def test_reverse_transform_float(self):
"""Test the ``GreaterThan.reverse_transform`` method for dtype float.
The ``GreaterThan.reverse_transform`` method is expected to:
- apply an exponential to the input
- subtract 1
- add the low column
- convert the output to float values
Input:
- Table with a high column that contains the constant np.log(4).
Output:
- Same table with the high column replaced by the low one + 3, as float values.
"""
# Setup
instance = GreaterThan(low='a', high='b', strict=True)
instance._dtype = np.dtype('float')
# Run
transformed = pd.DataFrame({
'a': [1.1, 2.2, 3.3],
'b': [np.log(4)] * 3,
'c': [7, 8, 9]
})
out = instance.reverse_transform(transformed)
# Assert
expected_out = pd.DataFrame({
'a': [1.1, 2.2, 3.3],
'b': [4.1, 5.2, 6.3],
'c': [7, 8, 9],
})
pd.testing.assert_frame_equal(out, expected_out)
def test_reverse_transform_datetime(self):
"""Test the ``GreaterThan.reverse_transform`` method for dtype datetime.
The ``GreaterThan.reverse_transform`` method is expected to:
- apply an exponential to the input
- subtract 1
- convert the distance to a timedelta
- add the low column
- convert the output to datetimes
Input:
- Table with a high column that contains the constant np.log(1_000_000_001).
Output:
- Same table with the high column replaced by the low one + one second.
"""
# Setup
instance = GreaterThan(low='a', high='b', strict=True)
instance._dtype = np.dtype('<M8[ns]')
# Run
transformed = pd.DataFrame({
'a':
pd.to_datetime(['2020-01-01T00:00:00', '2020-01-02T00:00:00']),
'b': [np.log(1_000_000_001),
np.log(1_000_000_001)],
'c': [1, 2]
})
def test_reverse_transform_int(self):
"""Test the ``GreaterThan.reverse_transform`` method for dtype int.
The ``GreaterThan.reverse_transform`` method is expected to:
- apply an exponential to the input
- subtract 1
- add the low column
- convert the output to integers
Input:
- Table with a high column that contains the constant np.log(4).
Output:
- Same table with the high column replaced by the low one + 3, as int.
"""
# Setup
instance = GreaterThan(low='a', high='b', strict=True)
instance._dtype = pd.Series(
[1]).dtype # exact dtype (32 or 64) depends on OS
# Run
transformed = pd.DataFrame({
'a': [1, 2, 3],
'b': [np.log(4)] * 3,
'c': [7, 8, 9]
})
out = instance.reverse_transform(transformed)
# Assert
expected_out = pd.DataFrame({
'a': [1, 2, 3],
'b': [4, 5, 6],
'c': [7, 8, 9],
})
pd.testing.assert_frame_equal(out, expected_out)