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=[10.0, 100.0, 1_000.0],
RA_over_RB=np.linspace(0.05, 0.95,
19).tolist()).build()
design_thing = BayesianAdaptiveDesignGenerator(D, max_trials=3)
assert isinstance(design_thing, BayesianAdaptiveDesignGenerator)
def test_DARCDesign_delayed_and_risky_instantiation():
D = DesignSpaceBuilder(
DA=[0.0],
DB=[7.0, 30, 30 * 6, 365],
PA=[1.0],
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=[100.0],
).build()
design_thing = BayesianAdaptiveDesignGenerator(D, max_trials=3)
assert isinstance(design_thing, BayesianAdaptiveDesignGenerator)
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 = BayesianAdaptiveDesignGenerator(D, max_trials=3)
n_designs = design_thing.all_possible_designs.shape[0]
assert n_designs > 10
def act_on_choices(desired_experiment_type, desired_model, expInfo):
# create desired experiment object ========================================
if desired_experiment_type == 'delayed (Bayesian Adaptive Design)':
from darc_toolbox.designs import BayesianAdaptiveDesignGeneratorDARC, DesignSpaceBuilder
# regular, or magnitude effect
if (desired_model == 'HyperbolicMagnitudeEffect') or (
desired_model == '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_toolbox.delayed import models
elif desired_experiment_type == 'delayed (Kirby 2009)':
from darc_toolbox.delayed.designs import Kirby2009
design_thing = Kirby2009()
from darc_toolbox.delayed import models
elif desired_experiment_type == 'delayed (Griskevicius et al, 2011)':
from darc_toolbox.delayed.designs import Griskevicius2011
design_thing = Griskevicius2011()
from darc_toolbox.delayed import models
elif desired_experiment_type == 'delayed (Frye et al, 2016)':
from darc_toolbox.delayed.designs import Frye
design_thing = Frye()
from darc_toolbox.delayed import models
elif desired_experiment_type == 'delayed (Du, Green, & Myerson, 2002)':
from darc_toolbox.delayed.designs import DuGreenMyerson2002
design_thing = DuGreenMyerson2002()
from darc_toolbox.delayed import models
elif desired_experiment_type == 'risky (Du, Green, & Myerson, 2002)':
from darc_toolbox.risky.designs import DuGreenMyerson2002
design_thing = DuGreenMyerson2002()
from darc_toolbox.risky import models
elif desired_experiment_type == 'risky (Griskevicius et al, 2011)':
from darc_toolbox.risky.designs import Griskevicius2011
design_thing = Griskevicius2011()
from darc_toolbox.risky import models
elif desired_experiment_type == 'risky (Bayesian Adaptive Design)':
from darc_toolbox.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_toolbox.risky import models
elif desired_experiment_type == 'delayed and risky (Bayesian Adaptive Design)':
from darc_toolbox.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_toolbox.delayed_and_risky import models
# chose the desired model here ============================================
if desired_model == 'Hyperbolic':
model = models.Hyperbolic(n_particles=expInfo['particles'])
elif desired_model == 'Exponential':
model = models.Exponential(n_particles=expInfo['particles'])
elif desired_model == 'MyersonHyperboloid':
model = models.MyersonHyperboloid(n_particles=expInfo['particles'])
elif desired_model == 'ModifiedRachlin':
model = models.ModifiedRachlin(n_particles=expInfo['particles'])
elif desired_model == 'HyperbolicMagnitudeEffect':
model = models.HyperbolicMagnitudeEffect(
n_particles=expInfo['particles'])
elif desired_model == 'ExponentialMagnitudeEffect':
model = models.ExponentialMagnitudeEffect(
n_particles=expInfo['particles'])
elif desired_model == 'HyperbolicNonLinearUtility':
model = models.HyperbolicNonLinearUtility(
n_particles=expInfo['particles'])
elif desired_model == 'MultiplicativeHyperbolic':
model = models.MultiplicativeHyperbolic(
n_particles=expInfo['particles'])
elif desired_model == '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)
def test_DARCDesign_delay_instantiation():
D = DesignSpaceBuilder(RA=list(100 * np.linspace(0.05, 0.95, 91)),
RB=[100.0]).build()
design_thing = BayesianAdaptiveDesignGenerator(D, max_trials=3)
assert isinstance(design_thing, BayesianAdaptiveDesignGenerator)
def test_DARCDesign_default_instantiation():
D = DesignSpaceBuilder(RA=list(100 * np.linspace(0.05, 0.95, 91))).build()
design_thing = BayesianAdaptiveDesignGenerator(D)
assert isinstance(design_thing, BayesianAdaptiveDesignGenerator)
def test_DARCDesign_delay_initial_design_space():
D = DesignSpaceBuilder(RA=list(100 * np.linspace(0.05, 0.95, 91))).build()
design_thing = BayesianAdaptiveDesignGenerator(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 = BayesianAdaptiveDesignGenerator(D, max_trials=3)
assert isinstance(design_thing, BayesianAdaptiveDesignGenerator)
def test_DARC_BAD_alt_delaymag():
D = DesignSpaceBuilder.delay_magnitude_effect().build()
design_thing = BayesianAdaptiveDesignGenerator(D, max_trials=3)
assert isinstance(design_thing, BayesianAdaptiveDesignGenerator)