def test_likelihood_simple_mostly_missing(self):
small_cm = pd.DataFrame.from_dict(
{
"node0": [0, -1, -1],
"node1": [1, 1, -1],
"node2": [1, -1, -1],
"node3": [1, -1, -1],
"node4": [1, -1, -1],
},
orient="index",
)
priors = {0: {1: 1}, 1: {1: 1}, 2: {1: 1}}
small_tree = cas.data.CassiopeiaTree(tree=self.small_net,
character_matrix=small_cm,
priors=priors)
small_tree.parameters["stochastic_missing_probability"] = 0.3
L = tree_metrics.calculate_likelihood_discrete(small_tree)
self.assertTrue(np.isclose(L, -11.458928604116634))
small_tree.parameters["mutation_rate"] = 0.5
small_tree.parameters["stochastic_missing_probability"] = 0.2
L = tree_metrics.calculate_likelihood_discrete(small_tree, )
self.assertTrue(np.isclose(L, -11.09716890609409))
small_tree.parameters.pop("stochastic_missing_probability")
small_tree.parameters["heritable_missing_rate"] = 0.25
L = tree_metrics.calculate_likelihood_discrete(small_tree, )
self.assertTrue(np.isclose(L, -10.685658651089808))
small_tree.parameters["stochastic_missing_probability"] = 0
L = tree_metrics.calculate_likelihood_discrete(small_tree, )
self.assertTrue(np.isclose(L, -10.549534744691526))
def test_bad_lineage_tracing_parameters(self):
small_cm = pd.DataFrame.from_dict(
{
"node0": [1, -1, -1],
"node1": [2, 1, -1],
"node2": [2, -1, -1],
"node3": [1, 2, 2],
"node4": [1, 1, 2],
},
orient="index",
)
small_tree = cas.data.CassiopeiaTree(tree=self.small_net,
character_matrix=small_cm)
with self.assertRaises(TreeMetricError):
small_tree.parameters["mutation_rate"] = -1
tree_metrics.calculate_likelihood_continuous(small_tree)
with self.assertRaises(TreeMetricError):
small_tree.parameters["mutation_rate"] = -1
tree_metrics.calculate_likelihood_discrete(small_tree)
with self.assertRaises(TreeMetricError):
small_tree.parameters["heritable_missing_rate"] = -1
tree_metrics.calculate_likelihood_continuous(small_tree)
with self.assertRaises(TreeMetricError):
small_tree.parameters["heritable_missing_rate"] = 1.5
tree_metrics.calculate_likelihood_discrete(small_tree)
with self.assertRaises(TreeMetricError):
small_tree.parameters["stochastic_missing_probability"] = -1
tree_metrics.calculate_likelihood_continuous(small_tree)
with self.assertRaises(TreeMetricError):
small_tree.parameters["stochastic_missing_probability"] = 1.5
tree_metrics.calculate_likelihood_continuous(small_tree)
def test_likelihood_set_internal_states(self):
small_cm = pd.DataFrame.from_dict(
{
"node0": [1, -1, -1],
"node1": [2, 1, -1],
"node2": [2, -1, -1],
"node3": [1, 2, 2],
"node4": [1, 1, 2],
},
orient="index",
)
priors = {
0: {
1: 0.3,
2: 0.7
},
1: {
1: 0.3,
2: 0.7
},
2: {
1: 0.3,
2: 0.7
},
3: {
1: 0.3,
2: 0.7
},
}
small_tree = cas.data.CassiopeiaTree(tree=self.small_net,
character_matrix=small_cm,
priors=priors)
small_tree.parameters["mutation_rate"] = 0.5
small_tree.parameters["heritable_missing_rate"] = 0.25
small_tree.parameters["stochastic_missing_probability"] = 0
small_tree.reconstruct_ancestral_characters()
L = tree_metrics.calculate_likelihood_discrete(
small_tree,
use_internal_character_states=True,
)
self.assertTrue(np.isclose(L, -24.57491637086155))
small_tree.set_character_states("node7", [0, 0, 0])
small_tree.set_character_states("node5", [0, 0, 0])
small_tree.set_character_states("node6", [0, 0, 2])
L = tree_metrics.calculate_likelihood_discrete(
small_tree,
use_internal_character_states=True,
