def test_serialization_unfitted_model(self):
# Setup
instance = VineCopula('regular')
# Run
result = VineCopula.from_dict(instance.to_dict())
# Check
assert result.to_dict() == instance.to_dict()
def test_serialization_unfitted_model(self):
"""An unfitted vine can be serialized and deserialized and kept unchanged."""
# Setup
instance = VineCopula('regular')
# Run
result = VineCopula.from_dict(instance.to_dict())
# Check
assert result.to_dict() == instance.to_dict()
def test_serialization_fit_model(self):
"""A fitted vine can be serialized and deserialized and kept unchanged."""
# Setup
instance = VineCopula('regular')
X = pd.DataFrame(data=[[1, 0, 0], [0, 1, 0], [0, 0, 1]])
instance.fit(X)
# Run
result = VineCopula.from_dict(instance.to_dict())
# Check
compare_nested_dicts(result.to_dict(), instance.to_dict())
def test_serialization_fit_model(self):
# Setup
instance = VineCopula('regular')
X = pd.DataFrame(data=[
[1, 0, 0],
[0, 1, 0],
[0, 0, 1]
])
instance.fit(X)
# Run
result = VineCopula.from_dict(instance.to_dict())
# Check
compare_nested_dicts(result.to_dict(), instance.to_dict())
def test_from_dict(self):
"""from_dict creates a new instance from its parameters."""
# Setup
vine_dict = {
'type':
'copulas.multivariate.vine.VineCopula',
'vine_type':
'regular',
'fitted':
True,
'n_sample':
100,
'n_var':
10,
'depth':
3,
'truncated':
3,
'trees': [{
'type': 'copulas.multivariate.tree.RegularTree',
'tree_type': 'regular',
'fitted': False
}],
'tau_mat': [[0, 1], [1, 0]],
'u_matrix': [[0, 1], [1, 0]],
'unis': [{
'type': 'copulas.univariate.gaussian_kde.GaussianKDE',
'fitted': False,
'constant_value': None
}]
}
# Run
instance = VineCopula.from_dict(vine_dict)
# Check
assert instance.vine_type == 'regular'
assert instance.n_sample == 100
assert instance.n_var == 10
assert instance.depth == 3
assert instance.truncated == 3
assert len(instance.trees) == 1
assert instance.trees[0].to_dict() == Tree('regular').to_dict()
assert (instance.tau_mat == np.array([[0, 1], [1, 0]])).all()
assert (instance.u_matrix == np.array([[0, 1], [1, 0]])).all()
