def test_valid_serialization_unfit_model(self):
"""For a unfitted model to_dict and from_dict are opposites."""
# Setup
instance = KDEUnivariate()
# Run
result = KDEUnivariate.from_dict(instance.to_dict())
# Check
assert instance.to_dict() == result.to_dict()
def test_valid_serialization_fit_model(self):
"""For a fitted model to_dict and from_dict are opposites."""
# Setup
instance = KDEUnivariate()
X = np.array([1, 2, 3, 4])
instance.fit(X)
# Run
result = KDEUnivariate.from_dict(instance.to_dict())
# Check
assert instance.to_dict() == result.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)
