n_islands=number_of_processes,
max_cycles_without_improvement=max_cycles_without_improvement,
stop_criteria_relative=0.01,
initial_archi_x=[],
lb_list=[],
ub_list=[],
max_flux=1e6,
label_problem_parameters=label_problem_parameters)
label_model.best_chi2 = optimal_solution
flux_sd_dict, hessian, inverse_hessian, covariance = get_std_deviation(
label_model, optimal_variables, initial_step=1e-3)
export_flux_results(label_model,
optimal_variables,
fn=output_prefix + "_fluxes.csv",
flux_sd_dict=flux_sd_dict)
objfunc(label_model, optimal_variables)
export_label_results(label_model,
fn=output_prefix + "_label.csv",
show_chi=True,
show_emu=False,
show_fluxes=False)
np.savetxt(output_prefix + "_variables.txt", optimal_variables)
reference_parameters = [optimal_variables]
label_model.best_label_variables = optimal_variables
save_iso2flux_model(label_model,
name=output_prefix + ".iso2flux",
write_sbml=True,
ask_sbml_name=False,
best_flux=initial_flux_estimation
best_variables=None
for iteration in range(0,n_iterations):
flux_objective,variables=minimize_fluxes(label_model,iso2flux_problem,label_problem_parameters,max_chi=max_chi,flux_penalty_dict=flux_penalty_dict ,pop_size=pop_size ,n_gen=n_gen ,n_islands=number_of_processes ,max_cycles_without_improvement=max_cycles_without_improvement ,max_evolve_cycles=999 ,stop_criteria_relative=0.0005 ,max_iterations=1, initial_flux_estimation=initial_flux_estimation,log_name=file_name.replace(".iso2flux","_p13cmfa_log.txt"),migrate="one_direction",max_flux_sampling=max_flux_for_sampling)
print "flux minimized to " + str(round(flux_objective,3))
if flux_objective<best_flux:
initial_flux_estimation=best_best_flux=flux_objective
label_problem_parameters={"label_weight":0.0001,"target_flux_dict":None,"max_chi":max_chi,"max_flux":best_flux,"flux_penalty_dict":flux_penalty_dict,"verbose":True,"flux_weight":1,"flux_unfeasible_penalty":25,"label_unfeasible_penalty":5}
best_best_flux=flux_objective
best_variables=variables
export_flux_results(label_model,best_variables,fn=output_prefix+"_p13cmfa_fluxes.csv")
export_flux_results(label_model,best_variables,fn=output_prefix+"_p13cmfa_fluxes_irreversible.csv",reversible=False)
objfunc(label_model,best_variables)
export_label_results(label_model,fn=output_prefix+"_p13cmfa_label.csv",show_chi=True,show_emu=True,show_fluxes=False)
np.savetxt(output_prefix+"_p13cmfa_variables.txt",best_variables)
label_model.best_p13cmfa_variables=best_variables
label_model.label_tolerance=max_chi
label_model.best_flux=flux_objective
print label_model.label_tolerance
save_iso2flux_model(label_model,name=output_prefix+".iso2flux",write_sbml=True,ask_sbml_name=False,gui=False)
label_problem_parameters={"label_weight":0.0001,"target_flux_dict":None,"max_chi":max_chi,"max_flux":initial_flux_estimation,"flux_penalty_dict":flux_penalty_dict,"verbose":True,"flux_weight":1,"flux_unfeasible_penalty":25,"label_unfeasible_penalty":5}
best_flux=initial_flux_estimation
best_variables=None
for iteration in range(0,n_iterations):
flux_objective,variables=minimize_fluxes(label_model,iso2flux_problem,label_problem_parameters,max_chi=max_chi,flux_penalty_dict=flux_penalty_dict ,pop_size=pop_size ,n_gen=n_gen ,n_islands=number_of_processes ,max_cycles_without_improvement=max_cycles_without_improvement ,max_evolve_cycles=999 ,stop_criteria_relative=0.000001 ,max_iterations=1, initial_flux_estimation=initial_flux_estimation,log_name=file_name.replace(".iso2flux","_p13cmfa_log.txt"),migrate="one_direction")
print "flux minimized to " + str(round(flux_objective,3))
if flux_objective<best_flux:
initial_flux_estimation=best_best_flux=flux_objective
label_problem_parameters={"label_weight":0.0001,"target_flux_dict":None,"max_chi":max_chi,"max_flux":best_flux,"flux_penalty_dict":flux_penalty_dict,"verbose":True,"flux_weight":1,"flux_unfeasible_penalty":25,"label_unfeasible_penalty":5}
best_best_flux=flux_objective
