class Test_inference(unittest.TestCase):
"""
Test class for creating graphical model scaffolds from phylogeny files
"""
def setUp(self):
"""
Loads a phylogeny.
"""
phylo_file = os.path.dirname(os.path.realpath(__file__)) + "/example_data/Asp_protease_2.xml"
self.phylo_graph = BioBayesGraph()
self.graph = self.phylo_graph.populate_from_phyloxml(phylo_file)
# Incorporates the code for the ProbDist1 class into the graph
class ProbDist1(object):
def __init__(self, graph, node, node_to_name_map):
# graph, node are respectively:
# http://projects.skewed.de/graph-tool/doc/graph_tool.html#graph_tool.Graph
# http://projects.skewed.de/graph-tool/doc/graph_tool.html#graph_tool.Vertex
# node_to_name_map is a python dictionary in which
# any named node's index (can get by int(node_of_interest))
# will map to the phylogenetic name associated. (If exists)
self.graph = graph
self.node = node
self.name_to_node_map = node_to_name_map
def compute_virtual_likelihood(self, vals, auxiliary_info):
# "vals" is vector of the particular values this node
# is taking.
#
# "auxiliary_info" is the custom information provided
# when the virtual evidence was specified.
return 1
def compute_pd(self, vals):
# Returns the conditional probability for this node at vals.
# Get parent node(s):
parents = []
for p_node in self.node.in_neighbours():
parents.append(int(p_node))
# Note that you shape this depending on node location and
# other properties in the graph.
# Also, you can store computations into class-wide variables
# (e.g. ClassName.var_to_store) to cache computations. You
# could also declare the variable being stored to as global.
return 1
self.phylo_graph.add_prob_dist(prob_dist_class=ProbDist1)
# Sets all nodes to have two, variables
# first with 3 values, second with two values.
for node in self.graph.vertices():
node_index = int(node)
# Each node has v1, v2
self.phylo_graph.set_node_variable_count(node_index=node_index, num_vars=2)
# v1 \in {0,1,2}, v2 \in {0,1}
self.phylo_graph.set_node_variable_domains(node_index=node_index, var_domains=[(0, 1, 2), (0, 1)])
# Use the same probability dist (defined in the class above)
self.phylo_graph.set_node_probability_dist(node_index=node_index, prob_dist_class="ProbDist1")
def testInference(self):
"""
Runs a query using libdai.
"""
# Creates one "hard" observation, and one "virtual" observation
self.phylo_graph.clear_all_evidence()
self.phylo_graph.add_hard_evidence(
node_index=self.phylo_graph.get_node_by_name("C8SHB6_9RHIZ/82-171"), observed_value=(0, 1) # v1 = 0, v2 = 1
)
self.phylo_graph.add_virtual_evidence(
node_index=self.phylo_graph.get_node_by_name("C7X6P2_9PORP/206-299"),
observed_value=(2, 0), # v1 = 2, v2 = 0
auxiliary_info={"custom_info"}, # info provided to likelihood function
)
# phylo_graph.remove_evidence_at_node(node_index=phylo_graph.get_node_by_name("C7X6P2_9PORP/206-299"))
self.phylo_graph.create_inference_representation()
query_nodes = [
self.phylo_graph.get_node_by_name("C7PIL1_CHIPD/40-136"), # Some other node
self.phylo_graph.get_node_by_name("C7X6P2_9PORP/206-299"), # Set as virtual observation above
self.phylo_graph.get_node_by_name("C8SHB6_9RHIZ/82-171"),
] # Set as hard observation above
q_results = self.phylo_graph.inference_query(query_nodes=query_nodes)
expected = {
self.phylo_graph.get_node_by_name("C7X6P2_9PORP/206-299"): ((0, 0), 0.166666666667),
self.phylo_graph.get_node_by_name("C8SHB6_9RHIZ/82-171"): ((0, 1), 1.0),
self.phylo_graph.get_node_by_name("C7PIL1_CHIPD/40-136"): ((0, 1), 0.166666666667),
}
for qn, marginals in q_results.iteritems():
print "For node", self.phylo_graph.get_name_by_node(qn)
for var_val, marg_val in marginals:
print var_val, ":", marg_val
if var_val == expected[qn][0]:
self.assertAlmostEqual(marg_val, expected[qn][1])
def testLeaveOneOut(self):
"""
Tests leave-one-out inference looping
"""
# Creates one "hard" observation, and one "virtual" observation
self.phylo_graph.clear_all_evidence()
self.phylo_graph.add_hard_evidence(
node_index=self.phylo_graph.get_node_by_name("C8SHB6_9RHIZ/82-171"), observed_value=(0, 1) # v1 = 0, v2 = 1
)
self.phylo_graph.add_virtual_evidence(
node_index=self.phylo_graph.get_node_by_name("C7X6P2_9PORP/206-299"),
observed_value=(2, 0), # v1 = 2, v2 = 0
auxiliary_info={"custom_info"}, # info provided to likelihood function
)
q_results = self.phylo_graph.inference_query_leave_one_out()
for qn, left_out_results in q_results.iteritems():
print "For node", self.phylo_graph.get_name_by_node(qn)
pprint(left_out_results)
print "------\n"
query_nodes = [
self.phylo_graph.get_node_by_name("C7PIL1_CHIPD/40-136"), # Some other node
self.phylo_graph.get_node_by_name("C7X6P2_9PORP/206-299"), # Set as virtual observation above
self.phylo_graph.get_node_by_name("C8SHB6_9RHIZ/82-171"),
] # Set as hard observation above
q_results = self.phylo_graph.inference_query(query_nodes=query_nodes)
expected = {
self.phylo_graph.get_node_by_name("C7X6P2_9PORP/206-299"): ((0, 0), 0.166666666667),
self.phylo_graph.get_node_by_name("C8SHB6_9RHIZ/82-171"): ((0, 1), 1.0),
self.phylo_graph.get_node_by_name("C7PIL1_CHIPD/40-136"): ((0, 1), 0.166666666667),
}
for qn, marginals in q_results.iteritems():
print "For node", self.phylo_graph.get_name_by_node(qn)
for var_val, marg_val in marginals:
print var_val, ":", marg_val
if var_val == expected[qn][0]:
self.assertAlmostEqual(marg_val, expected[qn][1])
print "------\n"
q_results = self.phylo_graph.inference_query_leave_one_out()
for qn, left_out_results in q_results.iteritems():
print "For node", self.phylo_graph.get_name_by_node(qn)
pprint(left_out_results)
print "------\n"
assert 1 == 2
