本文整理匯總了Python中expWorkbench.ModelEnsemble.parallel方法的典型用法代碼示例。如果您正苦於以下問題：Python ModelEnsemble.parallel方法的具體用法？Python ModelEnsemble.parallel怎麽用？Python ModelEnsemble.parallel使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類expWorkbench.ModelEnsemble的用法示例。
在下文中一共展示了ModelEnsemble.parallel方法的10個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: maxmin_optimize
# 需要導入模塊: from expWorkbench import ModelEnsemble [as 別名]
# 或者: from expWorkbench.ModelEnsemble import parallel [as 別名]
def maxmin_optimize():
ema_logging.log_to_stderr(ema_logging.INFO)
model = TestModel("", 'simpleModel') #instantiate the model
ensemble = ModelEnsemble() #instantiate an ensemble
ensemble.set_model_structure(model) #set the model on the ensemble
ensemble.parallel = True
ensemble.processes = 12
def obj_function1(outcomes):
return outcomes['y']
policy_levers = { "L1": (0,1),
"L2": (0,1)}
results = ensemble.perform_maximin_optimization(obj_function1 = obj_function1,
policy_levers = policy_levers,
minimax1='minimize',
nrOfGenerations1=50,
nrOfPopMembers1=200,
minimax2 = "maximize",
nrOfGenerations2 = 50,
nrOfPopMembers2 = 100,
)
graph_errorbars_raw(results['stats'])
plt.show()示例2: test_optimization
# 需要導入模塊: from expWorkbench import ModelEnsemble [as 別名]
# 或者: from expWorkbench.ModelEnsemble import parallel [as 別名]
def test_optimization():
ema_logging.log_to_stderr(ema_logging.INFO)
model = FluModel(r'..\data', "fluCase")
ensemble = ModelEnsemble()
ensemble.set_model_structure(model)
ensemble.parallel=True
# ensemble.processes = 12
stats, pop = ensemble.perform_outcome_optimization(obj_function = obj_function_multi,
reporting_interval=10,
weights=(MAXIMIZE, MAXIMIZE),
pop_size=100,
nr_of_generations=5,
crossover_rate=0.5,
mutation_rate=0.05)
res = stats.hall_of_fame.keys
x = [entry.values[0] for entry in res]
y = [entry.values[1] for entry in res]
print len(x), len(y)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(x,y)
ax.set_ylabel("deceased population")
ax.set_xlabel("infected fraction")
plt.show()示例3: perform_experiments
# 需要導入模塊: from expWorkbench import ModelEnsemble [as 別名]
# 或者: from expWorkbench.ModelEnsemble import parallel [as 別名]
def perform_experiments():
ema_logging.log_to_stderr(level=ema_logging.INFO)
model = SalinizationModel(r"C:\workspace\EMA-workbench\models\salinization", "verzilting")
model.step = 4
ensemble = ModelEnsemble()
ensemble.set_model_structure(model)
ensemble.parallel = True
nr_of_experiments = 10000
results = ensemble.perform_experiments(nr_of_experiments)
return results示例4: test_optimization
# 需要導入模塊: from expWorkbench import ModelEnsemble [as 別名]
# 或者: from expWorkbench.ModelEnsemble import parallel [as 別名]
def test_optimization():
ema_logging.log_to_stderr(ema_logging.INFO)
model = FluModel(r'..\data', "fluCase")
ensemble = ModelEnsemble()
ensemble.set_model_structure(model)
ensemble.parallel=True
stats, pop = ensemble.perform_outcome_optimization(obj_function = obj_function_multi,
reporting_interval=100,
weights=(MAXIMIZE, MAXIMIZE),
pop_size=100,
nr_of_generations=20,
crossover_rate=0.5,
mutation_rate=0.05,
caching=False)
res = stats.hall_of_fame.keys
print len(stats.tried_solutions.values())示例5: test_tree
# 需要導入模塊: from expWorkbench import ModelEnsemble [as 別名]
# 或者: from expWorkbench.ModelEnsemble import parallel [as 別名]
def test_tree():
log_to_stderr(level= INFO)
model = FluModel(r'..\..\models\flu', "fluCase")
ensemble = ModelEnsemble()
ensemble.parallel = True
ensemble.set_model_structure(model)
policies = [{'name': 'no policy',
'file': r'\FLUvensimV1basecase.vpm'},
{'name': 'static policy',
'file': r'\FLUvensimV1static.vpm'},
{'name': 'adaptive policy',
'file': r'\FLUvensimV1dynamic.vpm'}
]
ensemble.add_policies(policies)
results = ensemble.perform_experiments(10)
a_tree = tree(results, classify)示例6: test_feature_selection
# 需要導入模塊: from expWorkbench import ModelEnsemble [as 別名]
# 或者: from expWorkbench.ModelEnsemble import parallel [as 別名]
def test_feature_selection():
log_to_stderr(level= INFO)
model = FluModel(r'..\..\models\flu', "fluCase")
ensemble = ModelEnsemble()
