def test__ball__samples_sample_centre_of_priors(self):
model = af.PriorModel(af.m.MockClassx4)
model.one = af.UniformPrior(lower_limit=0.0, upper_limit=1.0)
model.two = af.UniformPrior(lower_limit=0.0, upper_limit=2.0)
model.three = af.UniformPrior(lower_limit=0.0, upper_limit=3.0)
model.four = af.UniformPrior(lower_limit=0.0, upper_limit=4.0)
initializer = af.InitializerBall(lower_limit=0.4999,
upper_limit=0.5001)
unit_parameter_lists, parameter_lists, figure_of_merit_list = initializer.samples_from_model(
total_points=2, model=model, fitness_function=MockFitness())
assert 0.4999 < unit_parameter_lists[0][0] < 0.5001
assert 0.4999 < unit_parameter_lists[1][0] < 0.5001
assert 0.4999 < unit_parameter_lists[0][1] < 0.5001
assert 0.4999 < unit_parameter_lists[1][1] < 0.5001
assert 0.4999 < unit_parameter_lists[0][2] < 0.5001
assert 0.4999 < unit_parameter_lists[1][2] < 0.5001
assert 0.4999 < unit_parameter_lists[0][3] < 0.5001
assert 0.4999 < unit_parameter_lists[1][3] < 0.5001
assert 0.499 < parameter_lists[0][0] < 0.501
assert 0.499 < parameter_lists[1][0] < 0.501
assert 0.999 < parameter_lists[0][1] < 1.001
assert 0.999 < parameter_lists[1][1] < 1.001
assert 1.499 < parameter_lists[0][2] < 1.501
assert 1.499 < parameter_lists[1][2] < 1.501
assert 1.999 < parameter_lists[0][3] < 2.001
assert 1.999 < parameter_lists[1][3] < 2.001
initializer = af.InitializerBall(lower_limit=0.7999,
upper_limit=0.8001)
unit_parameter_lists, parameter_lists, figure_of_merit_list = initializer.samples_from_model(
total_points=2, model=model, fitness_function=MockFitness())
assert 0.799 < parameter_lists[0][0] < 0.801
assert 0.799 < parameter_lists[1][0] < 0.801
assert 1.599 < parameter_lists[0][1] < 1.601
assert 1.599 < parameter_lists[1][1] < 1.601
assert 2.399 < parameter_lists[0][2] < 2.401
assert 2.399 < parameter_lists[1][2] < 2.401
assert 3.199 < parameter_lists[0][3] < 3.201
assert 3.199 < parameter_lists[1][3] < 3.201
assert figure_of_merit_list == 2 * [1.0]
def test__loads_from_config_file_correct(self):
drawer = af.Drawer(
total_draws=5,
prior_passer=af.PriorPasser(sigma=2.0,
use_errors=False,
use_widths=False),
initializer=af.InitializerBall(lower_limit=0.2, upper_limit=0.8),
)
assert drawer.prior_passer.sigma == 2.0
assert drawer.prior_passer.use_errors is False
assert drawer.prior_passer.use_widths is False
assert drawer.config_dict_search["total_draws"] == 5
assert isinstance(drawer.initializer, af.InitializerBall)
assert drawer.initializer.lower_limit == 0.2
assert drawer.initializer.upper_limit == 0.8
assert drawer.number_of_cores == 1
drawer = af.Drawer()
assert drawer.prior_passer.sigma == 3.0
assert drawer.prior_passer.use_errors is True
assert drawer.prior_passer.use_widths is True
assert drawer.config_dict_search["total_draws"] == 10
assert isinstance(drawer.initializer, af.InitializerPrior)
def test__loads_from_config_file_correct(self):
lbfgs = af.LBFGS(
prior_passer=af.PriorPasser(sigma=2.0, use_errors=False, use_widths=False),
tol=0.2,
disp=True,
maxcor=11,
ftol=2.,
gtol=3.,
eps=4.,
maxfun=25000,
maxiter=26000,
iprint=-2,
maxls=21,
initializer=af.InitializerBall(lower_limit=0.2, upper_limit=0.8),
iterations_per_update=10,
number_of_cores=2,
)
assert lbfgs.prior_passer.sigma == 2.0
assert lbfgs.prior_passer.use_errors is False
assert lbfgs.prior_passer.use_widths is False
assert lbfgs.config_dict_search["tol"] == 0.2
assert lbfgs.config_dict_options["maxcor"] == 11
assert lbfgs.config_dict_options["ftol"] == 2.
