def _optimize_gurobi(cobra_model,
new_objective=None,
objective_sense='maximize',
min_norm=0,
the_problem=None,
tolerance_optimality=1e-6,
tolerance_feasibility=1e-6,
tolerance_barrier=None,
tolerance_integer=1e-9,
error_reporting=None,
print_solver_time=False,
copy_problem=False,
lp_method=0,
relax_b=None,
quad_precision=False,
quadratic_component=None,
reuse_basis=True,
lp_parallel=None,
update_problem_reaction_bounds=True):
"""Uses the gurobi (http://gurobi.com) optimizer to perform an optimization on cobra_model
for the objective_coefficients in cobra_model._objective_coefficients based
on objective sense.
cobra_model: A cobra.Model object
new_objective: Reaction, String, or Integer referring to a reaction in
cobra_model.reactions to set as the objective. Currently, only supports single
objective coeffients. Will expand to include mixed objectives.
objective_sense: 'maximize' or 'minimize'
min_norm: not implemented
the_problem: None or a problem object for the specific solver that can be used to hot
start the next solution.
tolerance_optimality: Solver tolerance for optimality.
tolerance_feasibility: Solver tolerance for feasibility.
quad_precision: Boolean. Whether or not to used quad precision in calculations
error_reporting: None or True to disable or enable printing errors encountered
when trying to find the optimal solution.
print_solver_time: False or True. Indicates if the time to calculate the solution
should be displayed.
quadratic_component: None or
scipy.sparse.dok of dim(len(cobra_model.reactions),len(cobra_model.reactions))
If not None:
Solves quadratic programming problems for cobra_models of the form:
minimize: 0.5 * x' * quadratic_component * x + cobra_model._objective_coefficients' * x
such that,
cobra_model._lower_bounds <= x <= cobra_model._upper_bounds
cobra_model._S * x (cobra_model._constraint_sense) cobra_model._b
NOTE: When solving quadratic problems it may be necessary to disable quad_precision
and use lp_method = 0 for gurobi.
reuse_basis: Boolean. If True and the_problem is a model object for the solver,
attempt to hot start the solution.
update_problem_reaction_bounds: Boolean. Set to True if you're providing the_problem
and you've modified reaction bounds on your cobra_model since creating the_problem. Only
necessary for CPLEX
lp_parallel: Not implemented
lp.optimize() with Salmonella model:
cold start: 0.063 seconds
hot start: 0.057 seconds (Slow due to copying the LP)
"""
if relax_b is not None:
raise Exception('Need to reimplement constraint relaxation')
from numpy import array, nan, zeros
#TODO: speed this up
if objective_sense == 'maximize':
objective_sense = -1
else:
objective_sense = 1
from gurobipy import Model, LinExpr, GRB, QuadExpr
sense_dict = {'E': GRB.EQUAL, 'L': GRB.LESS_EQUAL, 'G': GRB.GREATER_EQUAL}
from cobra.flux_analysis.objective import update_objective
from cobra.solvers.legacy import status_dict, variable_kind_dict
variable_kind_dict = eval(variable_kind_dict['gurobi'])
status_dict = eval(status_dict['gurobi'])
#Update objectives if they are new.
if new_objective and new_objective != 'update problem':
update_objective(cobra_model, new_objective)
#Create a new problem
if not the_problem or the_problem in ['return', 'setup'] or \
not isinstance(the_problem, Model):
lp = Model("cobra")
lp.Params.OutputFlag = 0
lp.Params.LogFile = ''
# Create variables
#TODO: Speed this up
variable_list = [
lp.addVar(lb=float(x.lower_bound),
ub=float(x.upper_bound),
obj=objective_sense * float(x.objective_coefficient),
name=x.id,
vtype=variable_kind_dict[x.variable_kind])
for x in cobra_model.reactions
]
reaction_to_variable = dict(zip(cobra_model.reactions, variable_list))
# Integrate new variables
lp.update()
#Set objective to quadratic program
if quadratic_component is not None:
if not hasattr(quadratic_component, 'todok'):
raise Exception(
'quadratic component must be a scipy.sparse type array')
quadratic_objective = QuadExpr()
for (index_0,
index_1), the_value in quadratic_component.todok().items():
quadratic_objective.addTerms(the_value, variable_list[index_0],
variable_list[index_1])
lp.setObjective(quadratic_objective, sense=objective_sense)
#Constraints are based on mass balance
#Construct the lin expression lists and then add
#TODO: Speed this up as it takes about .18 seconds
#HERE
for the_metabolite in cobra_model.metabolites:
constraint_coefficients = []
constraint_variables = []
for the_reaction in the_metabolite._reaction:
constraint_coefficients.append(
the_reaction._metabolites[the_metabolite])
constraint_variables.append(reaction_to_variable[the_reaction])
#Add the metabolite to the problem
lp.addConstr(
LinExpr(constraint_coefficients, constraint_variables),
sense_dict[the_metabolite._constraint_sense.upper()],
the_metabolite._bound, the_metabolite.id)
else:
#When reusing the basis only assume that the objective coefficients or bounds can change
if copy_problem:
lp = the_problem.copy()
else:
lp = the_problem
if not reuse_basis:
lp.reset()
for the_variable, the_reaction in zip(lp.getVars(),
cobra_model.reactions):
the_variable.lb = float(the_reaction.lower_bound)
the_variable.ub = float(the_reaction.upper_bound)
the_variable.obj = float(objective_sense *
the_reaction.objective_coefficient)
if the_problem == 'setup':
return lp
if print_solver_time:
start_time = time()
lp.update()
lp.setParam("FeasibilityTol", tolerance_feasibility)
lp.setParam("OptimalityTol", tolerance_optimality)
if tolerance_barrier:
lp.setParam("BarConvTol", tolerance_barrier)
if quad_precision:
lp.setParam("Quad", 1)
lp.setParam("Method", lp_method)
#Different methods to try if lp_method fails
the_methods = [0, 2, 1]
if lp_method in the_methods:
the_methods.remove(lp_method)
if not isinstance(the_problem, Model):
lp.optimize()
if lp.status in status_dict:
status = status_dict[lp.status]
else:
status = 'failed'
if status != 'optimal':
#Try to find a solution using a different method
lp.setParam("MarkowitzTol", 1e-2)
for lp_method in the_methods:
lp.setParam("Method", lp_method)
lp.optimize()
if status_dict[lp.status] == 'optimal':
break
else:
lp.setParam("TimeLimit", 0.6)
lp.optimize()
lp.setParam("TimeLimit", "default")
if lp.status in status_dict:
status = status_dict[lp.status]
else:
status = 'failed'
if status != 'optimal':
lp.setParam("MarkowitzTol", 1e-2)
#Try to find a solution using a different method
for lp_method in the_methods:
lp.setParam("Method", lp_method)
lp.optimize()
if status_dict[lp.status] == 'optimal':
break
if status_dict[lp.status] != 'optimal':
lp = optimize_gurobi(
cobra_model,
new_objective=new_objective,
objective_sense=objective_sense,
min_norm=min_norm,
the_problem=None,
print_solver_time=print_solver_time)['the_problem']
if print_solver_time:
print 'optimize time: %f' % (time() - start_time)
x_dict = {}
y_dict = {}
y = None
if lp.status in status_dict:
status = status_dict[lp.status]
else:
status = 'failed'
if status == 'optimal':
objective_value = objective_sense * lp.ObjVal
[x_dict.update({v.VarName: v.X}) for v in lp.getVars()]
x = array([x_dict[v.id] for v in cobra_model.reactions])
if lp.isMIP:
y = y_dict = None #MIP's don't have duals
else:
[y_dict.update({c.ConstrName: c.Pi}) for c in lp.getConstrs()]
y = array([y_dict[v.id] for v in cobra_model.metabolites])
else:
y = y_dict = x = x_dict = None
objective_value = None
if error_reporting:
print 'gurobi failed: %s' % lp.status
cobra_model.solution = the_solution = Solution(objective_value,
x=x,
x_dict=x_dict,
y=y,
y_dict=y_dict,
status=status)
solution = {'the_problem': lp, 'the_solution': the_solution}
return solution
def _optimize_glpk(
cobra_model,
new_objective=None,
objective_sense='maximize',
min_norm=0,
the_problem=None,
tolerance_optimality=1e-6,
tolerance_feasibility=1e-6,
tolerance_integer=1e-9,
error_reporting=None,
print_solver_time=False,
lp_method=1,
quadratic_component=None,
reuse_basis=True,
#Not implemented
tolerance_barrier=None,
lp_parallel=None,
copy_problem=None,
relax_b=None,
update_problem_reaction_bounds=True):
"""Uses the GLPK (www.gnu.org/software/glpk/) optimizer via pyglpk
(http://www.tfinley.net/software/pyglpk/release.html) to perform an optimization
on cobra_model for the objective_coefficients in cobra_model._objective_coefficients
based on the objective sense.
