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_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_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_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