assert lbfgs.config_dict_options["gtol"] == 3.
assert lbfgs.config_dict_options["eps"] == 4.
assert lbfgs.config_dict_options["maxfun"] == 25000
assert lbfgs.config_dict_options["maxiter"] == 26000
assert lbfgs.config_dict_options["iprint"] == -2
assert lbfgs.config_dict_options["maxls"] == 21
assert lbfgs.config_dict_options["disp"] == True
assert isinstance(lbfgs.initializer, af.InitializerBall)
assert lbfgs.initializer.lower_limit == 0.2
assert lbfgs.initializer.upper_limit == 0.8
assert lbfgs.iterations_per_update == 10
assert lbfgs.number_of_cores == 2
lbfgs = af.LBFGS()
assert lbfgs.prior_passer.sigma == 3.0
assert lbfgs.prior_passer.use_errors is True
assert lbfgs.prior_passer.use_widths is True
assert lbfgs.config_dict_search["tol"] == None
assert lbfgs.config_dict_options["maxcor"] == 10
assert lbfgs.config_dict_options["ftol"] == 2.220446049250313e-09
assert lbfgs.config_dict_options["gtol"] == 1e-05
assert lbfgs.config_dict_options["eps"] == 1e-08
assert lbfgs.config_dict_options["maxfun"] == 15000
assert lbfgs.config_dict_options["maxiter"] == 15000
assert lbfgs.config_dict_options["iprint"] == -1
assert lbfgs.config_dict_options["maxls"] == 20
assert lbfgs.config_dict_options["maxiter"] == 15000
assert lbfgs.config_dict_options["disp"] == False
assert isinstance(lbfgs.initializer, af.InitializerPrior)
assert lbfgs.iterations_per_update == 11
def test__loads_from_config_file_correct(self):
zeus = af.Zeus(
prior_passer=af.PriorPasser(sigma=2.0,
use_errors=False,
use_widths=False),
nwalkers=51,
nsteps=2001,
initializer=af.InitializerBall(lower_limit=0.2, upper_limit=0.8),
auto_correlations_settings=af.AutoCorrelationsSettings(
check_for_convergence=False,
check_size=101,
required_length=51,
change_threshold=0.02),
tune=False,
number_of_cores=2,
)
assert zeus.prior_passer.sigma == 2.0
assert zeus.prior_passer.use_errors is False
assert zeus.prior_passer.use_widths is False
assert zeus.config_dict_search["nwalkers"] == 51
assert zeus.config_dict_run["nsteps"] == 2001
assert zeus.config_dict_run["tune"] == False
assert isinstance(zeus.initializer, af.InitializerBall)
assert zeus.initializer.lower_limit == 0.2
assert zeus.initializer.upper_limit == 0.8
assert zeus.auto_correlations_settings.check_for_convergence is False
assert zeus.auto_correlations_settings.check_size == 101
assert zeus.auto_correlations_settings.required_length == 51
assert zeus.auto_correlations_settings.change_threshold == 0.02
assert zeus.number_of_cores == 2
zeus = af.Zeus()
assert zeus.prior_passer.sigma == 3.0
assert zeus.prior_passer.use_errors is True
assert zeus.prior_passer.use_widths is True
assert zeus.config_dict_search["nwalkers"] == 50
assert zeus.config_dict_run["nsteps"] == 2000
assert zeus.config_dict_run["tune"] == True
assert isinstance(zeus.initializer, af.InitializerPrior)
assert zeus.auto_correlations_settings.check_for_convergence is True
assert zeus.auto_correlations_settings.check_size == 100
assert zeus.auto_correlations_settings.required_length == 50
assert zeus.auto_correlations_settings.change_threshold == 0.01
assert zeus.number_of_cores == 1
We create the Analysis as per using.
