def test_from_dict(self):
"""From_dict sets the values of a dictionary as attributes of the instance."""
# Setup
parameters = {
'fitted':
True,
'dataset': [[
0.4967141530112327, -0.13826430117118466, 0.6476885381006925,
1.5230298564080254, -0.23415337472333597, -0.23413695694918055,
1.5792128155073915, 0.7674347291529088, -0.4694743859349521,
0.5425600435859647
]],
}
# Run
distribution = GaussianKDE.from_dict(parameters)
# Check
assert distribution.model.d == 1
assert distribution.model.n == 10
assert distribution.model.covariance == np.array(
[[0.20810696044195226]])
assert distribution.model.factor == 0.6309573444801932
assert distribution.model.inv_cov == np.array([[4.805221304834406]])
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()
def from_dict(cls, vine_dict):
"""Create a new instance from a parameters dictionary.
Args:
params (dict):
Parameters of the Vine, in the same format as the one
returned by the ``to_dict`` method.
Returns:
Vine:
Instance of the Vine defined on the parameters.
"""
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 = [
GaussianKDE.from_dict(uni) for uni in vine_dict['unis']
]
instance.ppfs = [uni.percent_point for uni in instance.unis]
instance.columns = vine_dict['columns']
instance.tau_mat = np.array(vine_dict['tau_mat'])
instance.u_matrix = np.array(vine_dict['u_matrix'])
return instance
def test_valid_serialization_unfit_model(self):
"""For a unfitted model to_dict and from_dict are opposites."""
# Setup
instance = GaussianKDE()
# Run
result = GaussianKDE.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 = GaussianKDE()
X = np.array([1, 2, 3, 4])
instance.fit(X)
# Run
result = GaussianKDE.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 = [GaussianKDE.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_to_dict_from_dict(self):
model = GaussianKDE()
model.fit(self.data)
sampled_data = model.sample(50)
params = model.to_dict()
model2 = GaussianKDE.from_dict(params)
pdf = model.probability_density(sampled_data)
pdf2 = model2.probability_density(sampled_data)
assert np.all(np.isclose(pdf, pdf2, atol=0.01))
cdf = model.cumulative_distribution(sampled_data)
cdf2 = model2.cumulative_distribution(sampled_data)
assert np.all(np.isclose(cdf, cdf2, atol=0.01))
def test_to_dict_from_dict_constant(self):
model = GaussianKDE()
model.fit(self.constant)
sampled_data = model.sample(50)
pdf = model.probability_density(sampled_data)
cdf = model.cumulative_distribution(sampled_data)
params = model.to_dict()
model2 = GaussianKDE.from_dict(params)
np.testing.assert_equal(np.full(50, 5), sampled_data)
np.testing.assert_equal(np.full(50, 5), model2.sample(50))
np.testing.assert_equal(np.full(50, 1), pdf)
np.testing.assert_equal(np.full(50, 1), model2.probability_density(sampled_data))
np.testing.assert_equal(np.full(50, 1), cdf)
np.testing.assert_equal(np.full(50, 1), model2.cumulative_distribution(sampled_data))
