def test_to_dict(self):
"""To_dict returns the parameters to replicate the copula."""
# Setup
copula = GaussianMultivariate()
data = pd.read_csv('data/iris.data.csv')
copula.fit(data)
covariance = [[
1.006711409395973, -0.11010327176239865, 0.8776048563471857,
0.823443255069628
],
[
-0.11010327176239865, 1.006711409395972,
-0.4233383520816991, -0.3589370029669186
],
[
0.8776048563471857, -0.4233383520816991,
1.006711409395973, 0.9692185540781536
],
[
0.823443255069628, -0.3589370029669186,
0.9692185540781536, 1.0067114093959735
]]
expected_result = {
'covariance': covariance,
'fitted': True,
'type': 'copulas.multivariate.gaussian.GaussianMultivariate',
'distribution': 'copulas.univariate.gaussian.GaussianUnivariate',
'distribs': {
'feature_01': {
'type': 'copulas.univariate.gaussian.GaussianUnivariate',
'mean': 5.843333333333334,
'std': 0.8253012917851409,
'fitted': True,
},
'feature_02': {
'type': 'copulas.univariate.gaussian.GaussianUnivariate',
'mean': 3.0540000000000003,
'std': 0.4321465800705435,
'fitted': True,
},
'feature_03': {
'type': 'copulas.univariate.gaussian.GaussianUnivariate',
'mean': 3.758666666666666,
'std': 1.7585291834055212,
'fitted': True,
},
'feature_04': {
'type': 'copulas.univariate.gaussian.GaussianUnivariate',
'mean': 1.1986666666666668,
'std': 0.7606126185881716,
'fitted': True,
}
}
}
# Run
result = copula.to_dict()
# Check
compare_nested_dicts(result, expected_result)
def test_to_dict(self, kde_mock):
"""To_dict returns the defining parameters of a distribution in a dict."""
# Setup
column = np.array([[
0.4967141530112327, -0.13826430117118466, 0.6476885381006925,
1.5230298564080254, -0.23415337472333597, -0.23413695694918055,
1.5792128155073915, 0.7674347291529088, -0.4694743859349521,
0.5425600435859647
]])
kde_instance_mock = kde_mock.return_value
kde_instance_mock.dataset = column
kde_instance_mock.resample.return_value = column
distribution = GaussianKDE()
distribution.fit(column)
expected_result = {
'type':
'copulas.univariate.gaussian_kde.GaussianKDE',
'fitted':
True,
'dataset': [[
0.4967141530112327, -0.13826430117118466, 0.6476885381006925,
1.5230298564080254, -0.23415337472333597, -0.23413695694918055,
1.5792128155073915, 0.7674347291529088, -0.4694743859349521,
0.5425600435859647
]],
}
# Run
result = distribution.to_dict()
# Check
compare_nested_dicts(result, expected_result)
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_save(self, json_mock):
"""Save stores the internal dictionary as a json in a file."""
# Setup
instance = GaussianMultivariate()
data = pd.read_csv('data/iris.data.csv')
instance.fit(data)
covariance = [[
1.006711409395973, -0.11010327176239865, 0.8776048563471857,
0.823443255069628
],
[
-0.11010327176239865, 1.006711409395972,
-0.4233383520816991, -0.3589370029669186
],
[
0.8776048563471857, -0.4233383520816991,
1.006711409395973, 0.9692185540781536
],
[
0.823443255069628, -0.3589370029669186,
0.9692185540781536, 1.0067114093959735
]]
parameters = {
'covariance': covariance,
'distribs': {
'feature_01': {
'mean': 5.843333333333334,
'std': 0.8253012917851409
},
'feature_02': {
'mean': 3.0540000000000003,
'std': 0.4321465800705435
},
'feature_03': {
'mean': 3.758666666666666,
'std': 1.7585291834055212
},
'feature_04': {
'mean': 1.1986666666666668,
'std': 0.7606126185881716
}
}
}
expected_content = parameters
# Run
instance.save('test.json')
# Check
compare_nested_dicts(json_mock.call_args[0][0], expected_content)
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_to_dict(self):
"""To_dict returns the defining parameters of a distribution in a dict."""
