def run(self, dataset, results, mask=None, positions=None, info=None):
self.save_metadata(dataset)
self.dataset = dataset
self.results = results
self.mask = mask
self.positions = positions
return af.Result(af.ModelInstance(), 1)
def test_projected_model():
model = af.Model(
af.Gaussian,
centre=af.UniformPrior()
)
samples = af.Samples(
model,
[
af.Sample(
-1.0, -1.0,
weight=random(),
kwargs={
("centre",): random(),
("normalization",): random(),
("sigma",): random(),
}
)
for _ in range(100)
]
)
result = af.Result(
samples=samples,
model=model
)
projected_model = result.projected_model
assert projected_model.prior_count == 3
assert projected_model.centre is not model.centre
assert projected_model.centre.id == model.centre.id
assert isinstance(
projected_model.centre,
af.UniformPrior
)
def run(self, data, results, mask=None, positions=None):
self.data = data
self.results = results
self.mask = mask
self.positions = positions
self.assert_and_save_pickle()
return af.Result(af.ModelInstance(), 1)
def test_unpickle_result():
result = af.GridSearchResult(
[af.Result(samples=None, model=None)],
lower_limit_lists=[[1]],
physical_lower_limits_lists=[[1]],
)
result = pickle.loads(pickle.dumps(result))
assert result is not None
def test_unpickle_result():
# noinspection PyTypeChecker
result = af.GridSearchResult(
results=[af.Result(samples=None, model=None)],
lower_limits_lists=[[1]],
grid_priors=[],
)
result = pickle.loads(pickle.dumps(result))
assert result is not None
def make_result():
mapper = af.ModelMapper()
mapper.component = af.m.MockClassx2Tuple
# noinspection PyTypeChecker
return af.Result(
samples=af.m.MockSamples(gaussian_tuples=[(0, 0), (1, 0)]),
model=mapper,
search=af.m.MockSearch(),
)
def make_result(
model,
samples
):
return af.Result(
samples,
model,
af.m.MockSearch()
)
def make_modified_result(
model,
samples
):
model.gaussian.sigma = af.GaussianPrior(
mean=0.5,
sigma=1
)
model.gaussian.centre = af.GaussianPrior(
mean=0.5,
sigma=1
)
return af.Result(
samples,
model,
af.m.MockSearch()
)
def fit(self, model: af.AbstractPriorModel, analysis: af.Analysis):
best_likelihood = float("-inf")
best_instance = None
likelihoods = list()
for list_ in make_lists(no_dimensions=model.prior_count,
step_size=self.step_size):
instance = model.instance_from_unit_vector(list_)
likelihood = analysis.log_likelihood_function(instance)
likelihoods.append(likelihood)
if likelihood > best_likelihood:
best_likelihood = likelihood
best_instance = instance
return af.Result(samples=MockSamples(
max_log_likelihood_instance=best_instance,
log_likelihoods=likelihoods,
gaussian_tuples=None),
previous_model=model)
def test_higher_dimensions(
n_dimensions,
n_steps
):
shape = n_dimensions * (n_steps,)
total = n_steps ** n_dimensions
model = af.Model(
af.Gaussian
)
result = af.GridSearchResult(
results=total * [
af.Result(
af.NestSamples(
model,
[
af.Sample(
1.0,
1.0,
1.0,
{
"centre": 1.0,
"sigma": 1.0,
"normalization": 1.0
}
)
]
),
model=model
)
],
grid_priors=[],
lower_limits_lists=total * [
n_dimensions * [0.0]
]
)
assert result.shape == shape
assert result.results_native.shape == shape
assert result.log_likelihoods_native.shape == shape
def test__identifier_description__after_model_and_instance():
model = af.CollectionPriorModel(gaussian=af.PriorModel(
af.Gaussian,
centre=af.UniformPrior(lower_limit=0.0, upper_limit=1.0),
normalization=af.LogUniformPrior(lower_limit=0.001, upper_limit=0.01),
sigma=af.GaussianPrior(
mean=0.5, sigma=2.0, lower_limit=-1.0, upper_limit=1.0),
))
max_log_likelihood_instance = model.instance_from_prior_medians()
samples = af.m.MockSamples(
max_log_likelihood_instance=max_log_likelihood_instance,
gaussian_tuples=[(1.0, 2.0), (3.0, 4.0), (5.0, 6.0)])
search = af.m.MockSearch(prior_passer=af.PriorPasser(
sigma=1.0, use_errors=True, use_widths=False))
result = af.Result(samples=samples, model=model, search=search)
model.gaussian.centre = result.model.gaussian.centre
model.gaussian.normalization = result.instance.gaussian.normalization
identifier = Identifier([model])
description = identifier.description.splitlines()
# THIS TEST FAILS DUE TO THE BUG DESCRIBED IN A GITHUB ISSUE.
i = 0
assert description[i] == "CollectionPriorModel"
i += 1
assert description[i] == "item_number"
i += 1
assert description[i] == "0"
i += 1
assert description[i] == "gaussian"
i += 1
assert description[i] == "PriorModel"
i += 1
assert description[i] == "cls"
i += 1
assert description[i] == "autofit.example.model.Gaussian"
i += 1
assert description[i] == "centre"
i += 1
assert description[i] == "GaussianPrior"
i += 1
assert description[i] == "lower_limit"
i += 1
assert description[i] == "0.0"
i += 1
assert description[i] == "upper_limit"
i += 1
assert description[i] == "1.0"
i += 1
assert description[i] == "mean"
i += 1
assert description[i] == "1.0"
i += 1
assert description[i] == "sigma"
i += 1
assert description[i] == "2.0"
i += 1
assert description[i] == "normalization"
i += 1
assert description[i] == "0.00316228"
i += 1
assert description[i] == "sigma"
i += 1
assert description[i] == "GaussianPrior"
i += 1
assert description[i] == "lower_limit"
i += 1
assert description[i] == "-1.0"
i += 1
assert description[i] == "upper_limit"
i += 1
assert description[i] == "1.0"
i += 1
assert description[i] == "mean"
i += 1
assert description[i] == "0.5"
i += 1
assert description[i] == "sigma"
i += 1
assert description[i] == "2.0"
i += 1
def run(self, positions, pixel_scales, results):
self.save_metadata(MockImagingData())
self.positions = positions
self.pixel_scales = pixel_scales
self.results = results
return af.Result(af.ModelInstance(), 1)
def run(self, positions, pixel_scale, results):
self.positions = positions
self.pixel_scale = pixel_scale
self.results = results
return af.Result(af.ModelInstance(), 1)
def make_result():
model = af.Mapper()
model.one = af.PriorModel(mock.MockComponents, component=mock.MockClassx2)
return af.Result(model=model,
samples=mock.MockSamples(),
search=mock.MockSearch())
def fit(self, analysis, model):
# noinspection PyTypeChecker
return af.Result(None, analysis.fit(None), None)
def run(self, dataset, results, mask=None):
self.dataset = dataset
self.results = results
self.mask = mask
self.assert_and_save_pickle()
return af.Result(af.ModelInstance(), 1)
def _simple_fit(self, analysis, fitness_function):
# noinspection PyTypeChecker
return af.Result(None, analysis.fit(None), None)
