -5000, -301, -250, 0, 125, 400, np.inf])
# Run
transformer = NumericalTransformer(dtype=np.float, nan=None)
transformer._min_value = -300
result = transformer.reverse_transform(data)
# Asserts
expected_data = np.array([-300, -300, -300, -250, 0, 125, 400, np.inf])
np.testing.assert_array_equal(result, expected_data)
def test_reverse_transform_rounding_none_integer(self):
"""Test ``reverse_transform`` when ``rounding`` is ``None`` and the dtype is integer.
The data should be rounded to 0 decimals and returned as integer values.
Input:
- Array of multiple float values with decimals.
Output:
- Input array rounded an converted to integers.
"""
# Setup
data = np.array([0., 1.2, 3.45, 6.789])
# Run
transformer = NumericalTransformer(dtype=np.int64, nan=None)
transformer._rounding_digits = None
transformer._dtype = np.int64
result = transformer.reverse_transform(data)
# Assert
expected = np.array([0, 1, 3, 7])
np.testing.assert_array_equal(result, expected)
def test_fit_rounding_auto(self):
"""Test fit rounding parameter with ``'auto'``
If the ``rounding`` parameter is set to ``'auto'``,
``fit`` should learn the ``_rounding_digits`` to be the max
number of decimal places seen in the data.
Input:
- Array of floats with up to 4 decimals
Side Effect:
- ``_rounding_digits`` is set to 4
"""
# Setup
data = np.array([1, 2.1, 3.12, 4.123, 5.1234, 6.123, 7.12, 8.1, 9])
# Run
transformer = NumericalTransformer(dtype=np.float,
nan='nan',
rounding='auto')
transformer.fit(data)
# Asserts
assert transformer._rounding_digits == 4
def test_fit_rounding_auto_max_zero(self):
"""Test fit rounding parameter with ``'auto'``
If the ``rounding`` parameter is set to ``'auto'``,
and the max in the data is 0, ``fit`` should
learn the ``_rounding_digits`` to be 0.
Input:
- Array with 0 as max value
Side Effect:
- ``_rounding_digits`` is set to 0
"""
# Setup
data = np.array([0, 0, 0])
# Run
transformer = NumericalTransformer(dtype=np.float,
nan='nan',
rounding='auto')
transformer.fit(data)
# Asserts
assert transformer._rounding_digits == 0
def test_reverse_transform_min_and_max(self):
"""Test reverse_transform with ``min_value`` and ``max_value`` set
The ``reverse_transform`` method should clip any values above
the ``max_value`` and any values below the ``min_value``.
Input:
- Array with values above the max and below the min
Output:
- Array with out of bound values clipped to min and max
"""
# Setup
data = np.array([-np.inf, -5000, -301, -250, 0, 125, 401, np.inf])
# Run
transformer = NumericalTransformer(dtype=np.float, nan=None)
transformer._max_value = 400
transformer._min_value = -300
result = transformer.reverse_transform(data)
# Asserts
np.testing.assert_array_equal(
result, np.array([-300, -300, -300, -250, 0, 125, 400, 400]))
def test_reverse_transform_rounding_zero(self):
"""Test ``reverse_transform`` when ``rounding`` is a negative int
The data should round to the number set in the ``_rounding_digits``
attribute.
Input:
- Array with with larger numbers
Output:
- Same array rounded to the provided number of 0s
"""
# Setup
data = np.array([2000.554, 120.2, 3101, 4010])
# Run
transformer = NumericalTransformer(dtype=np.float, nan=None)
transformer._rounding_digits = 0
result = transformer.reverse_transform(data)
# Assert
expected_data = np.array([2001, 120, 3101, 4010])
np.testing.assert_array_equal(result, expected_data)
def test_reverse_transform_rounding_positive_rounding(self):
"""Test ``reverse_transform`` when ``rounding`` is a positive int
The data should round to the maximum number of decimal places
set in the ``_rounding_digits`` value.
Input:
- Array with decimals
Output:
- Same array rounded to the provided number of decimal places
"""
# Setup
data = np.array([1.1111, 2.2222, 3.3333, 4.44444, 5.555555])
# Run
transformer = NumericalTransformer(dtype=np.float, nan=None)
transformer._rounding_digits = 2
result = transformer.reverse_transform(data)
# Assert
expected_data = np.array([1.11, 2.22, 3.33, 4.44, 5.56])
np.testing.assert_array_equal(result, expected_data)
def test_fit_rounding_none(self):
"""Test fit rounding parameter with ``None``
If the rounding parameter is set to ``None``, the ``fit`` method
should not set its ``rounding`` or ``_rounding_digits`` instance
variables.
Input:
- An array with floats rounded to one decimal and a None value
Side Effect:
- ``rounding`` and ``_rounding_digits`` continue to be ``None``
"""
# Setup
data = np.array([1.5, None, 2.5])
# Run
transformer = NumericalTransformer(dtype=np.float,
nan='nan',
rounding=None)
transformer.fit(data)
# Asserts
assert transformer.rounding is None
assert transformer._rounding_digits is None
def test_fit_rounding_auto_max_negative(self):
"""Test fit rounding parameter with ``'auto'``
If the ``rounding`` parameter is set to ``'auto'``,
and the max in the data is negative, the ``fit`` method
should learn ``_rounding_digits`` to be the min number
of digits seen in those negative values.
Input:
- Array with negative max value
Side Effect:
- ``_rounding_digits`` is set to min number of digits in array
"""
# Setup
data = np.array([-500, -220, -10])
# Run
transformer = NumericalTransformer(dtype=np.float,
nan='nan',
rounding='auto')
transformer.fit(data)
# Asserts
assert transformer._rounding_digits == -1
def test_fit_min_max_int(self):
"""Test fit min and max parameters with int values
If the min and max parameters are set to an int,
the ``fit`` method should not change them.
Input:
- Array of floats and null values
Side Effect:
- ``_min_value`` and ``_max_value`` remain unchanged
"""
# Setup
data = np.array([1.5, None, 2.5])
# Run
transformer = NumericalTransformer(dtype=np.float,
nan='nan',
min_value=1,
max_value=10)
transformer.fit(data)
# Asserts
assert transformer._min_value == 1
assert transformer._max_value == 10