def test_single_gene_deletion_moma_benchmark(self, model, benchmark):
try:
sutil.get_solver_name(qp=True)
except sutil.SolverNotFound:
pytest.skip("no qp support")
genes = ['b0008', 'b0114', 'b2276', 'b1779']
benchmark(single_gene_deletion, model, gene_list=genes, method="moma")
def test_single_gene_deletion_moma(self, model):
try:
sutil.get_solver_name(qp=True)
except sutil.SolverNotFound:
pytest.skip("no qp support")
# expected knockouts for textbook model
growth_dict = {
"b0008": 0.87,
"b0114": 0.71,
"b0116": 0.56,
"b2276": 0.11,
"b1779": 0.00
}
df = single_gene_deletion(model,
gene_list=growth_dict.keys(),
method="moma")
assert numpy.all([df.status == 'optimal'])
assert all(
abs(df.flux[gene] - expected) < 0.01
for gene, expected in iteritems(growth_dict))
with model:
add_moma(model)
with pytest.raises(ValueError):
add_moma(model)
def test_moma_sanity(self, model):
"""Test optimization criterion and optimality."""
try:
solver = sutil.get_solver_name(qp=True)
model.solver = solver
except sutil.SolverNotFound:
pytest.skip("no qp support")
sol = model.optimize()
with model:
model.reactions.PFK.knock_out()
knock_sol = model.optimize()
ssq = (knock_sol.fluxes - sol.fluxes).pow(2).sum()
with model:
add_moma(model)
model.reactions.PFK.knock_out()
moma_sol = model.optimize()
moma_ssq = (moma_sol.fluxes - sol.fluxes).pow(2).sum()
# Use normal FBA as reference solution.
with model:
add_moma(model, solution=sol)
model.reactions.PFK.knock_out()
moma_ref_sol = model.optimize()
moma_ref_ssq = (moma_ref_sol.fluxes - sol.fluxes).pow(2).sum()
assert numpy.isclose(moma_sol.objective_value, moma_ssq)
assert moma_ssq < ssq
assert numpy.isclose(moma_sol.objective_value,
moma_ref_sol.objective_value)
assert numpy.isclose(moma_ssq, moma_ref_ssq)
def test_single_gene_deletion_linear_moma(self, model):
try:
solver = sutil.get_solver_name(qp=True)
model.solver = solver
except sutil.SolverNotFound:
pytest.skip("no qp support")
# expected knockouts for textbook model
growth_dict = {
"b0008": 0.87,
"b0114": 0.76,
"b0116": 0.65,
"b2276": 0.08,
"b1779": 0.00
}
result = single_gene_deletion(model,
gene_list=growth_dict.keys(),
method="linear moma")['growth']
assert all(
abs(result[frozenset([gene])] - expected) < 0.01
for gene, expected in iteritems(growth_dict))
with model:
add_moma(model, linear=True)
with pytest.raises(ValueError):
add_moma(model)
def test_moma_sanity(self, model):
"""Test optimization criterion and optimality."""
try:
solver = sutil.get_solver_name(qp=True)
model.solver = solver
except sutil.SolverNotFound:
pytest.skip("no qp support")
sol = model.optimize()
with model:
model.reactions.PFK.knock_out()
knock_sol = model.optimize()
ssq = (knock_sol.fluxes - sol.fluxes).pow(2).sum()
with model:
add_moma(model)
model.reactions.PFK.knock_out()
moma_sol = model.optimize()
moma_ssq = (moma_sol.fluxes - sol.fluxes).pow(2).sum()
# Use normal FBA as reference solution.
with model:
add_moma(model, solution=sol)
model.reactions.PFK.knock_out()
moma_ref_sol = model.optimize()
moma_ref_ssq = (moma_ref_sol.fluxes - sol.fluxes).pow(2).sum()
assert numpy.isclose(moma_sol.objective_value, moma_ssq)
assert moma_ssq < ssq
assert numpy.isclose(moma_sol.objective_value,
moma_ref_sol.objective_value)
assert numpy.isclose(moma_ssq, moma_ref_ssq)
def __init__(self, id_or_model=None, name=None):
if isinstance(id_or_model, Model):
Object.__init__(self, name=name)
self.__setstate__(id_or_model.__dict__)
if not hasattr(self, "name"):
self.name = None
self._solver = id_or_model.solver
else:
Object.__init__(self, id_or_model, name=name)
self._trimmed = False
self._trimmed_genes = []
self._trimmed_reactions = {}
self.genes = DictList()
self.reactions = DictList() # A list of cobra.Reactions
self.metabolites = DictList() # A list of cobra.Metabolites
# genes based on their ids {Gene.id: Gene}
self._compartments = dict()
self._contexts = []
# from cameo ...
# if not hasattr(self, '_solver'): # backwards compatibility
# with older cobrapy pickles?
interface = solvers[get_solver_name()]
self._solver = interface.Model()
self._solver.objective = interface.Objective(Zero)
self._populate_solver(self.reactions, self.metabolites)
def __init__(self, id_or_model=None, name=None):
if isinstance(id_or_model, Model):
Object.__init__(self, name=name)
self.__setstate__(id_or_model.__dict__)
if not hasattr(self, "name"):
self.name = None
self._solver = id_or_model.solver
else:
Object.__init__(self, id_or_model, name=name)
self._trimmed = False
self._trimmed_genes = []
self._trimmed_reactions = {}
self.genes = DictList()
self.reactions = DictList() # A list of cobra.Reactions
self.metabolites = DictList() # A list of cobra.Metabolites
# genes based on their ids {Gene.id: Gene}
self.compartments = dict()
self._contexts = []