cobra_model: A cobra.Model object
new_objective: Reaction, String, or Integer referring to a reaction in
cobra_model.reactions to set as the objective. Currently, only supports single
objective coeffients. Will expand to include mixed objectives.
objective_sense: 'maximize' or 'minimize'
min_norm: not implemented
the_problem: None or a problem object for the specific solver that can be used to hot
start the next solution.
tolerance_optimality: Solver tolerance for optimality.
tolerance_feasibility: Solver tolerance for feasibility.
error_reporting: None or True to disable or enable printing errors encountered
when trying to find the optimal solution.
print_solver_time: False or True. Indicates if the time to calculate the solution
should be displayed.
quadratic_component: None. GLPK cannot solve quadratic programs at the moment.
reuse_basis: Boolean. If True and the_problem is a model object for the solver,
attempt to hot start the solution. Currently, only True is available for GLPK
update_problem_reaction_bounds: Boolean. Set to True if you're providing the_problem
and you've modified reaction bounds on your cobra_model since creating the_problem. Only
necessary for CPLEX
lp.simplex() with Salmonella model:
cold start: 0.42 seconds
hot start: 0.0013 seconds
"""
from numpy import zeros, array, nan
#TODO: Speed up problem creation
if hasattr(quadratic_component, 'todok'):
raise Exception('GLPK cannot solve quadratic programs please '+\
'try using the gurobi or cplex solvers')
from glpk import LPX
from cobra.flux_analysis.objective import update_objective
from cobra.solvers.legacy import status_dict, variable_kind_dict
status_dict = eval(status_dict['glpk'])
variable_kind_dict = eval(variable_kind_dict['glpk'])
if new_objective and new_objective != 'update problem':
update_objective(cobra_model, new_objective)
#Faster to use these dicts than index lists
index_to_metabolite = dict(
zip(range(len(cobra_model.metabolites)), cobra_model.metabolites))
index_to_reaction = dict(
zip(range(len(cobra_model.reactions)), cobra_model.reactions))
reaction_to_index = dict(
zip(index_to_reaction.values(), index_to_reaction.keys()))
if the_problem == None or the_problem in ['return', 'setup'] or \
not isinstance(the_problem, LPX):
lp = LPX() # Create empty problem instance
lp.name = 'cobra' # Assign symbolic name to problem
lp.rows.add(len(cobra_model.metabolites))
lp.cols.add(len(cobra_model.reactions))
linear_constraints = []
for r in lp.rows:
the_metabolite = index_to_metabolite[r.index]
r.name = the_metabolite.id
b = float(the_metabolite._bound)
c = the_metabolite._constraint_sense
if c == 'E':
r.bounds = b, b # Set metabolite to steady state levels
elif c == 'L':
r.bounds = None, b
elif c == 'G':
r.bounds = b, None
#Add in the linear constraints
for the_reaction in the_metabolite._reaction:
reaction_index = reaction_to_index[the_reaction]
the_coefficient = the_reaction._metabolites[the_metabolite]
linear_constraints.append(
(r.index, reaction_index, the_coefficient))
#Need to assign lp.matrix after constructing the whole list
lp.matrix = linear_constraints
objective_coefficients = []
for c in lp.cols:
the_reaction = index_to_reaction[c.index]
c.name = the_reaction.id
the_reaction = index_to_reaction[c.index]
c.kind = variable_kind_dict[the_reaction.variable_kind]
c.bounds = the_reaction.lower_bound, the_reaction.upper_bound
objective_coefficients.append(
float(the_reaction.objective_coefficient))
#Add the new objective coefficients to the problem
lp.obj[:] = objective_coefficients
else:
lp = the_problem
#BUG with changing / unchanging the basis
if new_objective is not None:
objective_coefficients = []
for c in lp.cols: # Iterate over all rows
the_reaction = index_to_reaction[c.index]
c.name = the_reaction.id
c.bounds = the_reaction.lower_bound, the_reaction.upper_bound
objective_coefficients.append(
float(the_reaction.objective_coefficient))
c.kind = variable_kind_dict[the_reaction.variable_kind]
#Add the new objective coefficients to the problem
lp.obj[:] = objective_coefficients
else:
for c in lp.cols: # Iterate over all rows
the_reaction = index_to_reaction[c.index]
c.name = the_reaction.id
c.bounds = the_reaction.lower_bound, the_reaction.upper_bound
c.kind = variable_kind_dict[the_reaction.variable_kind]
if objective_sense.lower() == 'maximize':
lp.obj.maximize = True # Set this as a maximization problem
else:
lp.obj.maximize = False
if the_problem == 'setup':
return lp
if print_solver_time:
start_time = time()
the_methods = [1, 2, 3]
if lp_method in the_methods:
the_methods.remove(lp_method)
else:
lp_method = 1
if not isinstance(the_problem, LPX):
if lp.kind == int:
lp.simplex(tol_bnd=tolerance_optimality,
tol_dj=tolerance_optimality,
meth=lp_method) # we first have to solve the LP?
lp.integer(tol_int=tolerance_integer)
else:
lp.simplex(tol_bnd=tolerance_optimality,
tol_dj=tolerance_optimality,
meth=lp_method)
# Solve this LP or MIP with the simplex (depending on if integer variables exist). Takes about 0.35 s without hot start
if lp.status in status_dict:
status = status_dict[lp.status]
else:
status = 'failed'
if status != 'optimal':
for lp_method in the_methods:
lp.simplex(tol_bnd=tolerance_optimality,
tol_dj=tolerance_optimality,
meth=lp_method)
if lp.status == 'opt':
if lp.kind == int:
lp.integer(tol_int=tolerance_integer)
break
else:
if lp.kind == int:
lp.simplex(tol_bnd=tolerance_optimality,
tol_dj=tolerance_optimality,
meth=lp_method,
tm_lim=100) # we first have to solve the LP?
lp.integer(tol_int=tolerance_integer)
else:
lp.simplex(tol_bnd=tolerance_optimality,
tol_dj=tolerance_optimality,
meth=lp_method,
tm_lim=100)
#If the solver takes more than 0.1 s with a hot start it is likely stuck
if lp.status in status_dict:
status = status_dict[lp.status]
else:
status = 'failed'
if status != 'optimal':
if lp.kind == int:
lp.simplex(tol_bnd=tolerance_optimality,
tol_dj=tolerance_optimality,
meth=lp_method) # we first have to solve the LP?
lp.integer(tol_int=tolerance_integer)
else:
for lp_method in the_methods:
lp.simplex(tol_bnd=tolerance_optimality,
tol_dj=tolerance_optimality,
meth=lp_method)
if lp.status == 'opt':
if lp.kind == int:
lp.integer(tol_int=tolerance_integer)
break
if lp.status in status_dict:
status = status_dict[lp.status]
else:
status = 'failed'
if status != 'optimal':
lp = optimize_glpk(
cobra_model,
new_objective=new_objective,
objective_sense=objective_sense,
min_norm=min_norm,
the_problem=None,
print_solver_time=print_solver_time,
tolerance_optimality=tolerance_optimality,
tolerance_feasibility=tolerance_feasibility)['the_problem']
if lp.status == 'opt':
if lp.kind == int:
lp.integer(tol_int=tolerance_integer)
if lp.status in status_dict:
status = status_dict[lp.status]
else:
status = 'failed'
if status != 'optimal':
lp.simplex(tol_bnd=tolerance_optimality,
presolve=True,
tm_lim=5000)
if lp.kind == int:
lp.integer(tol_int=tolerance_integer)
if print_solver_time:
print 'simplex time: %f' % (time() - start_time)
x = []
y = []
x_dict = {}
y_dict = {}
if lp.status in status_dict:
status = status_dict[lp.status]
else:
status = 'failed'
if status == 'optimal':
objective_value = lp.obj.value
[(x.append(float(c.primal)), x_dict.update({c.name: c.primal}))
for c in lp.cols]
if lp.kind == float:
#return the duals as well as the primals for LPs
[(y.append(float(c.dual)), y_dict.update({c.name: c.dual}))
for c in lp.rows]
else:
#MIPs don't have duals
y = y_dict = None
x = array(x)
else:
x = y = x_dict = y_dict = objective_value = None
if error_reporting:
print 'glpk failed: %s' % lp.status
cobra_model.solution = the_solution = Solution(objective_value,
x=x,
x_dict=x_dict,
y=y,
y_dict=y_dict,
status=status)
solution = {'the_problem': lp, 'the_solution': the_solution}
return solution
def _optimize_cplex(cobra_model,
new_objective=None,
objective_sense='maximize',
min_norm=0,
the_problem=None,
tolerance_optimality=1e-6,
tolerance_feasibility=1e-6,
tolerance_integer=1e-9,
tolerance_barrier=1e-8,
error_reporting=None,
print_solver_time=False,
lp_method=1,
lp_parallel=0,
copy_problem=False,
relax_b=None,
quadratic_component=None,
reuse_basis=True,
update_problem_reaction_bounds=True):
"""Uses the ILOG/CPLEX (www.ibm.com/software/integration/optimization/cplex-optimizer/)
optimizer to perform an optimization on cobra_model for the objective_coefficients in
cobra_model._objective_coefficients based on the objective sense.
