] #0 = no discouting , 1 = normal discounting,
RICE.outcomes = [
ScalarOutcome('Intratemporal utility GINI 2055',
ScalarOutcome.MINIMIZE),
ScalarOutcome('Intratemporal impact GINI 2055',
ScalarOutcome.MINIMIZE),
ScalarOutcome('Intratemporal utility GINI 2105',
ScalarOutcome.MINIMIZE),
ScalarOutcome('Intratemporal impact GINI 2105',
ScalarOutcome.MINIMIZE),
ScalarOutcome('Intertemporal utility GINI', ScalarOutcome.MINIMIZE),
ScalarOutcome('Intertemporal impact GINI', ScalarOutcome.MINIMIZE)
]
convergence_metrics = [EpsilonProgress()]
minimize = ScalarOutcome.MINIMIZE
maximize = ScalarOutcome.MAXIMIZE
for outcome in RICE.outcomes:
if outcome.kind == minimize:
outcome.kind = maximize
else:
outcome.kind = minimize
ema_logging.log_to_stderr(ema_logging.INFO)
#only needed on IPython console within Anaconda
__spec__ = "ModuleSpec(name='builtins', loader=<class '_frozen_importlib.BuiltinImporter'>)"
for policy in total_policy_list[principle_index]:
def runMoea(model, params, fileEnd, reference=None, refNum=None):
archiveName = 'archives_' + fileEnd
convergenceName = 'convergences_' + fileEnd
if not params.createNewOptimizationResults:
print('Loading archives from ' + params.optimizeOutputFolder +
archiveName + '.csv')
print('Loading convergences from ' + params.optimizeOutputFolder +
convergenceName + '.csv')
archives = pd.read_csv(params.optimizeOutputFolder + archiveName +
'.csv',
index_col=0)
convergences = pd.read_csv(params.optimizeOutputFolder +
convergenceName + '.csv',
index_col=0)
return (archives, convergences)
archs = []
convs = []
if not os.path.exists(params.optimizeOutputFolder):
os.makedirs(params.optimizeOutputFolder)
tmpfolder = params.optimizeOutputFolder + model.name + '/'
if not os.path.exists(tmpfolder):
os.makedirs(tmpfolder)
with MultiprocessingEvaluator(model) as evaluator:
for i in range(params.numberOptimizationRepetitions):
print('Optimizing Run ', i)
if params.name == 'mordm':
convergences = [
HyperVolume(minimum=[0, 0, 0, 0], maximum=[2.5, 2, 1, 1]),
EpsilonProgress()
]
else:
convergences = [
HyperVolume(minimum=[0, 0, 0, 0], maximum=[10, 2, 1, 1]),
EpsilonProgress()
]
if (params.algoName == 'NSGAIIHybrid'):
for j, name in enumerate(['SBX', 'PCX', 'DE', 'UNDX', 'UM']):
Convergence.valid_metrics.add(name)
convergences.append(OperatorProbabilities(name, j))
arch, conv = evaluator.optimize(algorithm=params.algorithm,
nfe=params.nfeOptimize[model.name],
searchover='levers',
reference=reference,
epsilons=params.epsilons,
convergence=convergences,
population_size=100)
conv['run_index'] = i
arch['run_index'] = i
if refNum is not None:
conv['reference_scenario'] = refNum
arch['reference_scenario'] = refNum
conv.to_csv(tmpfolder + convergenceName + '_' + str(i) + '.csv')
arch.to_csv(tmpfolder + archiveName + '_' + str(i) + '.csv')
convs.append(conv)
archs.append(arch)
archives = pd.concat(archs)
convergences = pd.concat(convs)
archives.to_csv(params.optimizeOutputFolder + archiveName + '.csv')
convergences.to_csv(params.optimizeOutputFolder + convergenceName + '.csv')
return (archives, convergences)
ScalarOutcome('Distance to treshold 2305', ScalarOutcome.MINIMIZE),
ScalarOutcome('Population under treshold 2305', ScalarOutcome.MINIMIZE),
ScalarOutcome('Total Aggregated Utility',ScalarOutcome.MAXIMIZE)
]
epsilon_list = [0.01]
eps = []
for i in epsilon_list:
k = np.ones((len(RICE.outcomes))) * i
eps.append(k)
nfe = 200000
convergence_metrics = [HyperVolume(minimum=[0,0,0,0,0,0,0,0,0,0,0],
maximum=[100,10000,100,10000,100,10000,100,10000,100,10000,10000]),
EpsilonProgress()]
constraints = [Constraint('Total Aggregated Utility', outcome_names='Total Aggregated Utility',
function=lambda x:max(0, -x))]
if __name__ == "__main__":
for i in range(len(epsilon_list)):
start = time.time()
print("used epsilon is: " + str(epsilon_list[i]))
print("starting search for policy generation cycle: " + str(i+1) + "/" + str(len(epsilon_list)), flush=True)
#only needed on IPython console within Anaconda
__spec__ = "ModuleSpec(name='builtins', loader=<class '_frozen_importlib.BuiltinImporter'>)"
with MultiprocessingEvaluator(RICE) as evaluator:
results, convergence = evaluator.optimize(nfe=nfe,
def runMoea(model, params, fileEnd, refNum=-1):
archiveName = 'archives_' + fileEnd
convergenceName = 'convergences_' + fileEnd
if not params.createNewOptimizationResults:
print('Loading archives from ' + params.optimizeOutputFolder +
archiveName + '.csv')
print('Loading convergences from ' + params.optimizeOutputFolder +
convergenceName + '.csv')
archives = pd.read_csv(params.optimizeOutputFolder + archiveName +
'.csv',
index_col=0)
convergences = pd.read_csv(params.optimizeOutputFolder +
convergenceName + '.csv',
index_col=0)
return (archives, convergences)
archs = []
convs = []
if not os.path.exists(params.optimizeOutputFolder):
os.makedirs(params.optimizeOutputFolder)
tmpfolder = params.optimizeOutputFolder + model.name + '/'
if not os.path.exists(tmpfolder):
os.makedirs(tmpfolder)
with MultiprocessingEvaluator(model) as evaluator:
for i in range(params.numberOptimizationRepetitions):
print('Run ', i)
convergence_metrics = [
HyperVolume(minimum=[0, 0, 0, 0], maximum=[1, 1, 1, 1]),
EpsilonProgress()
]
if (params.algoName == 'NSGAIIHybrid'):
for j, name in enumerate(['SBX', 'PCX', 'DE', 'UNDX', 'UM']):
Convergence.valid_metrics.add(name)
convergence_metrics.append(OperatorProbabilities(name, j))
arch, conv = evaluator.robust_optimize(
robustnessFunctions,
params.optimizationScenarios,
algorithm=params.algorithm,
nfe=params.nfeOptimize[model.name],
constraints=[],
epsilons=params.epsilons,
convergence=convergence_metrics)
arch['run_index'] = i
conv['run_index'] = i
archs.append(arch)
convs.append(conv)
arch.to_csv(tmpfolder + archiveName + '_' + str(i) + '.csv')
conv.to_csv(tmpfolder + convergenceName + '_' + str(i) + '.csv')
archives = pd.concat(archs)
convergences = pd.concat(convs)
archives.to_csv(params.optimizeOutputFolder + archiveName + '.csv')
convergences.to_csv(params.optimizeOutputFolder + convergenceName + '.csv')
return (archives, convergences)