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_from_dict(self):
"""from_dict generates a new instance from its parameters."""
# Setup
copula = GaussianMultivariate()
copula.fit(self.data)
copula_dict = copula.to_dict()
# Run
new_copula = GaussianMultivariate.from_dict(copula_dict)
# Asserts
assert isinstance(new_copula, GaussianMultivariate)
assert new_copula.columns == ['column1', 'column2', 'column3']
assert len(new_copula.univariates) == 3
for new_univariate, old_univariate in zip(copula.univariates, new_copula.univariates):
assert new_univariate.to_dict() == old_univariate.to_dict()
def test_to_dict(self):
"""To_dict returns the parameters to replicate the copula."""
# Setup
copula = GaussianMultivariate()
copula.fit(self.data)
# Run
result = copula.to_dict()
# Asserts
assert result['type'] == 'copulas.multivariate.gaussian.GaussianMultivariate'
assert result['columns'] == ['column1', 'column2', 'column3']
assert len(result['univariates']) == 3
expected_cov = copula._get_covariance(self.data).to_numpy().tolist()
np.testing.assert_equal(result['covariance'], expected_cov)
for univariate, result_univariate in zip(copula.univariates, result['univariates']):
assert univariate.to_dict() == result_univariate
