def visualize_optimization(): import cPickle results = cPickle.load(open("..\..\..\models\CANER\Flu\SD\maximintest.cPickle")) # best_case, best_individual_score, results = results graph_errorbars_raw(results['stats']) graph_pop_heatmap_raw(results['raw']) plt.show()
outcome = outcomes['deceased population region 1'] zeros = np.zeros((outcome.shape[0], 1)) zeros[outcome[:,-1]<1000000] = 1 value = np.sum(zeros)/zeros.shape[0] return value if __name__ == "__main__": # ema_logging.log_to_stderr(ema_logging.INFO) # model = FluModel(r'..\..\..\models\CANER\Flu\SD', "fluCase") # # ensemble = ModelEnsemble() # ensemble.set_model_structure(model) # ensemble.parallel = True # # policy_levers = {'trackperiod': (1,8), # 'delaytime': (0.01,2)} # # res = ensemble.perform_robust_optimization(cases=1000, # obj_function=obj_func, # policy_levers = policy_levers, # nrOfPopMembers=50, # nrOfGenerations=50, # crossoverRate=0.7, # mutationRate=0.01) # cPickle.dump(res, open(r'FLU robust optimization results.cPickle', 'w')) res = cPickle.load(open(r'FLU robust optimization results.cPickle', 'r')) graph_pop_heatmap_raw(res['raw']) graph_errorbars_raw(res['stats']) plt.show()