def test__unflatten_gaussian_copula(self):
"""Test unflatte gaussian copula"""
# Setup
sampler = Mock(autospec=Sampler)
sampler._prepare_sampled_covariance.return_value = [[0.4, 0.2],
[0.2, 0.0]]
# Run
model_parameters = {
'distribs': {
'foo': {
'std': 0.5
}
},
'covariance': [[0.4, 0.1], [0.1]],
'distribution': 'GaussianUnivariate'
}
result = Sampler._unflatten_gaussian_copula(sampler, model_parameters)
# Asserts
expected = {
'distribs': {
'foo': {
'fitted': True,
'std': 1.6487212707001282,
'type': 'GaussianUnivariate'
}
},
'distribution': 'GaussianUnivariate',
'covariance': [[0.4, 0.2], [0.2, 0.0]]
}
assert result == expected
def test__unflatten_gaussian_copula(self):
"""_unflatten_gaussian_copula add the distribution, type and fitted kwargs."""
# Setup
data_navigator = MagicMock()
modeler = MagicMock()
modeler.model_kwargs = {
'distribution': 'distribution_name'
}
sampler = Sampler(data_navigator, modeler)
model_parameters = {
'some': 'key',
'covariance': [
[1],
[0, 1]
],
'distribs': {
0: {
'first': 'distribution',
'std': 0
},
1: {
'second': 'distribution',
'std': 0
}
}
}
expected_result = {
'some': 'key',
'distribution': 'distribution_name',
'covariance': [
[1, 0],
[0, 1]
],
'distribs': {
0: {
'type': 'distribution_name',
'fitted': True,
'first': 'distribution',
'std': 1
},
1: {
'type': 'distribution_name',
'fitted': True,
'second': 'distribution',
'std': 1
}
}
}
# Run
result = sampler._unflatten_gaussian_copula(model_parameters)
# Check
assert result == expected_result
data_navigator.assert_not_called()
modeler.assert_not_called()
def test__unflatten_gaussian_copula_negative_std(self):
"""_unflatten_gaussian_copula will transform negative or 0 std into positive."""
# Setup
data_navigator = MagicMock()
modeler = MagicMock()
modeler.model_kwargs = {
'distribution': 'distribution_name'
}
sampler = Sampler(data_navigator, modeler)
model_parameters = {
'some': 'key',
'covariance': [
[1],
[0, 1]
],
'distribs': {
0: {
'first': 'distribution',
'std': 0
},
1: {
'second': 'distribution',
'std': -1
}
}
}
expected_result = {
'some': 'key',
'distribution': 'distribution_name',
'covariance': [
[1, 0],
[0, 1]
],
'distribs': {
0: {
'type': 'distribution_name',
'fitted': True,
'first': 'distribution',
'std': 1
},
1: {
'type': 'distribution_name',
'fitted': True,
'second': 'distribution',
'std': np.exp(-1)
}
}
}
# Run
result = sampler._unflatten_gaussian_copula(model_parameters)
# Check
assert result == expected_result
data_navigator.assert_not_called()
modeler.assert_not_called()
def test__unflatten_gaussian_copula_not_matrix_symmetric(self):
"""unflatte with not matrix symmetric"""
# Setup
model_parameters = {
'some': 'key',
'covariance': [[1], [0, 1]],
'distribs': {
0: {
'first': 'distribution',
'std': 0
},
1: {
'second': 'distribution',
'std': 0
}
}
}
modeler_mock = Mock()
modeler_mock.model_kwargs = {'distribution': 'distribution_name'}
prepare_mock = Mock()
prepare_mock.return_value = [[1], [0, 1]]
check_mock = Mock()
check_mock.return_value = False
make_mock = Mock()
make_mock.return_value = np.array([[1, 0], [0, 1]])
# Run
sampler_mock = Mock()
sampler_mock.modeler = modeler_mock
sampler_mock._prepare_sampled_covariance = prepare_mock
sampler_mock._check_matrix_symmetric_positive_definite = check_mock
sampler_mock._make_positive_definite = make_mock
result = Sampler._unflatten_gaussian_copula(sampler_mock,
model_parameters)
# Asserts
assert result['covariance'] == [[1, 0], [0, 1]]
prepare_mock.assert_called_once_with([[1], [0, 1]])
check_mock.assert_called_once_with([[1], [0, 1]])
make_mock.assert_called_once_with([[1], [0, 1]])