cobra_model: A cobra.Model object
new_objective: Reaction, String, or Integer referring to a reaction in
cobra_model.reactions to set as the objective. Currently, only supports single
objective coeffients. Will expand to include mixed objectives.
objective_sense: 'maximize' or 'minimize'
min_norm: not implemented
the_problem: None or a problem object for the specific solver that can be used to hot
start the next solution.
tolerance_optimality: Solver tolerance for optimality.
tolerance_feasibility: Solver tolerance for feasibility.
error_reporting: None or True to disable or enable printing errors encountered
when trying to find the optimal solution.
print_solver_time: False or True. Indicates if the time to calculate the solution
should be displayed.
quadratic_component: None or
scipy.sparse.dok of dim(len(cobra_model.reactions),len(cobra_model.reactions))
If not None:
Solves quadratic programming problems for cobra_models of the form:
minimize: 0.5 * x' * quadratic_component * x + cobra_model._objective_coefficients' * x
such that,
cobra_model._lower_bounds <= x <= cobra_model._upper_bounds
cobra_model._S * x (cobra_model._constraint_sense) cobra_model._b
reuse_basis: Boolean. If True and the_problem is a model object for the solver,
attempt to hot start the solution.
update_problem_reaction_bounds: Boolean. Set to True if you're providing the_problem
and you've modified reaction bounds on your cobra_model since creating the_problem. Only
necessary for CPLEX
method for linear optimization: 0 = automatic
1 = primal simplex, 2 = dual simplex, 3 = network simplex,
4 = barrier, 5 = sifting, 6 = concurrent dual, barrier, and primal
lp.solve() with Salmonella model:
cold start: 0.05 seconds
hot start: 0.05 seconds (slow due to copying the LP)
"""
if relax_b is not None:
raise Exception('Need to reimplement constraint relaxation')
from numpy import array, nan, zeros
from cobra.flux_analysis.objective import update_objective
from cobra.solvers.legacy import status_dict, variable_kind_dict
if error_reporting == 'time' or print_solver_time:
from time import time
start_time = time()
try:
from cplex import Cplex, SparsePair
variable_kind_dict = eval(variable_kind_dict['cplex'])
status_dict = eval(status_dict['cplex'])
except ImportError as e:
import sys
if 'wrong architecture' in e[0] and sys.maxsize > 2**32:
print 'CPLEX python API is not 64-bit. please contact your IBM representative'
else:
print e
if new_objective and new_objective != 'update problem':
update_objective(cobra_model, new_objective)
if the_problem == None or the_problem in ['return', 'setup', 'parallel'] \
or not isinstance(the_problem, Cplex):
lp = Cplex()
#Using the new objects
#NOTE: This might be slow
objective_coefficients = []
lower_bounds = []
upper_bounds = []
variable_names = []
variable_kinds = []
[(objective_coefficients.append(x.objective_coefficient),
lower_bounds.append(x.lower_bound),
upper_bounds.append(x.upper_bound), variable_names.append(x.id),
variable_kinds.append(variable_kind_dict[x.variable_kind]))
for x in cobra_model.reactions]
#Cplex decides that the problem is a MIP if variable_kinds are supplied
#even if there aren't any integers.
if Cplex.variables.type.integer in variable_kinds:
lp.variables.add(obj=objective_coefficients,
lb=lower_bounds,
ub=upper_bounds,
names=variable_names,
types=variable_kinds)
else:
lp.variables.add(obj=objective_coefficients,
lb=lower_bounds,
ub=upper_bounds,
names=variable_names)
if relax_b:
raise Exception('need to reimplement relax_b')
## range_values = zeros(len(cobra_model.metabolites))
## b_values = array([x._bound for x in cobra_model.metabolties])
## for the_nonzero in list(b_values.nonzero()[0]):
## range_values[the_nonzero] = -relax_b
constraint_sense = []
constraint_names = []
constraint_limits = []
[(constraint_sense.append(x._constraint_sense),
constraint_names.append(x.id), constraint_limits.append(x._bound))
for x in cobra_model.metabolites]
the_linear_expressions = []
#NOTE: This won't work with metabolites that aren't in any reaction
for the_metabolite in cobra_model.metabolites:
variable_list = []
coefficient_list = []
for the_reaction in the_metabolite._reaction:
variable_list.append(the_reaction.id)
coefficient_list.append(
the_reaction._metabolites[the_metabolite])
the_linear_expressions.append(
SparsePair(ind=variable_list, val=coefficient_list))
if quadratic_component is not None:
if not hasattr(quadratic_component, 'todok'):
raise Exception(
'quadratic component must be a scipy.sparse type array')
quadratic_component_scaled = quadratic_component.todok()
lp.parameters.emphasis.numerical.set(1)
for k, v in quadratic_component_scaled.items():
lp.objective.set_quadratic_coefficients(
int(k[0]), int(k[1]), v)
if relax_b:
lp.linear_constraints.add(lin_expr=the_linear_expressions,
rhs=constraint_limits,
range_values=list(range_values),
senses=constraint_sense,
names=constraint_names)
else:
lp.linear_constraints.add(lin_expr=the_linear_expressions,
rhs=constraint_limits,
senses=constraint_sense,
names=constraint_names)
if error_reporting == 'time':
print 'setup new problem: ' + repr(time() - start_time)
start_time = time()
#Set the problem type as cplex doesn't appear to do this correctly
problem_type = Cplex.problem_type.LP
if Cplex.variables.type.integer in variable_kinds:
if quadratic_component is not None:
problem_type = Cplex.problem_type.MIQP
else:
problem_type = Cplex.problem_type.MILP
elif quadratic_component is not None:
problem_type = Cplex.problem_type.QP
lp.set_problem_type(problem_type)
else:
if copy_problem:
lp = Cplex(the_problem)
if error_reporting == 'time':
print 'copy problem: ' + repr(time() - start_time)
start_time = time()
else:
lp = the_problem
if new_objective:
lp.objective.set_linear([(x.id, float(x.objective_coefficient))
for x in cobra_model.reactions])
if error_reporting == 'time':
print 'set lp objective: ' + repr(time() - start_time)
start_time = time()
#SPEED THIS UP
if update_problem_reaction_bounds:
lp.variables.set_upper_bounds([(x.id, float(x.upper_bound))
for x in cobra_model.reactions])
lp.variables.set_lower_bounds([(x.id, float(x.lower_bound))
for x in cobra_model.reactions])
if error_reporting == 'time':
print 'changed all bounds: ' + repr(time() - start_time)
start_time = time()
if objective_sense == 'maximize':
lp.objective.set_sense(lp.objective.sense.maximize)
else:
lp.objective.set_sense(lp.objective.sense.minimize)
if tolerance_optimality < 1e-10:
lp.parameters.simplex.perturbation.constant.set(1)
lp.parameters.simplex.pgradient.set(1)
lp.parameters.emphasis.memory.set(1)
#lp.parameters.simplex.tolerances.markowitz.set(.01)
lp.parameters.advance.set(2)
lp.parameters.simplex.tolerances.optimality.set(tolerance_optimality)
lp.parameters.simplex.tolerances.feasibility.set(tolerance_feasibility)
if lp.get_problem_type() in [
Cplex.problem_type.LP, Cplex.problem_type.MILP
]:
lp.parameters.lpmethod.set(lp_method)
elif lp.get_problem_type() in [
Cplex.problem_type.QP, Cplex.problem_type.MIQP
]:
lp.parameters.qpmethod.set(lp_method)
if lp_parallel > 1:
lp.parameters.threads.set(lp_parallel)
#lp.parameters.parallel.set(lp_parallel)
lp.parameters.barrier.convergetol.set(tolerance_barrier)
if the_problem == 'setup':
return lp
if not error_reporting:
lp.set_results_stream(None)
lp.set_warning_stream(None)
if print_solver_time:
start_time = time()
if not isinstance(the_problem, Cplex):
#TODO: set tolerance
lp.solve()
# Solve this LP with the simplex method. Takes about 0.2 s without hot start
lp.status = lp.solution.status[lp.solution.get_status()]
if lp.status in status_dict:
status = status_dict[lp.status]
else:
status = 'failed'
else:
if isinstance(the_problem, Cplex) and reuse_basis:
try:
the_basis = the_problem.solution.basis.get_basis()
lp.start.set_basis(the_basis[0], the_basis[1])
#TODO: Determine whether the primal or dual works best for the
#problem of interest. For the ME matrix the primal appears to
#work best
lp_method = 1
lp.parameters.preprocessing.presolve.set(0)
lp.parameters.lpmethod.set(lp_method)
except:
print 'no basis in the_problem'
#TODO: set tolerance and time limit
#lp.parameters.timelimit.set()
lp.solve()
#If the solver takes more than 0.1 s with a hot start it is likely stuck
lp.status = lp.solution.status[lp.solution.get_status()]
if lp.status in status_dict:
status = status_dict[lp.status]
else:
status = 'failed'
if status != 'optimal':
#Cycle through the different solver options, if a solution is not found
for lp_method in (1, 2, 3, 4, 5, 6):
lp = optimize_cplex(
cobra_model,
new_objective=new_objective,
objective_sense=objective_sense,
min_norm=min_norm,
the_problem=None,
print_solver_time=print_solver_time,
tolerance_optimality=tolerance_optimality,
tolerance_feasibility=tolerance_feasibility,
lp_method=lp_method,
quadratic_component=quadratic_component)['the_problem']
lp.status = lp.solution.status[lp.solution.get_status()]
if lp.status in status_dict:
status = status_dict[lp.status]
else:
status = 'failed'
if status == 'optimal':
break
if error_reporting == 'time':
print 'solver time: ' + repr(
time() - start_time) + ' with method ' + repr(lp_method)
start_time = time()
if print_solver_time:
print 'cplex time: %f' % (time() - start_time)
#TODO: It might be able to speed this up a little.
