def test_dataframe_econding_1D(self):
"""
Test that the encoding/decoding works in 1D
"""
validator = InputValidator()
y = validator.validate_target(
pd.DataFrame(data=self.y, dtype=bool),
is_classification=True,
)
np.testing.assert_array_almost_equal(np.array([0, 1, 0]), y)
# Result should not change on a multi call
y = validator.validate_target(pd.DataFrame(data=self.y, dtype=bool))
np.testing.assert_array_almost_equal(np.array([0, 1, 0]), y)
y_decoded = validator.decode_target(y)
np.testing.assert_array_almost_equal(np.array(self.y, dtype=bool), y_decoded)
# Now go with categorical data
validator = InputValidator()
y = validator.validate_target(
pd.DataFrame(data=['a', 'a', 'b', 'c', 'a'], dtype='category'),
is_classification=True,
)
np.testing.assert_array_almost_equal(np.array([0, 0, 1, 2, 0]), y)
y_decoded = validator.decode_target(y)
self.assertListEqual(['a', 'a', 'b', 'c', 'a'], y_decoded.tolist())
def test_dataframe_econding_2D(self):
"""
Test that the encoding/decoding works in 2D
"""
validator = InputValidator()
multi_label = pd.DataFrame(np.array([[1, 0, 0, 1], [0, 0, 1, 1],
[0, 0, 0, 0]]),
dtype=bool)
y = validator.validate_target(multi_label, is_classification=True)
# Result should not change on a multi call
y_new = validator.validate_target(multi_label)
np.testing.assert_array_almost_equal(y_new, y)
y_decoded = validator.decode_target(y)
np.testing.assert_array_almost_equal(y, y_decoded)