"""
analysis = al.AnalysisImaging(dataset=imaging)
"""
__Search__
Below we use zeus to fit the lens model, using the model with start points as described above. See the Zeus docs
for a description of what the input parameters below do.
"""
search = af.Zeus(
path_prefix=path.join("imaging", "searches"),
name="Zeus",
unique_tag=dataset_name,
nwalkers=30,
nsteps=200,
initializer=af.InitializerBall(lower_limit=0.49, upper_limit=0.51),
auto_correlations_settings=af.AutoCorrelationsSettings(
check_for_convergence=True,
check_size=100,
required_length=50,
change_threshold=0.01,
),
tune=False,
tolerance=0.05,
patience=5,
maxsteps=10000,
mu=1.0,
maxiter=10000,
vectorize=False,
check_walkers=True,
shuffle_ensemble=True,
def test__loads_from_config_file_correct(self):
pso = af.PySwarmsGlobal(
prior_passer=af.PriorPasser(sigma=2.0,
use_errors=False,
use_widths=False),
n_particles=51,
iters=2001,
cognitive=0.4,
social=0.5,
inertia=0.6,
initializer=af.InitializerBall(lower_limit=0.2, upper_limit=0.8),
iterations_per_update=10,
number_of_cores=2,
)
assert pso.prior_passer.sigma == 2.0
assert pso.prior_passer.use_errors is False
assert pso.prior_passer.use_widths is False
assert pso.config_dict_search["n_particles"] == 51
assert pso.config_dict_search["cognitive"] == 0.4
assert pso.config_dict_run["iters"] == 2001
assert isinstance(pso.initializer, af.InitializerBall)
assert pso.initializer.lower_limit == 0.2
assert pso.initializer.upper_limit == 0.8
assert pso.iterations_per_update == 10
assert pso.number_of_cores == 2
pso = af.PySwarmsGlobal()
assert pso.prior_passer.sigma == 3.0
assert pso.prior_passer.use_errors is True
assert pso.prior_passer.use_widths is True
assert pso.config_dict_search["n_particles"] == 50
assert pso.config_dict_search["cognitive"] == 0.1
assert pso.config_dict_run["iters"] == 2000
assert isinstance(pso.initializer, af.InitializerPrior)
assert pso.iterations_per_update == 11
assert pso.number_of_cores == 1
pso = af.PySwarmsLocal(
prior_passer=af.PriorPasser(sigma=2.0,
use_errors=False,
use_widths=False),
n_particles=51,
iters=2001,
cognitive=0.4,
social=0.5,
inertia=0.6,
number_of_k_neighbors=4,
minkowski_p_norm=1,
initializer=af.InitializerBall(lower_limit=0.2, upper_limit=0.8),
iterations_per_update=10,
number_of_cores=2,
)
assert pso.prior_passer.sigma == 2.0
assert pso.prior_passer.use_errors is False
assert pso.prior_passer.use_widths is False
assert pso.config_dict_search["n_particles"] == 51
assert pso.config_dict_search["cognitive"] == 0.4
assert pso.config_dict_run["iters"] == 2001
assert isinstance(pso.initializer, af.InitializerBall)
assert pso.initializer.lower_limit == 0.2
assert pso.initializer.upper_limit == 0.8
assert pso.iterations_per_update == 10
assert pso.number_of_cores == 2
pso = af.PySwarmsLocal()
assert pso.prior_passer.sigma == 3.0
assert pso.prior_passer.use_errors is True
assert pso.prior_passer.use_widths is True
assert pso.config_dict_search["n_particles"] == 50
assert pso.config_dict_search["cognitive"] == 0.1
assert pso.config_dict_run["iters"] == 2000
assert isinstance(pso.initializer, af.InitializerPrior)
assert pso.iterations_per_update == 11
assert pso.number_of_cores == 1
