def test__transform_single_categorical(self):
"""Test the ``_transform`` with one category.
The values passed to ``_transform`` should be
returned in a one-hot encoding representation
using the categorical branch where it should
be a single column.
Input:
- Series with a single category
Output:
- one-hot encoding of the input
"""
# Setup
ohet = OneHotEncodingTransformer()
data = pd.Series(['a', 'a', 'a'])
ohet.dummies = ['a']
ohet.indexer = [0]
ohet.num_dummies = 1
ohet.dummy_encoded = True
# Run
out = ohet._transform(data)
# Assert
expected = np.array([[1], [1], [1]])
np.testing.assert_array_equal(out, expected)
def test__transform_zeros_categorical(self):
"""Test the ``_transform`` with unknown category.
The values passed to ``_transform`` should be
returned in a one-hot encoding representation
using the categorical branch where it should
be a column of zeros.
Input:
- Series with categorical and unknown values
Output:
- one-hot encoding of the input
"""
# Setup
ohet = OneHotEncodingTransformer()
pd.Series(['a'])
ohet.dummies = ['a']
ohet.indexer = [0]
ohet.num_dummies = 1
ohet.dummy_encoded = True
# Run
out = ohet._transform(pd.Series(['b', 'b', 'b']))
# Assert
expected = np.array([[0], [0], [0]])
np.testing.assert_array_equal(out, expected)
def test__transform_nans_categorical(self):
"""Test the ``_transform`` method with nans.
The values passed to ``_transform`` should be
returned in a one-hot encoding representation using
the categorical branch. Null values should be
represented by the same encoding.
Input:
- Series with categorical values containing nans
Output:
- one-hot encoding of the input
"""
# Setup
ohet = OneHotEncodingTransformer()
data = pd.Series([np.nan, None, 'a', 'b'])
ohet.dummies = ['a', 'b']
ohet.indexer = [0, 1]
ohet.dummy_na = True
ohet.num_dummies = 2
ohet.dummy_encoded = True
# Run
out = ohet._transform(data)
# Assert
expected = np.array([[0, 0, 1], [0, 0, 1], [1, 0, 0], [0, 1, 0]])
np.testing.assert_array_equal(out, expected)