def test_serialization_fit_model(self):
# Setup
instance = Tree(TreeTypes.REGULAR)
X = pd.DataFrame(data=[
[1, 0, 0],
[0, 1, 0],
[0, 0, 1]
])
index = 0
n_nodes = X.shape[1]
tau_matrix = X.corr(method='kendall').values
univariates_matrix = np.empty(X.shape)
for i, column in enumerate(X):
distribution = GaussianKDE()
distribution.fit(X[column])
univariates_matrix[:, i] = distribution.cumulative_distribution(X[column])
instance.fit(index, n_nodes, tau_matrix, univariates_matrix)
# Run
result = Tree.from_dict(instance.to_dict())
# Check
assert result.to_dict() == instance.to_dict()
def test_serialization_unfitted_model(self):
# Setup
instance = Tree(TreeTypes.REGULAR)
# Run
result = Tree.from_dict(instance.to_dict())
# Check
assert instance.to_dict() == result.to_dict()
def test_to_dict_unfitted_model(self):
# Setup
instance = Tree(TreeTypes.REGULAR)
expected_result = {
'type': 'copulas.multivariate.tree.RegularTree',
'tree_type': TreeTypes.REGULAR,
'fitted': False
}
# Run
result = instance.to_dict()
# Check
assert result == expected_result
def test_to_dict_fit_model(self):
# Setup
instance = Tree(TreeTypes.REGULAR)
X = pd.DataFrame(data=[
[1, 0, 0],
[0, 1, 0],
[0, 0, 1]
])
index = 0
n_nodes = X.shape[1]
tau_matrix = X.corr(method='kendall').values
univariates_matrix = np.empty(X.shape)
for i, column in enumerate(X):
distribution = GaussianKDE()
distribution.fit(X[column])
univariates_matrix[:, i] = distribution.cumulative_distribution(X[column])
instance.fit(index, n_nodes, tau_matrix, univariates_matrix)
expected_result = {
'type': 'copulas.multivariate.tree.RegularTree',
'fitted': True,
'level': 1,
'n_nodes': 3,
'previous_tree': [
[0.8230112726144534, 0.3384880496294825, 0.3384880496294825],
[0.3384880496294825, 0.8230112726144534, 0.3384880496294825],
[0.3384880496294825, 0.3384880496294825, 0.8230112726144534]
],
'tau_matrix': [
[1.0, -0.49999999999999994, -0.49999999999999994],
[-0.49999999999999994, 1.0, -0.49999999999999994],
[-0.49999999999999994, -0.49999999999999994, 1.0]
],
'tree_type': TreeTypes.REGULAR,
'edges': [
{
'index': 0,
'D': set(),
'L': 0,
'R': 1,
'U': [
[0.7969535322648066, 0.6887525261721343, 0.12077958383821545],
[0.6887525261721343, 0.7969535322648066, 0.12077958383821545]
],
'likelihood': None,
'name': CopulaTypes.FRANK,
'neighbors': [],
'parents': None,
'tau': -0.49999999999999994,
'theta': -5.736282443655552
},
{
'index': 1,
'D': set(),
'L': 1,
'R': 2,
'U': [
[0.12077958383821545, 0.7969535322648066, 0.6887525261721343],
[0.12077958383821545, 0.6887525261721343, 0.7969535322648066]
],
'likelihood': None,
'name': CopulaTypes.FRANK,
'neighbors': [],
'parents': None,
'tau': -0.49999999999999994,
'theta': -5.736282443655552
}
],
}
# Run
result = instance.to_dict()
# Check
compare_nested_dicts(result, expected_result)
def test_to_dict_fit_model(self):
# Setup
instance = Tree(TreeTypes.REGULAR)
X = pd.DataFrame(data=[
[1, 0, 0],
[0, 1, 0],
[0, 0, 1]
])
index = 0
n_nodes = X.shape[1]
tau_matrix = X.corr(method='kendall').values
univariates_matrix = np.empty(X.shape)
for i, column in enumerate(X):
distribution = KDEUnivariate()
distribution.fit(X[column])
univariates_matrix[:, i] = [distribution.cumulative_distribution(x) for x in X[column]]
instance.fit(index, n_nodes, tau_matrix, univariates_matrix)
expected_result = {
'type': 'copulas.multivariate.tree.RegularTree',
'fitted': True,
'level': 1,
'n_nodes': 3,
'previous_tree': [
[0.8230112726144534, 0.3384880496294825, 0.3384880496294825],
[0.3384880496294825, 0.8230112726144534, 0.3384880496294825],
[0.3384880496294825, 0.3384880496294825, 0.8230112726144534]
],
'tau_matrix': [
[1.0, -0.49999999999999994, -0.49999999999999994],
[-0.49999999999999994, 1.0, -0.49999999999999994],
[-0.49999999999999994, -0.49999999999999994, 1.0]
],
'tree_type': TreeTypes.REGULAR,
'edges': [
{
'D': set(),
'L': 0,
'R': 1,
'U': [
[6.533235975920359, 6.425034969827687, 5.857062027493768],
[6.425034969827687, 6.533235975920359, 5.857062027493768]
],
'likelihood': None,
'name': CopulaTypes.FRANK,
'neighbors': [],
'parents': None,
'tau': -0.49999999999999994,
'theta': -5.736282443655552
},
{
'D': set(),
'L': 1,
'R': 2,
'U': [
[5.857062027493768, 6.533235975920359, 6.425034969827687],
[5.857062027493768, 6.425034969827687, 6.533235975920359]
],
'likelihood': None,
'name': CopulaTypes.FRANK,
'neighbors': [],
'parents': None,
'tau': -0.49999999999999994,
'theta': -5.736282443655552
}
],
}
# Run
result = instance.to_dict()
# Check
compare_nested_dicts(result, expected_result)