)
self.assertTrue(np.isclose(L, -28.68500929005179))
def test_likelihood_sum_to_one(self):
priors = {
0: {
1: 0.2,
2: 0.8
},
1: {
1: 0.2,
2: 0.8
},
2: {
1: 0.2,
2: 0.8
}
}
ls_branch = []
ls_no_branch = []
for (
a,
b,
) in itertools.product([0, 1, -1, 2], repeat=2):
for a_, b_ in itertools.product([0, 1, -1, 2], repeat=2):
small_net = nx.DiGraph()
small_net.add_edges_from([("node2", "node0"),
("node2", "node1"),
("node3", "node2")])
small_cm = pd.DataFrame.from_dict(
{
"node0": [a, a_],
"node1": [b, b_],
},
orient="index",
)
small_tree = cas.data.CassiopeiaTree(tree=small_net,
character_matrix=small_cm,
priors=priors)
small_tree.parameters["mutation_rate"] = 0.5
small_tree.parameters["heritable_missing_rate"] = 0.25
small_tree.parameters["stochastic_missing_probability"] = 0.25
L_no_branch = tree_metrics.calculate_likelihood_discrete(
small_tree,
use_internal_character_states=False,
)
L_branch = tree_metrics.calculate_likelihood_continuous(
small_tree,
use_internal_character_states=False,
)
ls_no_branch.append(np.exp(L_no_branch))
ls_branch.append(np.exp(L_branch))
self.assertTrue(np.isclose(sum(ls_branch), 1.0))
self.assertTrue(np.isclose(sum(ls_no_branch), 1.0))
def test_likelihood_more_complex_case(self):
small_cm = pd.DataFrame.from_dict(
{
"node0": [1, -1, -1, 1],
"node1": [2, 1, -1, 1],
"node2": [2, -1, -1, -1],
"node3": [1, 2, 2, -1],
"node4": [1, 1, 2, 1],
},
orient="index",
)
priors = {
0: {
1: 0.3,
2: 0.7
},
1: {
1: 0.3,
2: 0.7
},
2: {
1: 0.3,
2: 0.7
},
3: {
1: 0.3,
2: 0.7
},
}
small_tree = cas.data.CassiopeiaTree(tree=self.small_net,
character_matrix=small_cm,
priors=priors)
small_tree.parameters["mutation_rate"] = 0.5
small_tree.parameters["heritable_missing_rate"] = 0.25
small_tree.parameters["stochastic_missing_probability"] = 0
L = tree_metrics.calculate_likelihood_discrete(small_tree)
self.assertTrue(np.isclose(L, -33.11623901010781))
def test_likelihood_bad_cases(self):
small_tree = cas.data.CassiopeiaTree(tree=self.small_net)
small_tree.parameters["stochastic_missing_probability"] = 0.2
with self.assertRaises(TreeMetricError):
tree_metrics.calculate_likelihood_discrete(small_tree)
small_cm = pd.DataFrame.from_dict(
{
"node0": [1, -1, -1],
"node1": [2, 1, -1],
"node2": [2, -1, -1],
"node3": [1, 2, 2],
"node4": [1, 1, 2],
},
orient="index",
)
small_tree = cas.data.CassiopeiaTree(tree=self.small_net,
character_matrix=small_cm)
small_tree.parameters["stochastic_missing_probability"] = 0.2
with self.assertRaises(TreeMetricError):
tree_metrics.calculate_likelihood_discrete(small_tree)
priors = {
0: {
1: 0.3,
2: 0.7
},
1: {
1: 0.3,
2: 0.7
},
2: {
1: 0.3,
2: 0.7
},
3: {
1: 0.3,
2: 0.7
},
}
small_tree = cas.data.CassiopeiaTree(tree=self.small_net,
character_matrix=small_cm,
priors=priors)
small_tree.parameters["stochastic_missing_probability"] = 0.2
with self.assertRaises(TreeMetricError):
tree_metrics.calculate_likelihood_discrete(
small_tree, use_internal_character_states=True)
small_tree.set_character_states("node7", [0, 0, 0])
small_tree.set_character_states("node5", [0, 0, 0])
with self.assertRaises(TreeMetricError):
tree_metrics.calculate_likelihood_discrete(
small_tree,
use_internal_character_states=True,
)
small_tree.set_character_states("node6", [0, 0, 1])
L = tree_metrics.calculate_likelihood_discrete(
small_tree,
use_internal_character_states=True,
)
self.assertEqual(-np.inf, L)