def test_to_dict_from_dict(self):
model = GammaUnivariate()
model.fit(self.data)
sampled_data = model.sample(50)
params = model.to_dict()
model2 = GammaUnivariate.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 = GammaUnivariate()
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 = GammaUnivariate.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))
def test_valid_serialization_fit_model(self):
"""For a fitted model to_dict and from_dict are opposites."""
instance = GammaUnivariate()
instance.fit(np.array([1, 2, 3, 2, 1]))
result = GammaUnivariate.from_dict(instance.to_dict())
assert instance.to_dict() == result.to_dict()
def test_valid_serialization_unfit_model(self):
"""For a unfitted model to_dict and from_dict are opposites."""
instance = GammaUnivariate()
result = GammaUnivariate.from_dict(instance.to_dict())
assert instance.to_dict() == result.to_dict()