if status == 'optimal':
objective_value = lp.solution.get_objective_value()
#This can be sped up a little
x_dict = dict(zip(lp.variables.get_names(), lp.solution.get_values()))
x = array(lp.solution.get_values())
x = x.reshape(x.shape[0], 1)
#MIP's don't have duals
if lp.get_problem_type() in (Cplex.problem_type.MIQP,
Cplex.problem_type.MILP):
y = y_dict = None
else:
y_dict = dict(
zip(lp.linear_constraints.get_names(),
lp.solution.get_dual_values()))
y = array(lp.solution.get_dual_values())
y = y.reshape(y.shape[0], 1)
else:
x = y = x_dict = y_dict = objective_value = None
if error_reporting:
print 'cplex failed: %s' % lp.status
cobra_model.solution = the_solution = Solution(objective_value,
x=x,
x_dict=x_dict,
status=status,
y=y,
y_dict=y_dict)
solution = {'the_problem': lp, 'the_solution': the_solution}
return solution
def _optimize_glpk(cobra_model, new_objective=None, objective_sense='maximize',
min_norm=0, the_problem=None,
tolerance_optimality=1e-6, tolerance_feasibility=1e-6, tolerance_integer=1e-9,
error_reporting=None, print_solver_time=False,
lp_method=1, quadratic_component=None,
reuse_basis=True,
#Not implemented
tolerance_barrier=None, lp_parallel=None,
copy_problem=None, relax_b=None,update_problem_reaction_bounds=True):
"""Uses the GLPK (www.gnu.org/software/glpk/) optimizer via pyglpk
(http://www.tfinley.net/software/pyglpk/release.html) to perform an optimization
on cobra_model for the objective_coefficients in cobra_model._objective_coefficients
based on the objective sense.
cobra_model: A cobra.Model object
new_objective: Reaction, String, or Integer referring to a reaction in
cobra_model.reactions to set as the objective. Currently, only supports single
objective coeffients. Will expand to include mixed objectives.
objective_sense: 'maximize' or 'minimize'
min_norm: not implemented
the_problem: None or a problem object for the specific solver that can be used to hot
start the next solution.
tolerance_optimality: Solver tolerance for optimality.
tolerance_feasibility: Solver tolerance for feasibility.
error_reporting: None or True to disable or enable printing errors encountered
when trying to find the optimal solution.
print_solver_time: False or True. Indicates if the time to calculate the solution
should be displayed.
quadratic_component: None. GLPK cannot solve quadratic programs at the moment.
reuse_basis: Boolean. If True and the_problem is a model object for the solver,
attempt to hot start the solution. Currently, only True is available for GLPK
update_problem_reaction_bounds: Boolean. Set to True if you're providing the_problem
and you've modified reaction bounds on your cobra_model since creating the_problem. Only
necessary for CPLEX
lp.simplex() with Salmonella model:
cold start: 0.42 seconds
hot start: 0.0013 seconds
"""
from numpy import zeros, array, nan
#TODO: Speed up problem creation
if hasattr(quadratic_component, 'todok'):
raise Exception('GLPK cannot solve quadratic programs please '+\
'try using the gurobi or cplex solvers')
from glpk import LPX
from cobra.flux_analysis.objective import update_objective
from cobra.solvers.legacy import status_dict, variable_kind_dict
status_dict = eval(status_dict['glpk'])
variable_kind_dict = eval(variable_kind_dict['glpk'])
if new_objective and new_objective != 'update problem':
update_objective(cobra_model, new_objective)
#Faster to use these dicts than index lists
index_to_metabolite = dict(zip(range(len(cobra_model.metabolites)),
cobra_model.metabolites))
index_to_reaction = dict(zip(range(len(cobra_model.reactions)),
cobra_model.reactions))
reaction_to_index = dict(zip(index_to_reaction.values(),
index_to_reaction.keys()))
if the_problem == None or the_problem in ['return', 'setup'] or \
not isinstance(the_problem, LPX):
lp = LPX() # Create empty problem instance
lp.name = 'cobra' # Assign symbolic name to problem
lp.rows.add(len(cobra_model.metabolites))
lp.cols.add(len(cobra_model.reactions))
linear_constraints = []
for r in lp.rows:
the_metabolite = index_to_metabolite[r.index]
r.name = the_metabolite.id
b = float(the_metabolite._bound)
c = the_metabolite._constraint_sense
if c == 'E':
r.bounds = b, b # Set metabolite to steady state levels
elif c == 'L':
r.bounds = None, b
elif c == 'G':
r.bounds = b, None
#Add in the linear constraints
for the_reaction in the_metabolite._reaction:
reaction_index = reaction_to_index[the_reaction]
the_coefficient = the_reaction._metabolites[the_metabolite]
linear_constraints.append((r.index, reaction_index,
the_coefficient))
#Need to assign lp.matrix after constructing the whole list
lp.matrix = linear_constraints
objective_coefficients = []
for c in lp.cols:
the_reaction = index_to_reaction[c.index]
c.name = the_reaction.id
the_reaction = index_to_reaction[c.index]
c.kind = variable_kind_dict[the_reaction.variable_kind]
c.bounds = the_reaction.lower_bound, the_reaction.upper_bound
objective_coefficients.append(float(the_reaction.objective_coefficient))
#Add the new objective coefficients to the problem
lp.obj[:] = objective_coefficients
else:
lp = the_problem
#BUG with changing / unchanging the basis
if new_objective is not None:
objective_coefficients = []
for c in lp.cols: # Iterate over all rows
the_reaction = index_to_reaction[c.index]
c.name = the_reaction.id
c.bounds = the_reaction.lower_bound, the_reaction.upper_bound
objective_coefficients.append(float(the_reaction.objective_coefficient))
c.kind = variable_kind_dict[the_reaction.variable_kind]
#Add the new objective coefficients to the problem
lp.obj[:] = objective_coefficients
else:
for c in lp.cols: # Iterate over all rows
the_reaction = index_to_reaction[c.index]
c.name = the_reaction.id
c.bounds = the_reaction.lower_bound, the_reaction.upper_bound
c.kind = variable_kind_dict[the_reaction.variable_kind]
if objective_sense.lower() == 'maximize':
lp.obj.maximize = True # Set this as a maximization problem
else:
lp.obj.maximize = False
if the_problem == 'setup':
return lp
if print_solver_time:
start_time = time()
the_methods = [1, 2, 3]
if lp_method in the_methods:
the_methods.remove(lp_method)
else:
lp_method = 1
if not isinstance(the_problem, LPX):
if lp.kind == int:
lp.simplex(tol_bnd=tolerance_optimality, tol_dj=tolerance_optimality, meth=lp_method) # we first have to solve the LP?
