def test_DARCDesign_delayed_and_risky_instantiation():
D = DesignSpaceBuilder(DA=[0.],
DB=[7., 30, 30 * 6, 365],
PA=[1.],
PB=[0.1, 0.25, 0.5, 0.75, 0.8, 0.9, 0.99],
RA=list(100 * np.linspace(0.05, 0.95, 91)),
RB=[100.]).build()
design_thing = BayesianAdaptiveDesignGeneratorDARC(D, max_trials=3)
assert isinstance(design_thing, BayesianAdaptiveDesignGeneratorDARC)
def test_DARCDesign_risky_initial_design_space():
D = DesignSpaceBuilder(DA=[0],
DB=[0],
PA=[1],
PB=[0.1, 0.25, 0.5, 0.75, 0.8, 0.9, 0.99],
RA=list(100 * np.linspace(0.05, 0.95, 91)),
RB=[100]).build()
design_thing = BayesianAdaptiveDesignGeneratorDARC(D, max_trials=3)
n_designs = design_thing.all_possible_designs.shape[0]
assert n_designs > 10
def test_DARCDesign_delay_magnitude_effect_instantiation():
'''When we are investigating the magnitide effect, we want to ask for a
reasonable range of DB values. When we do this, we are going to provide
a vector of proportions (RA_over_RB) which will be translated into
actual RA values. '''
D = DesignSpaceBuilder(RB=[10., 100., 1_000.],
RA_over_RB=np.linspace(0.05, 0.95,
19).tolist()).build()
design_thing = BayesianAdaptiveDesignGeneratorDARC(D, max_trials=3)
assert isinstance(design_thing, BayesianAdaptiveDesignGeneratorDARC)
def test_model_design_integration_delayed(model):
'''Tests integration of model and design. Basically conducts Parameter
Estimation'''
D = DesignSpaceBuilder(RA=list(100 * np.linspace(0.05, 0.95, 19))).build()
design_thing = BayesianAdaptiveDesignGeneratorDARC(D,
max_trials=max_trials)
model = model(n_particles=n_particles)
model = model.generate_faux_true_params()
simulated_experiment_trial_loop(design_thing, model)
def test_DARCDesign_delay_instantiation():
D = DesignSpaceBuilder(RA=list(100 * np.linspace(0.05, 0.95, 91)),
RB=[100.]).build()
design_thing = BayesianAdaptiveDesignGeneratorDARC(D, max_trials=3)
assert isinstance(design_thing, BayesianAdaptiveDesignGeneratorDARC)
def test_DARCDesign_default_instantiation():
D = DesignSpaceBuilder(RA=list(100 * np.linspace(0.05, 0.95, 91))).build()
design_thing = BayesianAdaptiveDesignGeneratorDARC(D)
assert isinstance(design_thing, BayesianAdaptiveDesignGeneratorDARC)
def test_DARCDesign_delay_initial_design_space():
D = DesignSpaceBuilder(RA=list(100 * np.linspace(0.05, 0.95, 91))).build()
design_thing = BayesianAdaptiveDesignGeneratorDARC(D)
n_designs = design_thing.all_possible_designs.shape[0]
assert n_designs > 10
def test_DARC_BAD_alt_frontenddelay():
D = DesignSpaceBuilder.frontend_delay().build()
design_thing = BayesianAdaptiveDesignGeneratorDARC(D, max_trials=3)
assert isinstance(design_thing, BayesianAdaptiveDesignGeneratorDARC)
def test_DARC_BAD_alt_delaymag():
D = DesignSpaceBuilder.delay_magnitude_effect().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)