def setUp(self):
self.x = np.array([1, 2, 3])
self.y = np.array([1, 2, 3])
self.first_likelihood = GaussianLikelihood(
x=self.x,
y=self.y,
func=lambda x, param1, param2: (param1 + param2) * x,
sigma=1,
)
self.second_likelihood = PoissonLikelihood(
x=self.x, y=self.y, func=lambda x, param2, param3: (param2 + param3) * x
)
self.third_likelihood = ExponentialLikelihood(
x=self.x, y=self.y, func=lambda x, param4, param5: (param4 + param5) * x
)
self.joint_likelihood = JointLikelihood(
self.first_likelihood, self.second_likelihood, self.third_likelihood
)
self.first_likelihood.parameters["param1"] = 1
self.first_likelihood.parameters["param2"] = 2
self.second_likelihood.parameters["param2"] = 2
self.second_likelihood.parameters["param3"] = 3
self.third_likelihood.parameters["param4"] = 4
self.third_likelihood.parameters["param5"] = 5
self.joint_likelihood.parameters["param1"] = 1
self.joint_likelihood.parameters["param2"] = 2
self.joint_likelihood.parameters["param3"] = 3
self.joint_likelihood.parameters["param4"] = 4
self.joint_likelihood.parameters["param5"] = 5
def test_log_likelihood_default(self):
""" Merely tests that it ends up at the right place in the code """
exponential_likelihood = ExponentialLikelihood(
x=self.x, y=self.y, func=lambda x: np.array([4.2]))
with mock.patch('numpy.sum') as m:
m.return_value = 3
self.assertEqual(-3, exponential_likelihood.log_likelihood())
def setUp(self):
self.N = 100
self.mu = 5
self.x = np.linspace(0, 1, self.N)
self.y = np.random.exponential(self.mu, self.N)
self.yneg = np.copy(self.y)
self.yneg[0] = -1.0
def test_function(x, c):
return c
def test_function_array(x, c):
return c * np.ones(len(x))
self.function = test_function
self.function_array = test_function_array
self.exponential_likelihood = ExponentialLikelihood(
x=self.x, y=self.y, func=self.function
)
class TestJointLikelihood(unittest.TestCase):
def setUp(self):
self.x = np.array([1, 2, 3])
self.y = np.array([1, 2, 3])
self.first_likelihood = GaussianLikelihood(
x=self.x,
y=self.y,
func=lambda x, param1, param2: (param1 + param2) * x,
sigma=1,
)
self.second_likelihood = PoissonLikelihood(
x=self.x, y=self.y, func=lambda x, param2, param3: (param2 + param3) * x
)
self.third_likelihood = ExponentialLikelihood(
x=self.x, y=self.y, func=lambda x, param4, param5: (param4 + param5) * x
)
self.joint_likelihood = JointLikelihood(
self.first_likelihood, self.second_likelihood, self.third_likelihood
)
self.first_likelihood.parameters["param1"] = 1
self.first_likelihood.parameters["param2"] = 2
self.second_likelihood.parameters["param2"] = 2
self.second_likelihood.parameters["param3"] = 3
self.third_likelihood.parameters["param4"] = 4
self.third_likelihood.parameters["param5"] = 5
self.joint_likelihood.parameters["param1"] = 1
self.joint_likelihood.parameters["param2"] = 2
self.joint_likelihood.parameters["param3"] = 3
self.joint_likelihood.parameters["param4"] = 4
self.joint_likelihood.parameters["param5"] = 5
def tearDown(self):
del self.x
del self.y
del self.first_likelihood
del self.second_likelihood
del self.third_likelihood
del self.joint_likelihood
def test_parameters_consistent_from_init(self):
expected = dict(param1=1, param2=2, param3=3, param4=4, param5=5,)
self.assertDictEqual(expected, self.joint_likelihood.parameters)
def test_log_likelihood_correctly_sums(self):
expected = (
self.first_likelihood.log_likelihood()
+ self.second_likelihood.log_likelihood()
+ self.third_likelihood.log_likelihood()
)
self.assertEqual(expected, self.joint_likelihood.log_likelihood())
def test_log_likelihood_checks_parameter_updates(self):
self.first_likelihood.parameters["param2"] = 7
self.second_likelihood.parameters["param2"] = 7
self.joint_likelihood.parameters["param2"] = 7
expected = (
self.first_likelihood.log_likelihood()
+ self.second_likelihood.log_likelihood()
+ self.third_likelihood.log_likelihood()
)
self.assertEqual(expected, self.joint_likelihood.log_likelihood())
def test_list_element_parameters_are_updated(self):
self.joint_likelihood.parameters["param2"] = 7
self.assertEqual(
self.joint_likelihood.parameters["param2"],
self.joint_likelihood.likelihoods[0].parameters["param2"],
)
self.assertEqual(
self.joint_likelihood.parameters["param2"],
self.joint_likelihood.likelihoods[1].parameters["param2"],
)
def test_log_noise_likelihood(self):
self.first_likelihood.noise_log_likelihood = MagicMock(return_value=1)
self.second_likelihood.noise_log_likelihood = MagicMock(return_value=2)
self.third_likelihood.noise_log_likelihood = MagicMock(return_value=3)
self.joint_likelihood = JointLikelihood(
self.first_likelihood, self.second_likelihood, self.third_likelihood
)
expected = (
self.first_likelihood.noise_log_likelihood()
+ self.second_likelihood.noise_log_likelihood()
+ self.third_likelihood.noise_log_likelihood()
)
self.assertEqual(expected, self.joint_likelihood.noise_log_likelihood())
def test_init_with_list_of_likelihoods(self):
with self.assertRaises(ValueError):
JointLikelihood(
[self.first_likelihood, self.second_likelihood, self.third_likelihood]
)
def test_setting_single_likelihood(self):
self.joint_likelihood.likelihoods = self.first_likelihood
self.assertEqual(
self.first_likelihood.log_likelihood(),
self.joint_likelihood.log_likelihood(),
)
def test_setting_likelihood_other(self):
with self.assertRaises(ValueError):
self.joint_likelihood.likelihoods = "test"
def test_negative_array_function(self):
likelihood = ExponentialLikelihood(self.x, self.y, self.function_array)
likelihood.parameters["c"] = -1
self.assertEqual(likelihood.log_likelihood(), -np.inf)
def test_negative_data(self):
with self.assertRaises(ValueError):
ExponentialLikelihood(self.x, self.yneg, self.function)