lp.integer(tol_int=tolerance_integer)
else:
lp.simplex(tol_bnd=tolerance_optimality, tol_dj=tolerance_optimality, meth=lp_method)
# Solve this LP or MIP with the simplex (depending on if integer variables exist). Takes about 0.35 s without hot start
if lp.status in status_dict:
status = status_dict[lp.status]
else:
status = 'failed'
if status != 'optimal':
for lp_method in the_methods:
lp.simplex(tol_bnd=tolerance_optimality, tol_dj=tolerance_optimality, meth=lp_method)
if lp.status == 'opt':
if lp.kind == int:
lp.integer(tol_int=tolerance_integer)
break
else:
if lp.kind == int:
lp.simplex(tol_bnd=tolerance_optimality, tol_dj=tolerance_optimality, meth=lp_method, tm_lim=100) # we first have to solve the LP?
lp.integer(tol_int=tolerance_integer)
else:
lp.simplex(tol_bnd=tolerance_optimality, tol_dj=tolerance_optimality, meth=lp_method, tm_lim=100)
#If the solver takes more than 0.1 s with a hot start it is likely stuck
if lp.status in status_dict:
status = status_dict[lp.status]
else:
status = 'failed'
if status != 'optimal':
if lp.kind == int:
lp.simplex(tol_bnd=tolerance_optimality, tol_dj=tolerance_optimality, meth=lp_method) # we first have to solve the LP?
lp.integer(tol_int=tolerance_integer)
else:
for lp_method in the_methods:
lp.simplex(tol_bnd=tolerance_optimality, tol_dj=tolerance_optimality, meth=lp_method)
if lp.status == 'opt':
if lp.kind == int:
lp.integer(tol_int=tolerance_integer)
break
if lp.status in status_dict:
status = status_dict[lp.status]
else:
status = 'failed'
if status != 'optimal':
lp = optimize_glpk(cobra_model, new_objective=new_objective,
objective_sense=objective_sense,
min_norm=min_norm, the_problem=None,
print_solver_time=print_solver_time,
tolerance_optimality=tolerance_optimality,
tolerance_feasibility=tolerance_feasibility)['the_problem']
if lp.status == 'opt':
if lp.kind == int:
lp.integer(tol_int=tolerance_integer)
if lp.status in status_dict:
status = status_dict[lp.status]
else:
status = 'failed'
if status != 'optimal':
lp.simplex(tol_bnd=tolerance_optimality, presolve=True, tm_lim=5000)
if lp.kind == int:
lp.integer(tol_int=tolerance_integer)
if print_solver_time:
print 'simplex time: %f'%(time() - start_time)
x = []
y = []
x_dict = {}
y_dict = {}
if lp.status in status_dict:
status = status_dict[lp.status]
else:
status = 'failed'
if status == 'optimal':
objective_value = lp.obj.value
[(x.append(float(c.primal)),
x_dict.update({c.name:c.primal}))
for c in lp.cols]
if lp.kind == float:
#return the duals as well as the primals for LPs
[(y.append(float(c.dual)),
y_dict.update({c.name:c.dual}))
for c in lp.rows]
else:
#MIPs don't have duals
y = y_dict = None
x = array(x)
else:
x = y = x_dict = y_dict = objective_value = None
if error_reporting:
print 'glpk failed: %s'%lp.status
the_solution = Solution(objective_value, x=x, x_dict=x_dict,
y=y, y_dict=y_dict,
status=status)
solution = {'the_problem': lp, 'the_solution': the_solution}
return solution
def _optimize_gurobi(cobra_model, new_objective=None, objective_sense='maximize',
min_norm=0, the_problem=None,
tolerance_optimality=1e-6, tolerance_feasibility=1e-6,
tolerance_barrier=None, tolerance_integer=1e-9, error_reporting=None,
print_solver_time=False, copy_problem=False, lp_method=0,
relax_b=None, quad_precision=False, quadratic_component=None,
reuse_basis=True, lp_parallel=None, update_problem_reaction_bounds=True):
"""Uses the gurobi (http://gurobi.com) optimizer to perform an optimization on cobra_model
for the objective_coefficients in cobra_model._objective_coefficients based
on objective sense.
cobra_model: A cobra.Model object
new_objective: Reaction, String, or Integer referring to a reaction in
cobra_model.reactions to set as the objective. Currently, only supports single
objective coeffients. Will expand to include mixed objectives.
objective_sense: 'maximize' or 'minimize'
min_norm: not implemented
the_problem: None or a problem object for the specific solver that can be used to hot
start the next solution.
tolerance_optimality: Solver tolerance for optimality.
tolerance_feasibility: Solver tolerance for feasibility.
quad_precision: Boolean. Whether or not to used quad precision in calculations
error_reporting: None or True to disable or enable printing errors encountered
when trying to find the optimal solution.
print_solver_time: False or True. Indicates if the time to calculate the solution
should be displayed.
quadratic_component: None or
scipy.sparse.dok of dim(len(cobra_model.reactions),len(cobra_model.reactions))
If not None:
Solves quadratic programming problems for cobra_models of the form:
minimize: 0.5 * x' * quadratic_component * x + cobra_model._objective_coefficients' * x
such that,
cobra_model._lower_bounds <= x <= cobra_model._upper_bounds
cobra_model._S * x (cobra_model._constraint_sense) cobra_model._b
NOTE: When solving quadratic problems it may be necessary to disable quad_precision
and use lp_method = 0 for gurobi.
reuse_basis: Boolean. If True and the_problem is a model object for the solver,
attempt to hot start the solution.
update_problem_reaction_bounds: Boolean. Set to True if you're providing the_problem
and you've modified reaction bounds on your cobra_model since creating the_problem. Only
necessary for CPLEX
lp_parallel: Not implemented
lp.optimize() with Salmonella model:
cold start: 0.063 seconds
hot start: 0.057 seconds (Slow due to copying the LP)
"""
if relax_b is not None:
raise Exception('Need to reimplement constraint relaxation')
from numpy import array, nan, zeros
#TODO: speed this up
if objective_sense == 'maximize':
objective_sense = -1
else:
objective_sense = 1
from gurobipy import Model, LinExpr, GRB, QuadExpr
sense_dict = {'E': GRB.EQUAL,
'L': GRB.LESS_EQUAL,
'G': GRB.GREATER_EQUAL}
from cobra.flux_analysis.objective import update_objective
from cobra.solvers.legacy import status_dict, variable_kind_dict
variable_kind_dict = eval(variable_kind_dict['gurobi'])
status_dict = eval(status_dict['gurobi'])
#Update objectives if they are new.