# from cameo ...
# if not hasattr(self, '_solver'): # backwards compatibility
# with older cobrapy pickles?
interface = solvers[get_solver_name()]
self._solver = interface.Model()
self._solver.objective = interface.Objective(S.Zero)
self._populate_solver(self.reactions, self.metabolites)
def test_single_deletion_linear_moma_benchmark(self, model, benchmark):
try:
solver = sutil.get_solver_name(qp=True)
model.solver = solver
except sutil.SolverNotFound:
pytest.skip("no qp support")
genes = ['b0008', 'b0114', 'b2276', 'b1779']
benchmark(single_gene_deletion, model, gene_list=genes,
method="linear moma")
def test_single_gene_deletion_moma(self, model):
try:
solver = sutil.get_solver_name(qp=True)
model.solver = solver
except sutil.SolverNotFound:
pytest.skip("no qp support")
# expected knockouts for textbook model
growth_dict = {"b0008": 0.87, "b0114": 0.71, "b0116": 0.56,
"b2276": 0.11, "b1779": 0.00}
result = single_gene_deletion(
model, gene_list=growth_dict.keys(), method="moma")["growth"]
for gene, expected_value in iteritems(growth_dict):
assert abs(result[frozenset([gene])] - expected_value) < 0.01
def test_single_gene_deletion_moma(self, model):
try:
solver = sutil.get_solver_name(qp=True)
model.solver = solver
except sutil.SolverNotFound:
pytest.skip("no qp support")
# expected knockouts for textbook model
growth_dict = {"b0008": 0.87, "b0114": 0.71, "b0116": 0.56,
"b2276": 0.11, "b1779": 0.00}
result = single_gene_deletion(
model=model,
gene_list=list(growth_dict),
method="moma",
processes=1
)["growth"]
for gene, value in iteritems(growth_dict):
assert numpy.isclose(result[frozenset([gene])], value,
atol=1E-02)
def test_single_gene_deletion_linear_moma(self, model):
try:
solver = sutil.get_solver_name(qp=True)
model.solver = solver
except sutil.SolverNotFound:
pytest.skip("no qp support")
# expected knockouts for textbook model
growth_dict = {"b0008": 0.87, "b0114": 0.76, "b0116": 0.65,
"b2276": 0.08, "b1779": 0.00}
result = single_gene_deletion(
model, gene_list=growth_dict.keys(),
method="linear moma")['growth']
assert all(abs(result[frozenset([gene])] - expected) < 0.01
for gene, expected in iteritems(growth_dict))
with model:
add_moma(model, linear=True)
with pytest.raises(ValueError):
add_moma(model)
def test_single_gene_deletion_moma(self, model):
try:
solver = sutil.get_solver_name(qp=True)
model.solver = solver
except sutil.SolverNotFound:
pytest.skip("no qp support")
# expected knockouts for textbook model
growth_dict = {
"b0008": 0.87,
"b0114": 0.71,
"b0116": 0.56,
"b2276": 0.11,
"b1779": 0.00
}
result = single_gene_deletion(model,
gene_list=growth_dict.keys(),
method="moma")["growth"]
for gene, expected_value in iteritems(growth_dict):
assert abs(result[frozenset([gene])] - expected_value) < 0.01
def test_single_gene_deletion_moma(self, model):
try:
solver = sutil.get_solver_name(qp=True)
model.solver = solver
except sutil.SolverNotFound:
pytest.skip("no qp support")
# expected knockouts for textbook model
growth_dict = {
"b0008": 0.87,
"b0114": 0.71,
"b0116": 0.56,
"b2276": 0.11,
"b1779": 0.00
}
result = single_gene_deletion(model=model,
gene_list=list(growth_dict),
method="moma",
processes=1)["growth"]
for gene, value in iteritems(growth_dict):
assert numpy.isclose(result[frozenset([gene])], value, atol=1E-02)
def test_linear_moma_sanity(self, model):
"""Test optimization criterion and optimality."""
try:
solver = sutil.get_solver_name(qp=True)
model.solver = solver
except sutil.SolverNotFound:
pytest.skip("no qp support")
sol = model.optimize()
with model:
model.reactions.PFK.knock_out()
knock_sol = model.optimize()
sabs = (knock_sol.fluxes - sol.fluxes).abs().sum()
with model:
add_moma(model, linear=True)
model.reactions.PFK.knock_out()
moma_sol = model.optimize()
moma_sabs = (moma_sol.fluxes - sol.fluxes).abs().sum()
assert numpy.allclose(moma_sol.objective_value, moma_sabs)
assert moma_sabs < sabs
def test_linear_moma_sanity(self, model):
"""Test optimization criterion and optimality."""
try:
solver = sutil.get_solver_name(qp=True)
model.solver = solver
except sutil.SolverNotFound:
pytest.skip("no qp support")
sol = model.optimize()
with model:
model.reactions.PFK.knock_out()
knock_sol = model.optimize()
sabs = (knock_sol.fluxes - sol.fluxes).abs().sum()
with model:
add_moma(model, linear=True)
model.reactions.PFK.knock_out()
moma_sol = model.optimize()
moma_sabs = (moma_sol.fluxes - sol.fluxes).abs().sum()
assert numpy.allclose(moma_sol.objective_value, moma_sabs)
assert moma_sabs < sabs
def test_solver_name(self):
assert su.get_solver_name() == "glpk"
def test_solver_name() -> None:
"""Test that the default LP solver name is GLPK."""
assert su.get_solver_name() == "glpk"
def test_solver_name():
assert su.get_solver_name() == "glpk"