def test__transform_to_normal_numpy_1d(self):
# Setup
gm = GaussianMultivariate()
dist_a = Mock()
dist_a.cdf.return_value = np.array([0])
dist_b = Mock()
dist_b.cdf.return_value = np.array([0.3])
gm.columns = ['a', 'b']
gm.univariates = [dist_a, dist_b]
# Run
data = np.array([
[3, 5],
])
returned = gm._transform_to_normal(data)
# Check
# Failures may occurr on different cpytonn implementations
# with different float precision values.
# If that happens, atol might need to be increased
expected = np.array([
[-5.166579, -0.524401],
])
np.testing.assert_allclose(returned, expected, atol=1e-6)
assert dist_a.cdf.call_count == 1
expected = np.array([3])
passed = dist_a.cdf.call_args[0][0]
np.testing.assert_allclose(expected, passed)
assert dist_b.cdf.call_count == 1
expected = np.array([5])
passed = dist_b.cdf.call_args[0][0]
np.testing.assert_allclose(expected, passed)
def test__transform_to_normal_dataframe(self):
# Setup
gm = GaussianMultivariate()
dist_a = Mock()
dist_a.cdf.return_value = np.array([0, 0.5, 1])
dist_b = Mock()
dist_b.cdf.return_value = np.array([0.3, 0.5, 0.7])
gm.distribs = OrderedDict((
('a', dist_a),
('b', dist_b),
))
# Run
data = pd.DataFrame({'a': [3, 4, 5], 'b': [5, 6, 7]})
returned = gm._transform_to_normal(data)
# Check
# Failures may occurr on different cpytonn implementations
# with different float precision values.
# If that happens, atol might need to be increased
expected = np.array([[-5.166579, -0.524401], [0.0, 0.0],
[5.166579, 0.524401]])
np.testing.assert_allclose(returned, expected, atol=1e-6)
assert dist_a.cdf.call_count == 1
expected = np.array([3, 4, 5])
passed = dist_a.cdf.call_args[0][0]
np.testing.assert_allclose(expected, passed)
assert dist_b.cdf.call_count == 1
expected = np.array([5, 6, 7])
passed = dist_b.cdf.call_args[0][0]
np.testing.assert_allclose(expected, passed)