if new_objective and new_objective != 'update problem':
update_objective(cobra_model, new_objective)
#Create a new problem
if not the_problem or the_problem in ['return', 'setup'] or \
not isinstance(the_problem, Model):
lp = Model("cobra")
lp.Params.OutputFlag = 0
lp.Params.LogFile = ''
# Create variables
#TODO: Speed this up
variable_list = [lp.addVar(lb=float(x.lower_bound),
ub=float(x.upper_bound),
obj=objective_sense*float(x.objective_coefficient),
name=x.id,
vtype=variable_kind_dict[x.variable_kind])
for x in cobra_model.reactions]
reaction_to_variable = dict(zip(cobra_model.reactions,
variable_list))
# Integrate new variables
lp.update()
#Set objective to quadratic program
if quadratic_component is not None:
if not hasattr(quadratic_component, 'todok'):
raise Exception('quadratic component must be a scipy.sparse type array')
quadratic_objective = QuadExpr()
for (index_0, index_1), the_value in quadratic_component.todok().items():
quadratic_objective.addTerms(the_value,
variable_list[index_0],
variable_list[index_1])
lp.setObjective(quadratic_objective, sense=objective_sense)
#Constraints are based on mass balance
#Construct the lin expression lists and then add
#TODO: Speed this up as it takes about .18 seconds
#HERE
for the_metabolite in cobra_model.metabolites:
constraint_coefficients = []
constraint_variables = []
for the_reaction in the_metabolite._reaction:
constraint_coefficients.append(the_reaction._metabolites[the_metabolite])
constraint_variables.append(reaction_to_variable[the_reaction])
#Add the metabolite to the problem
lp.addConstr(LinExpr(constraint_coefficients, constraint_variables),
sense_dict[the_metabolite._constraint_sense.upper()],
the_metabolite._bound,
the_metabolite.id)
else:
#When reusing the basis only assume that the objective coefficients or bounds can change
if copy_problem:
lp = the_problem.copy()
else:
lp = the_problem
if not reuse_basis:
lp.reset()
for the_variable, the_reaction in zip(lp.getVars(),
cobra_model.reactions):
the_variable.lb = float(the_reaction.lower_bound)
the_variable.ub = float(the_reaction.upper_bound)
the_variable.obj = float(objective_sense*the_reaction.objective_coefficient)
if the_problem == 'setup':
return lp
if print_solver_time:
start_time = time()
lp.update()
lp.setParam("FeasibilityTol", tolerance_feasibility)
lp.setParam("OptimalityTol", tolerance_optimality)
if tolerance_barrier:
lp.setParam("BarConvTol", tolerance_barrier)
if quad_precision:
lp.setParam("Quad", 1)
lp.setParam("Method", lp_method)
#Different methods to try if lp_method fails
the_methods = [0, 2, 1]
if lp_method in the_methods:
the_methods.remove(lp_method)
if not isinstance(the_problem, Model):
lp.optimize()
if lp.status in status_dict:
status = status_dict[lp.status]
else:
status = 'failed'
if status != 'optimal':
#Try to find a solution using a different method
lp.setParam("MarkowitzTol", 1e-2)
for lp_method in the_methods:
lp.setParam("Method", lp_method)
lp.optimize()
if status_dict[lp.status] == 'optimal':
break
else:
lp.setParam("TimeLimit", 0.6)
lp.optimize()
lp.setParam("TimeLimit", "default")
if lp.status in status_dict:
status = status_dict[lp.status]
else:
status = 'failed'
if status != 'optimal':
lp.setParam("MarkowitzTol", 1e-2)
#Try to find a solution using a different method
for lp_method in the_methods:
lp.setParam("Method", lp_method)
lp.optimize()
if status_dict[lp.status] == 'optimal':
break
if status_dict[lp.status] != 'optimal':
lp = optimize_gurobi(cobra_model, new_objective=new_objective, objective_sense=objective_sense,
min_norm=min_norm, the_problem=None,
print_solver_time=print_solver_time)['the_problem']
if print_solver_time:
print 'optimize time: %f'%(time() - start_time)
x_dict = {}
y_dict = {}
y = None
if lp.status in status_dict:
status = status_dict[lp.status]
else:
status = 'failed'
if status == 'optimal':
objective_value = objective_sense*lp.ObjVal
[x_dict.update({v.VarName: v.X}) for v in lp.getVars()]
x = array([x_dict[v.id] for v in cobra_model.reactions])
if lp.isMIP:
y = y_dict = None #MIP's don't have duals
else:
[y_dict.update({c.ConstrName: c.Pi})
for c in lp.getConstrs()]
y = array([y_dict[v.id] for v in cobra_model.metabolites])
else:
y = y_dict = x = x_dict = None
objective_value = None
if error_reporting:
print 'gurobi failed: %s'%lp.status
the_solution = Solution(objective_value, x=x, x_dict=x_dict,
y=y, y_dict=y_dict,
status=status)
solution = {'the_problem': lp, 'the_solution': the_solution}
return solution
def _optimize_cplex(cobra_model, new_objective=None, objective_sense='maximize',
min_norm=0, the_problem=None,
tolerance_optimality=1e-6, tolerance_feasibility=1e-6, tolerance_integer=1e-9,
tolerance_barrier=1e-8,error_reporting=None,
print_solver_time=False, lp_method=1, lp_parallel=0, copy_problem=False,
relax_b=None, quadratic_component=None, reuse_basis=True,
update_problem_reaction_bounds=True):
"""Uses the ILOG/CPLEX (www.ibm.com/software/integration/optimization/cplex-optimizer/)
optimizer to perform an optimization on cobra_model for the objective_coefficients in
cobra_model._objective_coefficients based on the objective sense.
cobra_model: A cobra.Model object
new_objective: Reaction, String, or Integer referring to a reaction in
cobra_model.reactions to set as the objective. Currently, only supports single
objective coeffients. Will expand to include mixed objectives.
objective_sense: 'maximize' or 'minimize'
min_norm: not implemented
the_problem: None or a problem object for the specific solver that can be used to hot
start the next solution.
tolerance_optimality: Solver tolerance for optimality.
tolerance_feasibility: Solver tolerance for feasibility.
error_reporting: None or True to disable or enable printing errors encountered
when trying to find the optimal solution.
print_solver_time: False or True. Indicates if the time to calculate the solution
should be displayed.
quadratic_component: None or
scipy.sparse.dok of dim(len(cobra_model.reactions),len(cobra_model.reactions))
If not None:
Solves quadratic programming problems for cobra_models of the form:
minimize: 0.5 * x' * quadratic_component * x + cobra_model._objective_coefficients' * x
such that,
cobra_model._lower_bounds <= x <= cobra_model._upper_bounds
cobra_model._S * x (cobra_model._constraint_sense) cobra_model._b
reuse_basis: Boolean. If True and the_problem is a model object for the solver,
attempt to hot start the solution.
update_problem_reaction_bounds: Boolean. Set to True if you're providing the_problem
and you've modified reaction bounds on your cobra_model since creating the_problem. Only
necessary for CPLEX
method for linear optimization: 0 = automatic
1 = primal simplex, 2 = dual simplex, 3 = network simplex,
4 = barrier, 5 = sifting, 6 = concurrent dual, barrier, and primal
lp.solve() with Salmonella model:
cold start: 0.05 seconds
hot start: 0.05 seconds (slow due to copying the LP)
"""
if relax_b is not None:
raise Exception('Need to reimplement constraint relaxation')
from numpy import array, nan, zeros
from cobra.flux_analysis.objective import update_objective
from cobra.solvers.legacy import status_dict, variable_kind_dict
if error_reporting == 'time' or print_solver_time:
from time import time
start_time = time()
try:
from cplex import Cplex, SparsePair
variable_kind_dict = eval(variable_kind_dict['cplex'])
status_dict = eval(status_dict['cplex'])
except ImportError as e:
import sys
if 'wrong architecture' in e[0] and sys.maxsize > 2**32:
print 'CPLEX python API is not 64-bit. please contact your IBM representative'
else:
print e
if new_objective and new_objective != 'update problem':
update_objective(cobra_model, new_objective)
if the_problem == None or the_problem in ['return', 'setup', 'parallel'] \
or not isinstance(the_problem, Cplex):
lp = Cplex()
#Using the new objects
#NOTE: This might be slow
objective_coefficients = []
lower_bounds = []
upper_bounds = []
variable_names = []
variable_kinds = []
[(objective_coefficients.append(x.objective_coefficient),
lower_bounds.append(x.lower_bound),
upper_bounds.append(x.upper_bound),
variable_names.append(x.id),
variable_kinds.append(variable_kind_dict[x.variable_kind]))
for x in cobra_model.reactions]
#Cplex decides that the problem is a MIP if variable_kinds are supplied
#even if there aren't any integers.
