def test_experiments_to_cases():
from examples.FLUvensimExample import FluModel
from expWorkbench.model import SimpleModelEnsemble
EMAlogging.log_to_stderr(EMAlogging.INFO)
data = load_results(r'../analysis/1000 flu cases.cPickle')
experiments, results = data
cases = experiments_to_cases(experiments)
model = FluModel(r'..\..\models\flu', "fluCase")
ensemble = SimpleModelEnsemble()
ensemble.set_model_structure(model)
ensemble.perform_experiments(cases)
def test_feature_selection():
from expWorkbench.model import SimpleModelEnsemble
from examples.FLUvensimExample import FluModel
log_to_stderr(level= INFO)
model = FluModel(r'..\..\models\flu', "fluCase")
ensemble = SimpleModelEnsemble()
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])
def test_save_and_load():
import matplotlib.pyplot as plt
from expWorkbench.EMAlogging import log_to_stderr, INFO
from expWorkbench.model import SimpleModelEnsemble
from examples.FLUvensimExample import FluModel
from analysis.graphs import lines
log_to_stderr(level= INFO)
model = FluModel(r'..\..\models\flu', "fluCase")
ensemble = SimpleModelEnsemble()
# 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)
file = r'C:\eclipse\workspace\EMA workbench\models\results.cPickle'
save_results(results, file)
results = load_results(file)
lines(results)
plt.show()
# EMAlogging.debug("no policy specified")
super(EnergyTrans, self).model_init(policy, kwargs)
#pop name
policy = copy.copy(policy)
policy.pop('name')
for key, value in policy.items():
vensim.set_value(key, value)
if __name__ == "__main__":
logger = logging.log_to_stderr(logging.INFO)
model = EnergyTrans(r"..\VensimModels\TFSC", "ESDMAElecTrans")
ensemble = SimpleModelEnsemble()
ensemble.set_model_structure(model)
cases, uncertainties = ensemble._generate_cases(1)
valuelist = [15.467089994193 , 18.3948367845855 , 17.5216359599053 , 0.0323513175268276 , 0.0267216806566911 , 0.0252897989265933 , 0.0211748970259063 , 0.0192967619764282 , 0.0298868721235403 , 0.026846492561752 , 0.0282265728603356 , 0.0274643497911105 , 0.0206173186487346 , 0.930953610229856 , 1.05807449426449 , 58.6261672319115 , 1.0959476696141 , 48.4897275078371 , 79.8968117041453 , 2.03012275630195 , 2.33576352581696 , 2.60266175740213 , 1.24700542123355 , 3.06884098418713 , 1 , 0 , 0 , 0 , 0 , 1.45807445678444 , 3.53395235847141 , 1.75257486371618 , 2.9795030911447 , 4.00199168664975 , 1.97473349200058 , 4.74196793795403 , 4.72730891245437 , 0 , 0 , 14826.4074143275 , 1.24609526886412 , 1.18827514220571 , 1.09824115488565 , 1245886.83942348 , 6282282.69560999 , 6118827.67237203 , 9531496.10651471 , 8693813.50295679 , 32.948697875027 , 17.1705785135149 , 13.0971274404015 , 3.74255065304761 , 1.36231655867486 , 1.92101352688469 , 3.8941723138427 , 0.898745338298322 , 0.782806406356795 , 0.817631734201507 , 0.705822656618514 , 43.3820783577107]
newcases = []
case = {}
i=0
for uncertainty in uncertainties:
print uncertainty.name
case[uncertainty.name] = valuelist[i]
i+=1
# case['desired fraction'] = 1.0
# self.uncertainties.append(CategoricalUncertainty((1,3,10,1000), "order lifetime T3", default = 3))
# self.uncertainties.append(CategoricalUncertainty((1,3,10,1000), "order lifetime T4", default = 3))
def model_init(self, policy, kwargs):
try:
self.modelFile = policy['file']
except:
EMAlogging.debug("no policy specified")
super(EnergyTrans, self).model_init(policy, kwargs)
if __name__ == "__main__":
# logger = logging.log_to_stderr(logging.INFO)
model = EnergyTrans(r"..\..\models\EnergyTrans", "ESDMAElecTrans")
ensemble = SimpleModelEnsemble()
ensemble.set_model_structure(model)
def eval_func(chromosome):
x1 = chromosome[0]
x2 = chromosome[1]