# Setup
distribution = KDEUnivariate()
column = np.array([[
0.4967141530112327,
-0.13826430117118466,
0.6476885381006925,
1.5230298564080254,
-0.23415337472333597,
-0.23413695694918055,
1.5792128155073915,
0.7674347291529088,
-0.4694743859349521,
0.5425600435859647
]])
distribution.fit(column)
expected_result = {
'type': 'copulas.univariate.kde.KDEUnivariate',
'fitted': True,
'd': 1,
'n': 10,
'dataset': [[
0.4967141530112327,
-0.13826430117118466,
0.6476885381006925,
1.5230298564080254,
-0.23415337472333597,
-0.23413695694918055,
1.5792128155073915,
0.7674347291529088,
-0.4694743859349521,
0.5425600435859647
]],
'covariance': [[0.20810696044195218]],
'factor': 0.6309573444801932,
'inv_cov': [[4.805221304834407]]
}
# Run
result = distribution.to_dict()
# Check
compare_nested_dicts(result, expected_result)
def test_save(self, json_mock):
"""Save stores the internal dictionary as a json in a file."""
# Setup
instance = Bivariate('frank')
instance.fit(self.X)
expected_content = {
"copula_type": "FRANK",
"tau": 0.014492753623188406,
"theta": 0.13070829945417198
}
# Run
instance.save('test.json')
# Check
assert json_mock.called
compare_nested_dicts(json_mock.call_args[0][0], expected_content)
def test_save(self, json_mock, open_mock):
"""Save stores the internal dictionary as a json in a file."""
# Setup
instance = Bivariate(copula_type='frank')
instance.fit(self.X)
expected_content = {
"copula_type": "FRANK",
"tau": 0.9128709291752769,
"theta": 44.2003852484162
}
# Run
instance.save('test.json')
# Check
assert open_mock.called_once_with('test.json', 'w')
assert json_mock.called
compare_nested_dicts(json_mock.call_args[0][0], expected_content)
def test_to_dict_fit_model(self):
# Setup
instance = get_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': [
[0.7969636014074211, 0.6887638642325501, 0.12078520049364487],
[0.6887638642325501, 0.7969636014074211, 0.12078520049364487]
],
'likelihood': None,
'name': CopulaTypes.FRANK,
'neighbors': [],
'parents': None,
'tau': -0.49999999999999994,
'theta': -5.736282443655552
},
{
'index': 1,
'D': set(),
'L': 1,
'R': 2,
'U': [
[0.12078520049364491, 0.7969636014074213, 0.6887638642325501],
[0.12078520049364491, 0.6887638642325503, 0.7969636014074211]
],
'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_save(self, json_mock, open_mock):
"""Save stores the internal dictionary as a json in a file."""
# Setup
instance = GaussianMultivariate(
distribution='copulas.univariate.gaussian.GaussianUnivariate')
data = pd.read_csv('data/iris.data.csv')
instance.fit(data)
covariance = [[
1.006711409395973, -0.11010327176239865, 0.8776048563471857,
0.823443255069628
],
[
-0.11010327176239865, 1.006711409395972,
-0.4233383520816991, -0.3589370029669186
],
[
0.8776048563471857, -0.4233383520816991,
1.006711409395973, 0.9692185540781536
],
[
0.823443255069628, -0.3589370029669186,
0.9692185540781536, 1.0067114093959735
]]
expected_content = {
'covariance':
covariance,
'fitted':
True,
'type':
'copulas.multivariate.gaussian.GaussianMultivariate',
'distribution':
'copulas.univariate.gaussian.GaussianUnivariate',
'columns':
['feature_01', 'feature_02', 'feature_03', 'feature_04'],
'univariates': [{
'type': 'copulas.univariate.gaussian.GaussianUnivariate',
'mean': 5.843333333333334,
'std': 0.8253012917851409,
'fitted': True,
}, {
'type': 'copulas.univariate.gaussian.GaussianUnivariate',
'mean': 3.0540000000000003,
'std': 0.4321465800705435,
'fitted': True,
}, {
'type': 'copulas.univariate.gaussian.GaussianUnivariate',
'mean': 3.758666666666666,
'std': 1.7585291834055212,
'fitted': True,
}, {
'type': 'copulas.univariate.gaussian.GaussianUnivariate',
'mean': 1.1986666666666668,
'std': 0.7606126185881716,
'fitted': True,
}]
}
# Run
instance.save('test.json')
# Check
assert open_mock.called_once_with('test.json', 'w')
compare_nested_dicts(json_mock.call_args[0][0], expected_content)
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)
