def test_int_nan(self):
data = np.array([1, 2, 1, 2, 1, np.nan])
ct = GaussianCopulaTransformer(dtype=int)
transformed = ct.fit_transform(data)
assert isinstance(transformed, np.ndarray)
assert transformed.shape == (6, 2)
reverse = ct.reverse_transform(transformed)
np.testing.assert_array_almost_equal(reverse, data, decimal=2)
def test_int(self):
data = np.array([1, 2, 1, 2, 1])
ct = GaussianCopulaTransformer(dtype=int)
transformed = ct.fit_transform(data)
assert isinstance(transformed, np.ndarray)
assert transformed.shape == (5, )
reverse = ct.reverse_transform(transformed)
assert list(reverse) == [1, 2, 1, 2, 1]
def test_not_null_column(self):
data = np.array([1, 2, 1, 2, np.nan, 1])
ct = GaussianCopulaTransformer(null_column=False)
transformed = ct.fit_transform(data)
assert isinstance(transformed, np.ndarray)
assert transformed.shape == (6, )
reverse = ct.reverse_transform(transformed)
np.testing.assert_array_almost_equal(reverse, data, decimal=2)
def test_stats(self):
data = np.random.normal(loc=4, scale=4, size=1000)
ct = GaussianCopulaTransformer()
transformed = ct.fit_transform(data)
assert isinstance(transformed, np.ndarray)
assert transformed.shape == (1000, )
np.testing.assert_almost_equal(transformed.mean(), 0, decimal=1)
np.testing.assert_almost_equal(transformed.std(), 1, decimal=1)
reverse = ct.reverse_transform(transformed)
np.testing.assert_array_almost_equal(reverse, data, decimal=1)