def test_initialize_engines(self):
function = mock.Mock()
mock_msi = Model('test', function)
msis = {mock_msi.name: mock_msi}
mock_client = mock.create_autospec(ipyparallel.Client)
mock_client.ids = [0, 1] # pretend we have two engines
mock_view = mock.create_autospec(
ipyparallel.client.view.View) #@ @UndefinedVariable
mock_client.__getitem__.return_value = mock_view
cwd = '.'
ema.initialize_engines(mock_client, msis, cwd)
mock_view.apply_sync.assert_any_call(ema._initialize_engine, 0, msis,
cwd)
mock_view.apply_sync.assert_any_call(ema._initialize_engine, 1, msis,
cwd)
def test_copy_wds_for_msis(self, mock_os, mock_shutil):
mock_os.path.join.return_value = '.'
function = mock.Mock()
mock_msi = Model('test', function)
msis = {mock_msi.name: mock_msi}
engine_id = 0
engine = ema.Engine(engine_id, msis)
engine.root_dir = '/dir_name'
dirs_to_copy = ['/test']
wd_by_msi = {'/test': [mock_msi.name]}
engine.copy_wds_for_msis(dirs_to_copy, wd_by_msi)
mock_os.path.basename.called_once_with(dirs_to_copy[0])
mock_os.path.join.called_once_with('/dir_name', dirs_to_copy[0])
mock_shutil.copytree.assert_called_once_with('/test', '.')
self.assertEqual('.', mock_msi.working_directory)
def test_run_experiment(self):
function = mock.Mock()
mock_msi = Model('test', function)
mock_runner = mock.create_autospec(experiment_runner.ExperimentRunner)
msis = [mock_msi]
engine_id = 0
engine = ema.Engine(engine_id, msis, '.')
engine.runner = mock_runner
experiment = {'a': 1}
engine.run_experiment(experiment)
mock_runner.run_experiment.assert_called_once_with(experiment)
mock_runner.run_experiment.side_effect = EMAError
self.assertRaises(EMAError, engine.run_experiment, experiment)
mock_runner.run_experiment.side_effect = Exception
self.assertRaises(EMAParallelError, engine.run_experiment, experiment)
minimize = 1
outcome = outcomes[entry]
mean_performance = minimize * np.mean(outcome)
std_performance = np.std(outcome)
objs.append(float(mean_performance))
objs.append(float(std_performance))
return objs
if __name__ == '__main__':
ema_logging.log_to_stderr(ema_logging.INFO)
#instantiate the model
model = Model('lakeproblem', function=lake_problem)
#specify uncertainties
model.uncertainties = [
RealParameter("b", 0.1, 0.45),
RealParameter("q", 2.0, 4.5),
RealParameter("mean", 0.01, 0.05),
RealParameter("stdev", 0.001, 0.005),
RealParameter("delta", 0.93, 0.99)
]
#specify outcomes
model.outcomes = [
ScalarOutcome("max_P", ),
ScalarOutcome("utility"),
ScalarOutcome("inertia"),
ScalarOutcome("reliability")