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 from_dict(cls, vine_dict):
instance = cls(vine_dict['vine_type'])
fitted = vine_dict['fitted']
if fitted:
instance.fitted = fitted
instance.n_sample = vine_dict['n_sample']
instance.n_var = vine_dict['n_var']
instance.truncated = vine_dict['truncated']
instance.depth = vine_dict['depth']
instance.trees = cls._deserialize_trees(vine_dict['trees'])
instance.unis = [KDEUnivariate.from_dict(uni) for uni in vine_dict['unis']]
instance.tau_mat = np.array(vine_dict['tau_mat'])
instance.u_matrix = np.array(vine_dict['u_matrix'])
return instance
def test_from_dict(self):
"""From_dict sets the values of a dictionary as attributes of the instance."""
# Setup
distribution = KDEUnivariate()
parameters = {
'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': [[0.2081069604419522]],
'factor': 0.6309573444801932,
'inv_cov': [[4.805221304834407]]
}
# Run
distribution = KDEUnivariate.from_dict(parameters)
# Check
assert distribution.model.d == 1
assert distribution.model.n == 10
assert distribution.model.covariance == np.array([[0.2081069604419522]])
assert distribution.model.factor == 0.6309573444801932
assert distribution.model.inv_cov == np.array([[4.805221304834407]])
assert (distribution.model.dataset == np.array([[
0.4967141530112327,
-0.13826430117118466,
0.6476885381006925,
1.5230298564080254,
-0.23415337472333597,
-0.23413695694918055,
1.5792128155073915,
0.7674347291529088,
-0.4694743859349521,
0.5425600435859647
]])).all()
