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 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)
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 (
BayesianAdaptiveDesignGenerator,
DesignSpaceBuilder,
)
# regular, or magnitude effect
if (desired_model is "HyperbolicMagnitudeEffect") or (
desired_model is "ExponentialMagnitudeEffect"):
D = DesignSpaceBuilder.delay_magnitude_effect().build()
design_thing = BayesianAdaptiveDesignGenerator(
D, max_trials=expInfo["trials"])
else:
D = DesignSpaceBuilder.delayed().build()
design_thing = BayesianAdaptiveDesignGenerator(
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 (
BayesianAdaptiveDesignGenerator,
DesignSpaceBuilder,
)
# create an appropriate design object
D = DesignSpaceBuilder.risky().build()
design_thing = BayesianAdaptiveDesignGenerator(
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 BayesianAdaptiveDesignGenerator
# create an appropriate design object
D = DesignSpaceBuilder.delayed_and_risky().build()
design_thing = BayesianAdaptiveDesignGenerator(
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 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)