def test_DARC_BAD_alt_delayed():
D = DesignSpaceBuilder.delayed().build()
design_thing = BayesianAdaptiveDesignGeneratorDARC(D, max_trials=3)
assert isinstance(design_thing, BayesianAdaptiveDesignGeneratorDARC)
def act_on_choices(desired_experiment_type, desired_model, expInfo):
# create desired experiment object ========================================
if desired_experiment_type == 'delayed (Bayesian Adaptive Design)':
from darc.designs import BayesianAdaptiveDesignGeneratorDARC, DesignSpaceBuilder
# regular, or magnitude effect
if (desired_model is 'HyperbolicMagnitudeEffect') or (desired_model is 'ExponentialMagnitudeEffect'):
D = DesignSpaceBuilder.delay_magnitude_effect().build()
design_thing = BayesianAdaptiveDesignGeneratorDARC(D,
max_trials=expInfo['trials'])
else:
D = DesignSpaceBuilder.delayed().build()
design_thing = BayesianAdaptiveDesignGeneratorDARC(D,
max_trials=expInfo['trials'])
# import the appropriate set of models
from darc.delayed import models
elif desired_experiment_type == 'delayed (Kirby 2009)':
from darc.delayed.designs import Kirby2009
design_thing = Kirby2009()
from darc.delayed import models
elif desired_experiment_type == 'delayed (Griskevicius et al, 2011)':
from darc.delayed.designs import Griskevicius2011
design_thing = Griskevicius2011()
from darc.delayed import models
elif desired_experiment_type == 'delayed (Frye et al, 2016)':
from darc.delayed.designs import Frye
design_thing = Frye()
from darc.delayed import models
elif desired_experiment_type == 'delayed (Du, Green, & Myerson, 2002)':
from darc.delayed.designs import DuGreenMyerson2002
design_thing = DuGreenMyerson2002()
from darc.delayed import models
elif desired_experiment_type == 'risky (Du, Green, & Myerson, 2002)':
from darc.risky.designs import DuGreenMyerson2002
design_thing = DuGreenMyerson2002()
from darc.risky import models
elif desired_experiment_type == 'risky (Griskevicius et al, 2011)':
from darc.risky.designs import Griskevicius2011
design_thing = Griskevicius2011()
from darc.risky import models
elif desired_experiment_type == 'risky (Bayesian Adaptive Design)':
from darc.designs import BayesianAdaptiveDesignGeneratorDARC, DesignSpaceBuilder
# create an appropriate design object
D = DesignSpaceBuilder.risky().build()
design_thing = BayesianAdaptiveDesignGeneratorDARC(D,
max_trials=expInfo['trials'])
# import the appropriate set of models
from darc.risky import models
elif desired_experiment_type == 'delayed and risky (Bayesian Adaptive Design)':
from darc.designs import BayesianAdaptiveDesignGeneratorDARC
# create an appropriate design object
D = DesignSpaceBuilder.delayed_and_risky().build()
design_thing = BayesianAdaptiveDesignGeneratorDARC(D,
max_trials=expInfo['trials'])
# import the appropriate set of models
from darc.delayed_and_risky import models
# chose the desired model here ============================================
if desired_model is 'Hyperbolic':
model = models.Hyperbolic(n_particles=expInfo['particles'])
elif desired_model is 'Exponential':
model = models.Exponential(n_particles=expInfo['particles'])
elif desired_model is 'MyersonHyperboloid':
model = models.MyersonHyperboloid(n_particles=expInfo['particles'])
elif desired_model is 'ModifiedRachlin':
model = models.ModifiedRachlin(n_particles=expInfo['particles'])
elif desired_model is 'HyperbolicMagnitudeEffect':
model = models.HyperbolicMagnitudeEffect(n_particles=expInfo['particles'])
elif desired_model is 'ExponentialMagnitudeEffect':
model = models.ExponentialMagnitudeEffect(
n_particles=expInfo['particles'])
elif desired_model is 'HyperbolicNonLinearUtility':
model = models.HyperbolicNonLinearUtility(
n_particles=expInfo['particles'])
elif desired_model is 'MultiplicativeHyperbolic':
model = models.MultiplicativeHyperbolic(
n_particles=expInfo['particles'])
elif desired_model is 'LinearInLogOdds':
model = models.LinearInLogOdds(n_particles=expInfo['particles'])
else:
logging.error(f'Value of desired_model ({desired_model}) not recognised')
raise ValueError('Filed to act on desired_model')
return (design_thing, model)