best_variables=variables
export_flux_results(label_model,best_variables,fn=output_prefix+"_p13cmfa_fluxes.csv")
objfunc(label_model,best_variables)
export_label_results(label_model,fn=output_prefix+"_p13cmfa_label.csv",show_chi=True,show_emu=True,show_fluxes=False)
np.savetxt(output_prefix+"_p13cmfa_variables.txt",best_variables)
label_model.best_p13cmfa_variables=best_variables
label_model.label_tolerance=max_chi
label_model.best_flux=flux_objective
print label_model.label_tolerance
save_iso2flux_model(label_model,name=output_prefix+".iso2flux",write_sbml=True,ask_sbml_name=False,gui=False)
max_flux=1e6,
label_problem_parameters=label_problem_parameters,
migrate="one_direction",
max_flux_sampling=max_flux_for_sampling)
label_model.best_chi2 = optimal_solution
try:
flux_sd_dict, hessian, inverse_hessian, covariance = get_std_deviation(
label_model, optimal_variables, initial_step=1e-3)
except:
flux_sd_dict = {}
export_flux_results(label_model,
optimal_variables,
fn=output_prefix + "_fluxes.csv",
flux_sd_dict=flux_sd_dict,
reversible=True)
export_flux_results(label_model,
optimal_variables,
fn=output_prefix + "_irreversible_fluxes.csv",
flux_sd_dict={},
reversible=False)
objfunc(label_model, optimal_variables)
export_label_results(label_model,
fn=output_prefix + "_label.csv",
show_chi=True,
show_emu=True,
show_fluxes=False)
if passed_flag==True:
print "Validation passed"
sys.exit(2)
label_problem_parameters={"label_weight":1,"target_flux_dict":None,"max_chi":1e6,"max_flux":1e6,"flux_penalty_dict":{},"verbose":True,"flux_weight":0.0,"label_unfeasible_penalty":1,"flux_unfeasible_penalty":10}
iso2flux_problem=define_isoflux_problem(label_model)
optimal_solution,optimal_variables=optimize(label_model,iso2flux_problem,pop_size = pop_size,n_gen = n_gen,n_islands=number_of_processes ,max_cycles_without_improvement=max_cycles_without_improvement,stop_criteria_relative=0.01,initial_archi_x=[],lb_list=[],ub_list=[],max_flux=1e6,label_problem_parameters=label_problem_parameters)
label_model.best_chi2=optimal_solution
flux_sd_dict, hessian,inverse_hessian,covariance=get_std_deviation(label_model,optimal_variables,initial_step=1e-3)
export_flux_results(label_model,optimal_variables,fn=output_prefix+"_fluxes.csv",flux_sd_dict=flux_sd_dict)
objfunc(label_model,optimal_variables)
export_label_results(label_model,fn=output_prefix+"_label.csv",show_chi=True,show_emu=False,show_fluxes=False)
np.savetxt(output_prefix+"_variables.txt",optimal_variables)
reference_parameters=[optimal_variables]
label_model.best_label_variables=optimal_variables
save_iso2flux_model(label_model,name=output_prefix+".iso2flux",write_sbml=True,ask_sbml_name=False,gui=False)
if not compute_intervals:
output_model=label_model.constrained_model.copy()
for reaction in output_model.reactions:
if reaction.id in label_model.reversible_flux_dict:
reaction.lower_bound=max(round_down(label_model.reversible_flux_dict[reaction.id],6),reaction.lower_bound)
reaction.upper_bound=min(round_up(label_model.reversible_flux_dict[reaction.id],6),reaction.upper_bound)
label_problem_parameters = {
"label_weight": 0.0001,
"target_flux_dict": None,
"max_chi": max_chi,
"max_flux": best_flux,
"flux_penalty_dict": flux_penalty_dict,
"verbose": True,
"flux_weight": 1,
"flux_unfeasible_penalty": 25,
"label_unfeasible_penalty": 5
}
best_best_flux = flux_objective
best_variables = variables
export_flux_results(label_model,
best_variables,
fn=output_prefix + "_p13cmfa_fluxes.csv")
objfunc(label_model, best_variables)
export_label_results(label_model,
fn=output_prefix + "_p13cmfa_label.csv",
show_chi=True,
show_emu=True,
show_fluxes=False)
np.savetxt(output_prefix + "_p13cmfa_variables.txt", best_variables)
label_model.best_p13cmfa_variables = best_variables
label_model.label_tolerance = max_chi
label_model.best_flux = flux_objective
print label_model.label_tolerance
save_iso2flux_model(label_model,