def test_multiple_models(self):
"""
Test running running with two different pysd models
Returns
-------
"""
relative_path_to_file = '../models/Sales_Agent_Market_Building_Dynamics.mdl'
directory = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
mdl_file = os.path.join(directory, relative_path_to_file)
market_model = PysdModel(mdl_file=mdl_file)
market_model.uncertainties = [RealParameter('Startup Subsidy',0, 3),
RealParameter('Startup Subsidy Length', 0, 10)]
market_model.outcomes = [TimeSeriesOutcome('Still Employed')]
relative_path_to_file = '../models/Sales_Agent_Motivation_Dynamics.mdl'
directory = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
mdl_file = os.path.join(directory, relative_path_to_file)
motivation_model = PysdModel(mdl_file=mdl_file)
motivation_model.uncertainties = [RealParameter('Startup Subsidy', 0, 3),
RealParameter('Startup Subsidy Length', 0, 10)]
motivation_model.outcomes =[TimeSeriesOutcome('Still Employed')]
models = [market_model, motivation_model] # set the model on the ensemble
perform_experiments(models, 5)
def test_multiple_models(self):
"""
Test running running with two different pysd models
Returns
-------
"""
relative_path_to_file = '../models/Sales_Agent_Market_Building_Dynamics.mdl'
directory = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
mdl_file = os.path.join(directory, relative_path_to_file)
market_model = PysdModel(mdl_file=mdl_file)
market_model.uncertainties = [RealParameter('Startup Subsidy',0, 3),
RealParameter('Startup Subsidy Length', 0, 10)]
market_model.outcomes = [TimeSeriesOutcome('Still Employed')]
relative_path_to_file = '../models/Sales_Agent_Motivation_Dynamics.mdl'
directory = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
mdl_file = os.path.join(directory, relative_path_to_file)
motivation_model = PysdModel(mdl_file=mdl_file)
motivation_model.uncertainties = [RealParameter('Startup Subsidy', 0, 3),
RealParameter('Startup Subsidy Length', 0, 10)]
motivation_model.outcomes =[TimeSeriesOutcome('Still Employed')]
models = [market_model, motivation_model] # set the model on the ensemble
perform_experiments(models, 5)
def test_running_lookup_uncertainties(self):
'''
This is the more comprehensive test, given that the lookup
uncertainty replaces itself with a bunch of other uncertainties, check
whether we can successfully run a set of experiments and get results
back. We assert that the uncertainties are correctly replaced by
analyzing the experiments array.
'''
if os.name != 'nt':
return
model = LookupTestModel( r'../models/', 'lookupTestModel')
#model.step = 4 #reduce data to be stored
perform_experiments(model, 10)
def test_running_lookup_uncertainties(self):
'''
This is the more comprehensive test, given that the lookup
uncertainty replaces itself with a bunch of other uncertainties, check
whether we can successfully run a set of experiments and get results
back. We assert that the uncertainties are correctly replaced by
analyzing the experiments array.
'''
if os.name != 'nt':
return
model = LookupTestModel(r'../models/', 'lookupTestModel')
#model.step = 4 #reduce data to be stored
perform_experiments(model, 10)
def test_parallel_experiment(self):
"""
Test running an experiment in parallel
Returns
-------
"""
relative_path_to_file = '../models/Teacup.mdl'
directory = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
mdl_file = os.path.join(directory, relative_path_to_file)
model = PysdModel(mdl_file=mdl_file)
model.uncertainties = [RealParameter('Room Temperature', 33, 120)]
model.outcomes = [TimeSeriesOutcome('Teacup Temperature')]
with MultiprocessingEvaluator(model, 2) as evaluator:
perform_experiments(model, 5, evaluator=evaluator)
def test_parallel_experiment(self):
"""
Test running an experiment in parallel
Returns
-------
"""
relative_path_to_file = '../models/Teacup.mdl'
directory = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
mdl_file = os.path.join(directory, relative_path_to_file)
model = PysdModel(mdl_file=mdl_file)
model.uncertainties = [RealParameter('Room Temperature', 33, 120)]
model.outcomes = [TimeSeriesOutcome('Teacup Temperature')]
with MultiprocessingEvaluator(model, 2) as evaluator:
perform_experiments(model, 5, evaluator=evaluator)
def test_vensim_model(self):
#instantiate a model
wd = r'../models'
model = VensimExampleModel(wd, "simpleModel")
nr_runs = 10
experiments, outcomes = perform_experiments(model, nr_runs)
self.assertEqual(experiments.shape[0], nr_runs)
self.assertIn('TIME', outcomes.keys())
self.assertIn(model.outcomes[0].name, outcomes.keys())
def test_vensim_model(self):
#instantiate a model
wd = r'../models'
model = VensimExampleModel(wd, "simpleModel")
nr_runs = 10
experiments, outcomes = perform_experiments(model, nr_runs)