# ensemble.parallel = True
experiment = {"lifetime T1":x1, "lifetime T2": x2}
experiments = [experiment]
results = ensemble.perform_experiments(experiments)
print results[1]['total fraction new technologies'][0, -1]
return results[1]['total fraction new technologies'][0, -1]
## activationTimeStep = activationTimeStep
## else:
## activationTimeStep = np.array([0])
# results[output.name] = activationTimeStep
self.output = results
if error:
raise CaseError("run not completed", case)
if __name__ == "__main__":
logger = logging.log_to_stderr(logging.INFO)
model = EnergyTrans(r'..\..\models\CANER\CESUN', "ESDMAElecTrans")
model.step = 4 #reduce data to be stored
ensemble = SimpleModelEnsemble()
ensemble.set_model_structure(model)
ensemble.parallel = True
policies = [
{'name': 'No Policy',
'file': r'\CESUN_no.vpm'},
{'name': 'Basic Policy',
'file': r'\CESUN_basic.vpm'},
{'name': 'Adaptive Policy',
'file': r'\CESUN_adaptive.vpm'},
# {'name': 'optimized adaptive',
# 'file': r'\CESUN_optimized2.vpm'},
{'name': 'Optimized Adaptive Policy',
'file': r'\CESUN_optimized_new.vpm'}
]
# activationTimeStep = np.array([0])
## if activationTimeStep.shape[0] > 0:
## activationTimeStep = activationTimeStep
## else:
## activationTimeStep = np.array([0])
# results[output.name] = activationTimeStep
self.output = results
if error:
raise CaseError("run not completed", case)
if __name__ == "__main__":
logger = logging.log_to_stderr(logging.INFO)
model = EnergyTrans(r'..\..\models\CANER\CESUN', "ESDMAElecTrans")
model.step = 4 #reduce data to be stored
ensemble = SimpleModelEnsemble()
ensemble.set_model_structure(model)
ensemble.parallel = True
policies = [
{
'name': 'No Policy',
'file': r'\CESUN_no.vpm'
},
{
'name': 'Basic Policy',
'file': r'\CESUN_basic.vpm'
},
{
'name': 'Adaptive Policy',
'file': r'\CESUN_adaptive.vpm'
},
immune_population_region_1 = immune_population_region_1_NEXT
immune_population_region_2 = immune_population_region_2_NEXT
deceased_population_region_1.append(deceased_population_region_1_NEXT)
deceased_population_region_2.append(deceased_population_region_2_NEXT)
#End of main code
return (deceased_population_region_1, runTime, Max_infected, Max_time)
if __name__ == "__main__":
import expWorkbench.EMAlogging as logging
logging.log_to_stderr(logging.INFO)
fluModel = MexicanFlu(None, "mexican flu example")
ensemble = SimpleModelEnsemble()
ensemble.parallel = True
ensemble.set_model_structure(fluModel)
result = ensemble.perform_experiments(10)
# import matplotlib.pyplot as plt
# figure = plt.figure()
# ax = figure.add_subplot(111)
# for entry in result:
# a = entry[1][0]
# b = entry[1][1]
#
# ax.plot(b,a)
# plt.show()
# Base case parameters
"""Method for retrieving output after a model run """
return self.result
def optimize(self, case, policy):
"""method called when using the model in an optimization context
this method should return a single value that represents the performance of the policy
params are the same as for run model
"""
raise NotImplementedError
def reset_model(self):
"""Method for reseting the model to its initial state before runModel was called"""
self.modelInterface.resetModel()
if __name__ == '__main__':
logger = logging.log_to_stderr(logging.DEBUG)
# emailHander = logging.TlsSMTPHandler(("smtp.gmail.com", 587),
# '*****@*****.**',
# ['*****@*****.**'],
# 'finished!',
# ('*****@*****.**', 'password'))
# emailHander.setLevel(logging.WARNING)
# logger.addHandler(emailHander)
model = ElectTransEMA(r'C:\workspace\ElectTransEMA\workingDirectory', "test")
ensemble = SimpleModelEnsemble()
ensemble.set_model_structure(model)
ensemble.parallel=True
results = ensemble.perform_experiments(10)