if Cplex.variables.type.integer in variable_kinds:
lp.variables.add(obj=objective_coefficients,
lb=lower_bounds,
ub=upper_bounds,
names=variable_names,
types=variable_kinds)
else:
lp.variables.add(obj=objective_coefficients,
lb=lower_bounds,
ub=upper_bounds,
names=variable_names)
if relax_b:
raise Exception('need to reimplement relax_b')
## range_values = zeros(len(cobra_model.metabolites))
## b_values = array([x._bound for x in cobra_model.metabolties])
## for the_nonzero in list(b_values.nonzero()[0]):
## range_values[the_nonzero] = -relax_b
constraint_sense = []
constraint_names = []
constraint_limits = []
[(constraint_sense.append(x._constraint_sense),
constraint_names.append(x.id),
constraint_limits.append(x._bound))
for x in cobra_model.metabolites]
the_linear_expressions = []
#NOTE: This won't work with metabolites that aren't in any reaction
for the_metabolite in cobra_model.metabolites:
variable_list = []
coefficient_list = []
for the_reaction in the_metabolite._reaction:
variable_list.append(the_reaction.id)
coefficient_list.append(the_reaction._metabolites[the_metabolite])
the_linear_expressions.append(SparsePair(ind=variable_list,
val=coefficient_list))
if quadratic_component is not None:
if not hasattr(quadratic_component, 'todok'):
raise Exception('quadratic component must be a scipy.sparse type array')
quadratic_component_scaled = quadratic_component.todok()
lp.parameters.emphasis.numerical.set(1)
for k, v in quadratic_component_scaled.items():
lp.objective.set_quadratic_coefficients(int(k[0]), int(k[1]), v)
if relax_b:
lp.linear_constraints.add(lin_expr=the_linear_expressions,
rhs=constraint_limits,
range_values=list(range_values),
senses=constraint_sense,
names=constraint_names)
else:
lp.linear_constraints.add(lin_expr=the_linear_expressions,
rhs=constraint_limits,
senses=constraint_sense,
names=constraint_names)
if error_reporting == 'time':
print 'setup new problem: ' + repr(time()-start_time)
start_time = time()
#Set the problem type as cplex doesn't appear to do this correctly
problem_type = Cplex.problem_type.LP
if Cplex.variables.type.integer in variable_kinds:
if quadratic_component is not None:
problem_type = Cplex.problem_type.MIQP
else:
problem_type = Cplex.problem_type.MILP
elif quadratic_component is not None:
problem_type = Cplex.problem_type.QP
lp.set_problem_type(problem_type)
else:
if copy_problem:
lp = Cplex(the_problem)
if error_reporting == 'time':
print 'copy problem: ' + repr(time()-start_time)
start_time = time()
else:
lp = the_problem
if new_objective:
lp.objective.set_linear([(x.id, float(x.objective_coefficient))
for x in cobra_model.reactions])
if error_reporting == 'time':
print 'set lp objective: ' + repr(time()-start_time)
start_time = time()
#SPEED THIS UP
if update_problem_reaction_bounds:
lp.variables.set_upper_bounds([(x.id, float(x.upper_bound))
for x in cobra_model.reactions])
lp.variables.set_lower_bounds([(x.id, float(x.lower_bound))
for x in cobra_model.reactions])
if error_reporting == 'time':
print 'changed all bounds: ' + repr(time()-start_time)
start_time = time()
if objective_sense == 'maximize':
lp.objective.set_sense(lp.objective.sense.maximize)
else:
lp.objective.set_sense(lp.objective.sense.minimize)
if tolerance_optimality < 1e-10:
lp.parameters.simplex.perturbation.constant.set(1)
lp.parameters.simplex.pgradient.set(1)
lp.parameters.emphasis.memory.set(1)
#lp.parameters.simplex.tolerances.markowitz.set(.01)
lp.parameters.advance.set(2)
lp.parameters.simplex.tolerances.optimality.set(tolerance_optimality)
lp.parameters.simplex.tolerances.feasibility.set(tolerance_feasibility)
if lp.get_problem_type() in [Cplex.problem_type.LP,
Cplex.problem_type.MILP]:
lp.parameters.lpmethod.set(lp_method)
elif lp.get_problem_type() in [Cplex.problem_type.QP,
Cplex.problem_type.MIQP]:
lp.parameters.qpmethod.set(lp_method)
if lp_parallel > 1:
lp.parameters.threads.set(lp_parallel)
#lp.parameters.parallel.set(lp_parallel)
lp.parameters.barrier.convergetol.set(tolerance_barrier)
if the_problem == 'setup':
return lp
if not error_reporting:
lp.set_results_stream(None)
lp.set_warning_stream(None)
if print_solver_time:
start_time = time()
if not isinstance(the_problem, Cplex):
#TODO: set tolerance
lp.solve()
# Solve this LP with the simplex method. Takes about 0.2 s without hot start
lp.status = lp.solution.status[lp.solution.get_status()]
if lp.status in status_dict:
status = status_dict[lp.status]
else:
status = 'failed'
else:
if isinstance(the_problem, Cplex) and reuse_basis:
try:
the_basis = the_problem.solution.basis.get_basis()
lp.start.set_basis(the_basis[0],the_basis[1])
#TODO: Determine whether the primal or dual works best for the
#problem of interest. For the ME matrix the primal appears to
#work best
lp_method = 1
lp.parameters.preprocessing.presolve.set(0)
lp.parameters.lpmethod.set(lp_method)
except:
print 'no basis in the_problem'
#TODO: set tolerance and time limit
#lp.parameters.timelimit.set()
lp.solve()
#If the solver takes more than 0.1 s with a hot start it is likely stuck
lp.status = lp.solution.status[lp.solution.get_status()]
if lp.status in status_dict:
status = status_dict[lp.status]
else:
status = 'failed'
if status != 'optimal':
#Cycle through the different solver options, if a solution is not found
for lp_method in (1, 2, 3, 4, 5, 6):
lp = optimize_cplex(cobra_model, new_objective=new_objective,
objective_sense=objective_sense,
min_norm=min_norm, the_problem=None,
print_solver_time=print_solver_time,
tolerance_optimality=tolerance_optimality,
tolerance_feasibility=tolerance_feasibility,
lp_method=lp_method,
quadratic_component=quadratic_component)['the_problem']
lp.status = lp.solution.status[lp.solution.get_status()]
if lp.status in status_dict:
status = status_dict[lp.status]
else:
status = 'failed'
if status == 'optimal':
break
if error_reporting == 'time':
print 'solver time: ' + repr(time()-start_time) + ' with method ' + repr(lp_method)
start_time = time()
if print_solver_time:
print 'cplex time: %f'%(time() - start_time)
x = []
x_dict = {}
#TODO: It might be able to speed this up a little.
if status == 'optimal':
objective_value = lp.solution.get_objective_value()
#This can be sped up a little
x_dict = dict(zip(lp.variables.get_names(),
lp.solution.get_values()))
x = array(lp.solution.get_values())
x = x.reshape(x.shape[0],1)
#MIP's don't have duals
if lp.get_problem_type() in (Cplex.problem_type.MIQP,
Cplex.problem_type.MILP):
y = y_dict = None
else:
y_dict = dict(zip(lp.linear_constraints.get_names(),
lp.solution.get_dual_values()))
y = array(lp.solution.get_dual_values())
y = y.reshape(y.shape[0],1)
else:
x = y = x_dict = y_dict = objective_value = None
if error_reporting:
print 'cplex failed: %s'%lp.status
the_solution = Solution(objective_value, x=x, x_dict=x_dict,
status=status, y=y, y_dict=y_dict)
solution = {'the_problem': lp, 'the_solution': the_solution}
return solution
def load_ALEWt(self,anoxic = False, oxic = True, update_ampms2 = True, convert2irreversible = False):
