def test_model_check_data():
# Create a set of statements
a = Agent('A', db_refs={'HGNC': '1'})
b = Agent('B', db_refs={'HGNC': '2'})
b_phos = Agent('B',
mods=[ModCondition('phosphorylation')],
db_refs={'HGNC': '2'})
c = Agent('C', db_refs={'HGNC': '3'})
c_phos = Agent('C',
mods=[ModCondition('phosphorylation')],
db_refs={'HGNC': '3'})
d = Agent('D', db_refs={'HGNC': '4'})
d_phos = Agent('D',
mods=[ModCondition('phosphorylation')],
db_refs={'HGNC': '4'})
# Two paths from A to D: One going through B and another through C
st1 = Phosphorylation(a, b)
st2 = Phosphorylation(b_phos, d)
st3 = Phosphorylation(a, c)
st4 = Phosphorylation(c_phos, d)
# Statements/Data agents for checking
stmt_to_check = Phosphorylation(a, d)
agent_obs = [b_phos, c_phos, d_phos]
# Make model
pa = PysbAssembler()
pa.add_statements([st1, st2, st3, st4])
pa.make_model(policies='one_step')
mc = ModelChecker(pa.model, [stmt_to_check], agent_obs)
results = mc.check_model(max_paths=5)
mc.get_im().draw('im.pdf', prog='dot')
# Create observable
assert len(results) == 1
pr = results[0][1]
res = pr.paths[0:2]
assert len(res) == 2
p1 = [('A_phosphorylation_B_phospho', 1),
('B_phospho_phosphorylation_D_phospho', 1), ('D_phospho_p_obs', 1)]
assert p1 in res
p2 = [('A_phosphorylation_C_phospho', 1),
('C_phospho_phosphorylation_D_phospho', 1), ('D_phospho_p_obs', 1)]
assert p2 in res
# Now, a vector linking agents with values, expressed at first as
# +/- 1
# This data should ensure that the path through B should be more highly
# ranked than the path through C
data = {b_phos: 1, c_phos: -1, d_phos: 1}
paths = results[0][1].paths
scored_paths = mc.score_paths(paths, data)
assert scored_paths[0][0] == p1
assert scored_paths[0][1] == 0
assert scored_paths[1][0] == p2
paths = []
for stmt in stmt_list:
print("Checking: %s" % stmt)
result = mc.check_statement(stmt, max_paths=5,
max_path_length=6)
print(result)
if result.path_found:
path_found = 1
if result.paths:
paths += result.paths
else:
print("No path found")
if paths:
print('===========================')
print('Scoring a total of %d paths' % len(paths))
scored_result = mc.score_paths(paths, agent_values,
loss_of_function=True)
for res in scored_result:
print(res[1])
for link in res[0]:
print('--->', link[0], link[1])
paths = [s[0] for s in scored_result]
print('===========================')
results.append((drug_name, ab, relation, value, path_found,
paths))
with open('pathfinding_results.pkl', 'wb') as fh:
pickle.dump(results, fh)
else:
with open('pathfinding_results.pkl', 'rb') as fh:
results = pickle.load(fh)
#write_unicode_csv('model_check_results.csv', results)