self.assertEqual(experiments.shape[0], nr_runs)
self.assertIn('TIME', outcomes.keys())
self.assertIn(model.outcomes[0].name, outcomes.keys())
RealParameter("initial annual supply", 100000, 120000),
RealParameter("initial in goods", 1500000, 2500000),
RealParameter("average construction time extraction capacity", 1, 10),
RealParameter("average lifetime extraction capacity", 20, 40),
RealParameter("average lifetime recycling capacity", 20, 40),
RealParameter("initial extraction capacity under construction", 5000,
20000),
RealParameter("initial recycling capacity under construction", 5000,
20000),
RealParameter("initial recycling infrastructure", 5000, 20000),
# order of delay
CategoricalParameter("order in goods delay", (1, 4, 10, 1000)),
CategoricalParameter("order recycling capacity delay", (1, 4, 10)),
CategoricalParameter("order extraction capacity delay", (1, 4, 10)),
# uncertainties associated with lookups
RealParameter("lookup shortage loc", 20, 50),
RealParameter("lookup shortage speed", 1, 5),
RealParameter("lookup price substitute speed", 0.1, 0.5),
RealParameter("lookup price substitute begin", 3, 7),
RealParameter("lookup price substitute end", 15, 25),
RealParameter("lookup returns to scale speed", 0.01, 0.2),
RealParameter("lookup returns to scale scale", 0.3, 0.7),
RealParameter("lookup approximated learning speed", 0.01, 0.2),
RealParameter("lookup approximated learning scale", 0.3, 0.6),
RealParameter("lookup approximated learning start", 30, 60)
]
results = perform_experiments(model, 50)
from ema_workbench.connectors.netlogo import NetLogoModel
from ema_workbench.em_framework import (TimeSeriesOutcome, RealParameter,
perform_experiments)
from ema_workbench.util import ema_logging
from ema_workbench.analysis import plotting, plotting_util
if __name__ == '__main__':
#turn on logging
ema_logging.log_to_stderr(ema_logging.DEBUG)
model = NetLogoModel('predprey',
wd="./models/predatorPreyNetlogo",
model_file="Wolf Sheep Predation.nlogo")
model.run_length = 100
model.uncertainties = [RealParameter("grass-regrowth-time", 1, 99),
RealParameter("initial-number-sheep", 1, 200),
RealParameter("initial-number-wolves", 1, 200),
RealParameter("sheep-reproduce", 1, 20),
RealParameter("wolf-reproduce", 1, 20),
]
model.outcomes = [TimeSeriesOutcome('sheep'),
TimeSeriesOutcome('wolves'),
TimeSeriesOutcome('grass') ]
#perform experiments
n = 100
results = perform_experiments(model, n, parallel=True)
Constant('nsamples', 100),
Constant('timehorizon', lake_model.time_horizon)
]
scenarios = ['Ref', 77, 96, 130, 181]
random_scenarios = [81, 289, 391, 257]
policies = []
for s in random_scenarios:
# if s == 'Ref':
# solutions = pd.DataFrame.from_csv(r'../results/Results_EpsNsgaII_nfe10000_scRef_v3.csv')
# else:
# solutions = pd.DataFrame.from_csv(r'../results/Results_EpsNsgaII_nfe10000_sc{}_v5.csv'.format(s))
#checked if there are duplicates: No.
solutions = pd.DataFrame.from_csv(
r'../data/brushed_random_nfe10000_sc{}.csv'.format(s))
for index, row in solutions.iterrows():
name = str(s) + '_' + str(index)
decision = {
lever.name: row[lever.name]
for lever in lake_model.levers
} #levers are in the first columns of the solutions
policies.append(Policy(name=name, **decision))
#with MultiprocessingEvaluator(lake_model) as evaluator:
# results = evaluator.perform_experiments(scenarios=1000, policies=policies)
results = perform_experiments(lake_model, 1000, policies, parallel=True)
save_results(
results,
r'../CandidateTesting_1000scenarios_revisionRandom_nfe10000.tar.gz')
1, 10),
RealParameter("average lifetime extraction capacity", 20, 40),
RealParameter("average lifetime recycling capacity", 20, 40),
RealParameter("initial extraction capacity under construction",
5000, 20000),
RealParameter("initial recycling capacity under construction",
5000, 20000),
RealParameter("initial recycling infrastructure", 5000, 20000),
# order of delay
CategoricalParameter("order in goods delay", (1, 4, 10, 1000)),
CategoricalParameter("order recycling capacity delay", (1, 4, 10)),
CategoricalParameter("order extraction capacity delay", (1, 4, 10)),
# uncertainties associated with lookups
RealParameter("lookup shortage loc", 20, 50),
RealParameter("lookup shortage speed", 1, 5),
RealParameter("lookup price substitute speed", 0.1, 0.5),
RealParameter("lookup price substitute begin", 3, 7),
RealParameter("lookup price substitute end", 15, 25),
RealParameter("lookup returns to scale speed", 0.01, 0.2),
RealParameter("lookup returns to scale scale", 0.3, 0.7),
RealParameter("lookup approximated learning speed", 0.01, 0.2),
RealParameter("lookup approximated learning scale", 0.3, 0.6),
RealParameter("lookup approximated learning start", 30, 60)]
results = perform_experiments(model, 50)