'''load iJO1366 with the following changes:
1. update to AMPMS2 to account for carbon monoxide
2. changes to uptake bounds for glucose M9 media
3. constrain the model to use 'PFK' instead of 'F6PA', 'DHAPT' when grown on glucose
4. constrain the model to use the physiologically perferred glutamate synthesis enzymes
5. depending on oxygen availability, constrain the model to use the correct RNR enzymes
6. depending on oxygen availability, constrain the model to use the correct Dihydroorotate dehydrogenase (PyrD) enzymes
7. constrain fatty acid biosynthesis to use the physiologically preferred enzymes'''
ijo1366_sbml = settings.workspace_data+"/models/iJO1366.xml"
# Read in the sbml file and define the model conditions
cobra_model = create_cobra_model_from_sbml_file(ijo1366_sbml, print_time=True)
if update_ampms2:
# Update AMPMS2
coc = Metabolite('co_c','CO','carbon monoxide','c');
cop = Metabolite('co_p','CO','carbon monoxide','p');
coe = Metabolite('co_e','CO','carbon monoxide','e');
cobra_model.add_metabolites([coc,cop,coe])
ampms2_mets = {};
ampms2_mets[cobra_model.metabolites.get_by_id('air_c')] = -1;
ampms2_mets[cobra_model.metabolites.get_by_id('amet_c')] = -1;
ampms2_mets[cobra_model.metabolites.get_by_id('dad_DASH_5_c')] = 1;
ampms2_mets[cobra_model.metabolites.get_by_id('met_DASH_L_c')] = 1;
ampms2_mets[cobra_model.metabolites.get_by_id('4ampm_c')] = 1;
ampms2_mets[cobra_model.metabolites.get_by_id('h_c')] = 3;
ampms2_mets[cobra_model.metabolites.get_by_id('for_c')] = 1;
ampms2_mets[cobra_model.metabolites.get_by_id('co_c')] = 1;
ampms2 = Reaction('AMPMS3');
ampms2.add_metabolites(ampms2_mets);
copp_mets = {};
copp_mets[cobra_model.metabolites.get_by_id('co_c')] = -1;
copp_mets[cobra_model.metabolites.get_by_id('co_p')] = 1;
copp = Reaction('COtpp');
copp.add_metabolites(copp_mets);
coex_mets = {};
coex_mets[cobra_model.metabolites.get_by_id('co_p')] = -1;
coex_mets[cobra_model.metabolites.get_by_id('co_e')] = 1;
coex = Reaction('COtex');
coex.add_metabolites(coex_mets);
cotrans_mets = {};
cotrans_mets[cobra_model.metabolites.get_by_id('co_e')] = -1;
cotrans = Reaction('EX_co_LPAREN_e_RPAREN_');
cotrans.add_metabolites(cotrans_mets);
cobra_model.add_reactions([ampms2,copp,coex,cotrans]);
cobra_model.remove_reactions(['AMPMS2']);
# Define the model conditions:
system_boundaries = [x.id for x in cobra_model.reactions if x.boundary == 'system_boundary'];
for b in system_boundaries:
cobra_model.reactions.get_by_id(b).lower_bound = 0.0;
cobra_model.reactions.get_by_id(b).upper_bound = 0.0;
# Reset demand reactions
demand = ['DM_4CRSOL',
'DM_5DRIB',
'DM_AACALD',
'DM_AMOB',
'DM_MTHTHF',
'DM_OXAM'];
for d in demand:
cobra_model.reactions.get_by_id(d).lower_bound = 0.0;
cobra_model.reactions.get_by_id(d).upper_bound = 1000.0;
# Change the objective
update_objective(cobra_model,{'Ec_biomass_iJO1366_WT_53p95M':1.0})
# Assign KOs
# Specify media composition (M9 glucose):
cobra_model.reactions.get_by_id('EX_glc_LPAREN_e_RPAREN_').lower_bound = -10.0;
cobra_model.reactions.get_by_id('EX_o2_LPAREN_e_RPAREN_').lower_bound = -18.0;
#uptake = ['EX_cl_LPAREN_e_RPAREN_',
# 'EX_so4_LPAREN_e_RPAREN_',
# 'EX_ca2_LPAREN_e_RPAREN_',
# 'EX_pi_LPAREN_e_RPAREN_',
# 'EX_fe2_LPAREN_e_RPAREN_',
# 'EX_cu2_LPAREN_e_RPAREN_',
# 'EX_zn2_LPAREN_e_RPAREN_',
# 'EX_cbl1_LPAREN_e_RPAREN_',
# 'EX_mobd_LPAREN_e_RPAREN_',
# 'EX_ni2_LPAREN_e_RPAREN_',
# 'EX_mn2_LPAREN_e_RPAREN_',
# 'EX_k_LPAREN_e_RPAREN_',
# 'EX_nh4_LPAREN_e_RPAREN_',
# 'EX_cobalt2_LPAREN_e_RPAREN_',
# 'EX_mg2_LPAREN_e_RPAREN_'];
uptake = ['EX_ca2_LPAREN_e_RPAREN_',
'EX_cbl1_LPAREN_e_RPAREN_',
'EX_cl_LPAREN_e_RPAREN_',
'EX_co2_LPAREN_e_RPAREN_',
'EX_cobalt2_LPAREN_e_RPAREN_',
'EX_cu2_LPAREN_e_RPAREN_',
'EX_fe2_LPAREN_e_RPAREN_',
'EX_fe3_LPAREN_e_RPAREN_',
'EX_h_LPAREN_e_RPAREN_',
'EX_h2o_LPAREN_e_RPAREN_',
'EX_k_LPAREN_e_RPAREN_',
'EX_mg2_LPAREN_e_RPAREN_',
'EX_mn2_LPAREN_e_RPAREN_',
'EX_mobd_LPAREN_e_RPAREN_',
'EX_na1_LPAREN_e_RPAREN_',
'EX_nh4_LPAREN_e_RPAREN_',
'EX_ni2_LPAREN_e_RPAREN_',
'EX_pi_LPAREN_e_RPAREN_',
'EX_sel_LPAREN_e_RPAREN_',
'EX_slnt_LPAREN_e_RPAREN_',
'EX_so4_LPAREN_e_RPAREN_',
'EX_tungs_LPAREN_e_RPAREN_',
'EX_zn2_LPAREN_e_RPAREN_'];
for u in uptake:
cobra_model.reactions.get_by_id(u).lower_bound = -1000.0;
# Specify allowed secretion products
secrete = ['EX_meoh_LPAREN_e_RPAREN_',
'EX_5mtr_LPAREN_e_RPAREN_',
'EX_h_LPAREN_e_RPAREN_',
'EX_co2_LPAREN_e_RPAREN_',
'EX_co_LPAREN_e_RPAREN_',
'EX_h2o_LPAREN_e_RPAREN_',
'EX_ac_LPAREN_e_RPAREN_',
'EX_fum_LPAREN_e_RPAREN_',
'EX_for_LPAREN_e_RPAREN_',
'EX_etoh_LPAREN_e_RPAREN_',
'EX_lac_DASH_L_LPAREN_e_RPAREN_',
'EX_pyr_LPAREN_e_RPAREN_',
'EX_succ_LPAREN_e_RPAREN_'];
for s in secrete:
cobra_model.reactions.get_by_id(s).upper_bound = 1000.0;
# Constrain specific reactions
noFlux = ['F6PA', 'DHAPT'];
ammoniaExcess = ['GLUDy']; # PMCID: 196288
# RNR control (DOI:10.1111/j.1365-2958.2006.05493.x)
# Dihydroorotate dehydrogenase (PyrD) (DOI:10.1016/S0076-6879(78)51010-0, PMID: 199252, DOI:S0969212602008316 [pii])
aerobic = ['RNDR1', 'RNDR2', 'RNDR3', 'RNDR4', 'DHORD2', 'ASPO6','LCARR','PFL','FRD2','FRD3']; # see DOI:10.1111/j.1365-2958.2011.07593.x; see DOI:10.1089/ars.2006.8.773 for a review
anaerobic = ['RNTR1c2', 'RNTR2c2', 'RNTR3c2', 'RNTR4c2', 'DHORD5', 'ASPO5','PDH','SUCDi']; # see DOI:10.1074/jbc.274.44.31291, DOI:10.1128/JB.00440-07
if anoxic:
rxnList = noFlux + ammoniaExcess + anaerobic;
for rxn in rxnList:
cobra_model.reactions.get_by_id(rxn).lower_bound = 0.0;
cobra_model.reactions.get_by_id(rxn).upper_bound = 0.0;
elif oxic:
rxnList = noFlux + ammoniaExcess + aerobic;
for rxn in rxnList:
cobra_model.reactions.get_by_id(rxn).lower_bound = 0.0;
cobra_model.reactions.get_by_id(rxn).upper_bound = 0.0;
else:
rxnList = noFlux + ammoniaExcess;
for rxn in rxnList:
cobra_model.reactions.get_by_id(rxn).lower_bound = 0.0;
cobra_model.reactions.get_by_id(rxn).upper_bound = 0.0;
# Set the direction for specific reactions
# Fatty acid biosynthesis: DOI: 10.1016/j.ymben.2010.10.007, PMCID: 372925
fattyAcidSynthesis = ['ACCOAC', 'ACOATA', 'HACD1', 'HACD2', 'HACD3', 'HACD4', 'HACD5', 'HACD6', 'HACD7', 'HACD8', 'KAS14', 'KAS15', 'MACPD', 'MCOATA', '3OAR100', '3OAR120', '3OAR121', '3OAR140', '3OAR141', '3OAR160', '3OAR161', '3OAR180', '3OAR181', '3OAR40', '3OAR60', '3OAR80']
fattyAcidOxidation = ['ACACT1r', 'ACACT2r', 'ACACT3r', 'ACACT4r', 'ACACT5r', 'ACACT6r', 'ACACT7r', 'ACACT8r', 'ACOAD1f', 'ACOAD2f', 'ACOAD3f', 'ACOAD4f', 'ACOAD5f', 'ACOAD6f', 'ACOAD7f', 'ACOAD8f', 'CTECOAI6', 'CTECOAI7', 'CTECOAI8', 'ECOAH1', 'ECOAH2', 'ECOAH3', 'ECOAH4', 'ECOAH5', 'ECOAH6', 'ECOAH7', 'ECOAH8']
ndpk = ['NDPK1','NDPK2','NDPK3','NDPK4','NDPK5','NDPK7','NDPK8'];
rxnList = fattyAcidSynthesis + fattyAcidOxidation;
for rxn in rxnList:
cobra_model.reactions.get_by_id(rxn).lower_bound = 0.0;
cobra_model.reactions.get_by_id(rxn).upper_bound = 1000.0;
# convert to irreversible
if convert2irreversible: convert_to_irreversible(cobra_model);
return cobra_model;
