def test_identify_eds_not_adjacent(self):
"""_identify_eds_ing raises a ValueError if the edges are not adjacent."""
# Setup
first = Edge(None, 0, 1, None, None)
second = Edge(None, 2, 3, None, None)
# Please, note that we passed the index, copula_name and copula_theta as None
# To show they are no going to be used in the scope of this test.
# Run / Check
# As they are not adjacent, we can asure calling _identify_eds_ing will raise a ValueError.
assert not first.is_adjacent(second)
with self.assertRaises(ValueError):
Edge._identify_eds_ing(first, second)
def test_sort_edge(self):
"""sort_edge sorts Edge objects by left node index, and in case of match by right index."""
# Setup
edge_1 = Edge(None, 1, 0, None, None)
edge_2 = Edge(None, 1, 1, None, None)
edge_3 = Edge(None, 1, 2, None, None)
edge_4 = Edge(None, 2, 0, None, None)
edge_5 = Edge(None, 3, 0, None, None)
edges = [edge_3, edge_1, edge_5, edge_4, edge_2]
expected_result = [edge_1, edge_2, edge_3, edge_4, edge_5]
# Run
result = Edge.sort_edge(edges)
# Check
assert result == expected_result
def test_identify_eds_empty_dependence(self):
"""_identify_eds_ing don't require edges to have dependence set."""
# Setup
first = Edge(None, 0, 1, None, None)
second = Edge(None, 1, 2, None, None)
# Please, note that we passed the index, copula_name and copula_theta as None
# To show they are no going to be used in the scope of this test.
# left, right and dependence set
expected_result = (0, 2, {1})
# Run
result = Edge._identify_eds_ing(first, second)
# Check
assert result == expected_result
def test_get_likelihood_with_parents(self, bivariate_mock):
"""If edge has parents, their dependences are used to retrieve univariates."""
# Setup
index = None
left = 0
right = 1
copula_name = 'copula_name'
copula_theta = 'copula_theta'
instance = Edge(index, left, right, copula_name, copula_theta)
instance.D = {0, 1, 2, 3}
parent_1 = MagicMock(spec=Edge)
parent_1.D = {1, 2, 3}
parent_2 = MagicMock(spec=Edge)
parent_2.D = {0, 2, 3}
univariates = np.array([
[0.25, 0.75],
[0.50, 0.50],
[0.75, 0.25]
]).T
instance_mock = bivariate_mock.return_value
instance_mock.probability_density.return_value = [0]
instance_mock.partial_derivative.return_value = 'partial_derivative'
expected_partial_derivative_call_args = [
(
(np.array([[
[0.25, 0.75],
[0.50, 0.50],
]]),), {}
),
(
(np.array([[
[0.50, 0.50],
[0.25, 0.75]
]]), ), {}
)
]
# Run
result = instance.get_likelihood(univariates)
# Check
value, left_given_right, right_given_left = result
assert value == 0
assert left_given_right == 'partial_derivative'
assert right_given_left == 'partial_derivative'
bivariate_mock.assert_called_once_with(copula_type='copula_name')
assert instance_mock.theta == 'copula_theta'
compare_nested_iterables(
instance_mock.partial_derivative.call_args_list,
expected_partial_derivative_call_args
)
def test_valid_serialization(self):
# Setup
instance = Edge(0, 2, 5, 'clayton', 1.5)
# Run
result = Edge.from_dict(instance.to_dict())
# Check
assert instance.to_dict() == result.to_dict()
def test_get_conditional_uni(self, adjacent_mock):
"""get_conditional_uni return the corresponding univariate adjacent to the parents."""
# Setup
left = Edge(None, 1, 2, None, None)
left.U = np.array([['left_0_0', 'left_0_1'], ['left_1_0', 'left_1_1']])
right = Edge(None, 4, 2, None, None)
right.U = np.array([['right_0_0', 'right_0_1'],
['right_1_0', 'right_1_1']])
adjacent_mock.return_value = (0, 1, None)
expected_result = (np.array(['left_1_0', 'left_1_1']),
np.array(['right_1_0', 'right_1_1']))
# Run
result = Edge.get_conditional_uni(left, right)
# Check
compare_nested_iterables(result, expected_result)
def test_identify_eds(self):
"""_identify_eds_ing returns the left, right and dependency for a new edge."""
# Setup
first = Edge(None, 2, 5, None, None)
first.D = {1, 3}
second = Edge(None, 3, 4, None, None)
second.D = {1, 5}
# Please, note that we passed the index, copula_name and copula_theta as None
# To show they are no going to be used in the scope of this test.
# left, right and dependence set
expected_result = (2, 4, set([1, 3, 5]))
# Run
result = Edge._identify_eds_ing(first, second)
# Check
assert result == expected_result
def test_get_likelihood_no_parents(self, bivariate_mock):
"""get_likelihood will use current node indices if there are no parents."""
# Setup
index = 0
left = 0
right = 1
copula_name = 'copula_name'
copula_theta = 'copula_theta'
instance = Edge(index, left, right, copula_name, copula_theta)
univariates = np.array([
[0.25, 0.75],
[0.50, 0.50],
[0.75, 0.25]
]).T
instance_mock = bivariate_mock.return_value
instance_mock.probability_density.return_value = [0]
instance_mock.partial_derivative.return_value = 'partial_derivative'
expected_partial_derivative_call_args = [
(
(np.array([[
[0.25, 0.75],
[0.50, 0.50],
]]),), {}
),
(
(np.array([[
[0.50, 0.50],
[0.25, 0.75]
]]), ), {}
)
]
# Run
result = instance.get_likelihood(univariates)
# Check
value, left_given_right, right_given_left = result
assert value == 0
assert left_given_right == 'partial_derivative'
assert right_given_left == 'partial_derivative'
bivariate_mock.assert_called_once_with(copula_type='copula_name')
assert instance_mock.theta == 'copula_theta'
compare_nested_iterables(
instance_mock.partial_derivative.call_args_list,
expected_partial_derivative_call_args
)
def test_to_dict(self):
"""To_dict returns a dictionary with the parameters to recreate an edge."""
# Setup
edge = Edge(2, 5, 'clayton', 1.5)
edge.D = [1, 3]
expected_result = {
'L': 2,
'R': 5,
'name': 'clayton',
'theta': 1.5,
'D': [1, 3],
'U': None,
'likelihood': None,
'neighbors': None,
'parents': None,
'tau': None
}
# Run
result = edge.to_dict()
# Check
assert result == expected_result
def setUp(self):
self.e1 = Edge(0, 2, 5, 'clayton', 1.5)
self.e1.D = [1, 3]
self.e2 = Edge(1, 3, 4, 'clayton', 1.5)
self.e2.D = [1, 5]