ensemble.parallel = True
ensemble.set_model_structure(model)
policies = [{'name': 'no policy',
'file': r'\FLUvensimV1basecase.vpm'},
{'name': 'static policy',
'file': r'\FLUvensimV1static.vpm'},
{'name': 'adaptive policy',
'file': r'\FLUvensimV1dynamic.vpm'}
]
ensemble.add_policies(policies)
results = ensemble.perform_experiments(5000)
results = feature_selection(results, classify)
for entry in results:
print entry[0] +"\t" + str(entry[1])示例7: test_optimization
# 需要導入模塊: from expWorkbench import ModelEnsemble [as 別名]
# 或者: from expWorkbench.ModelEnsemble import parallel [as 別名]
def test_optimization():
ema_logging.log_to_stderr(ema_logging.INFO)
model = FluModel(r'../models', "fluCase")
ensemble = ModelEnsemble()
ensemble.set_model_structure(model)
ensemble.parallel=True
pop_size = 8
nr_of_generations = 10
eps = np.array([1e-3, 1e6])
stats, pop = ensemble.perform_outcome_optimization(obj_function = obj_function_multi,
algorithm=epsNSGA2,
reporting_interval=100,
weights=(MAXIMIZE, MAXIMIZE),
pop_size=pop_size,
nr_of_generations=nr_of_generations,
crossover_rate=0.8,
mutation_rate=0.05,
eps=eps)
fn = '../data/test optimization save.bz2'示例8: CaseError
# 需要導入模塊: from expWorkbench import ModelEnsemble [as 別名]
# 或者: from expWorkbench.ModelEnsemble import parallel [as 別名]
raise CaseError("run not completed", case)
def obj_func(outcomes):
outcome = outcomes['total fraction new technologies']
zeros = np.zeros((outcome.shape[0], 1))
zeros[outcome[:,-1]>0.6] = 1
value = np.sum(zeros)/zeros.shape[0]
return value,
if __name__ == "__main__":
ema_logging.log_to_stderr(ema_logging.INFO)
model = EnergyTrans(r"..\data", "ESDMAElecTrans")
ensemble = ModelEnsemble()
ensemble.set_model_structure(model)
ensemble.parallel = True
policy_levers = {'Trigger subsidy T2': {'type':'range', 'values':(0,1)},
'Trigger subsidy T3': {'type':'range', 'values':(0,1)},
'Trigger subsidy T4': {'type':'range', 'values':(0,1)},
'Trigger addnewcom': {'type':'list', 'values':[0, 0.25, 0.5, 0.75, 1]}}
stats_callback, pop = ensemble.perform_robust_optimization(cases=10,
reporting_interval=100,
obj_function=obj_func,
policy_levers=policy_levers,
weights = (MAXIMIZE,),
nr_of_generations=100,
pop_size=10,
crossover_rate=0.5,
mutation_rate=0.02,示例9: model_init
# 需要導入模塊: from expWorkbench import ModelEnsemble [as 別名]
# 或者: from expWorkbench.ModelEnsemble import parallel [as 別名]
def model_init(self, policy, kwargs):
'''initializes the model'''
try:
self.model_file = policy['file']
except KeyError:
ema_logging.warning("key 'file' not found in policy")
super(FluModel, self).model_init(policy, kwargs)
if __name__ == "__main__":
ema_logging.log_to_stderr(ema_logging.INFO)
model = FluModel(r'..\..\models\flu', "fluCase")
ensemble = ModelEnsemble()
ensemble.set_model_structure(model)
#add policies
policies = [{'name': 'no policy',
'file': r'\FLUvensimV1basecase.vpm'},
{'name': 'static policy',
'file': r'\FLUvensimV1static.vpm'},
{'name': 'adaptive policy',
'file': r'\FLUvensimV1dynamic.vpm'}
]
ensemble.add_policies(policies)
ensemble.parallel = True #turn on parallel processing
results = ensemble.perform_experiments(1000)
save_results(results, r'./data/1000 flu cases.bz2')示例10: ParameterUncertainty
# 需要導入模塊: from expWorkbench import ModelEnsemble [as 別名]
# 或者: from expWorkbench.ModelEnsemble import parallel [as 別名]
ParameterUncertainty((0.45,0.55),
"tH"),
ParameterUncertainty((0.1,0.3),
"kk")]
#specification of the outcomes
outcomes = [Outcome("B4:B1076", time=True), #we can refer to a range in the normal way
Outcome("P_t", time=True)] # we can also use named range
#name of the sheet
sheet = "Sheet1"
#relative path to the Excel file
workbook = r'\excel example.xlsx'
if __name__ == "__main__":
ema_logging.log_to_stderr(level=ema_logging.INFO)
model = ExcelModel(r"./models/excelModel", "predatorPrey")
ensemble = ModelEnsemble()
ensemble.set_model_structure(model)
ensemble.parallel = True #turn on parallel computing
ensemble.processes = 2 #using only 2 cores
#run 100 experiments
nr_experiments = 100
results = ensemble.perform_experiments(nr_experiments)