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This was referenced Jul 31, 2024
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rhayes777
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Aug 5, 2024
autofit/non_linear/initializer.py
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| info += f"{key}: Start[{value}]\n" | ||
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| except KeyError: | ||
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| info += f"{key} : {prior})\n" |
Collaborator
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Inconsistent spacing before the :
| info_start = self.search.initializer.info_from_model(model=self.model) | ||
| self._save_model_start_point(info=info_start) | ||
| except (NotImplementedError, AttributeError): | ||
| pass |
Collaborator
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Could be worth logging something here as these errors may cover up code mistakes
| self.logger.debug("Visualizing") | ||
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| if analysis.should_visualize(paths=self.paths, during_analysis=during_analysis): | ||
| if paths_override is None: |
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paths = paths_override or self.paths|
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| class AbstractBFGS(AbstractMLE): | ||
| __identifier_fields__ = () |
Collaborator
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Probably best not to define an empty identifier_fields - I think as it stands it would mean attributes would be ignored when creating an identifier for an AbstractBFGS meaning all AbstractBFGS runs of the same type would have the same identifier
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| def test__priors__samples_from_model(): | ||
| model = af.Model(af.m.MockClassx4) | ||
| model.one = af.UniformPrior(lower_limit=0.099, upper_limit=0.101) | ||
| model.two = af.UniformPrior(lower_limit=0.199, upper_limit=0.201) | ||
| model.three = af.UniformPrior(lower_limit=0.299, upper_limit=0.301) | ||
| model.four = af.UniformPrior(lower_limit=0.399, upper_limit=0.401) | ||
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| initializer = af.InitializerPrior() | ||
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| ( | ||
| unit_parameter_lists, | ||
| parameter_lists, | ||
| figure_of_merit_list, | ||
| ) = initializer.samples_from_model( | ||
| total_points=2, | ||
| model=model, | ||
| fitness=MockFitness(), | ||
| paths=af.DirectoryPaths(), | ||
| ) | ||
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| assert 0.0 < unit_parameter_lists[0][0] < 1.0 | ||
| assert 0.0 < unit_parameter_lists[1][0] < 1.0 | ||
| assert 0.0 < unit_parameter_lists[0][1] < 1.0 | ||
| assert 0.0 < unit_parameter_lists[1][1] < 1.0 | ||
| assert 0.0 < unit_parameter_lists[0][2] < 1.0 | ||
| assert 0.0 < unit_parameter_lists[1][2] < 1.0 | ||
| assert 0.0 < unit_parameter_lists[0][3] < 1.0 | ||
| assert 0.0 < unit_parameter_lists[1][3] < 1.0 | ||
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| assert 0.099 < parameter_lists[0][0] < 0.101 | ||
| assert 0.099 < parameter_lists[1][0] < 0.101 | ||
| assert 0.199 < parameter_lists[0][1] < 0.201 | ||
| assert 0.199 < parameter_lists[1][1] < 0.201 | ||
| assert 0.299 < parameter_lists[0][2] < 0.301 | ||
| assert 0.299 < parameter_lists[1][2] < 0.301 | ||
| assert 0.399 < parameter_lists[0][3] < 0.401 | ||
| assert 0.399 < parameter_lists[1][3] < 0.401 |
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@pytest.fixture
def model_and_samples():
model = af.Model(af.m.MockClassx4)
model.one = af.UniformPrior(lower_limit=0.099, upper_limit=0.101)
model.two = af.UniformPrior(lower_limit=0.199, upper_limit=0.201)
model.three = af.UniformPrior(lower_limit=0.299, upper_limit=0.301)
model.four = af.UniformPrior(lower_limit=0.399, upper_limit=0.401)
initializer = af.InitializerPrior()
unit_parameter_lists, parameter_lists, _ = initializer.samples_from_model(
total_points=2,
model=model,
fitness=MockFitness(),
paths=af.DirectoryPaths(),
)
return unit_parameter_lists, parameter_lists
@pytest.mark.parametrize("index, param_index, lower, upper", [
(0, 0, 0.0, 1.0),
(1, 0, 0.0, 1.0),
(0, 1, 0.0, 1.0),
(1, 1, 0.0, 1.0),
(0, 2, 0.0, 1.0),
(1, 2, 0.0, 1.0),
(0, 3, 0.0, 1.0),
(1, 3, 0.0, 1.0),
])
def test_unit_parameter_lists(model_and_samples, index, param_index, lower, upper):
unit_parameter_lists, _ = model_and_samples
assert lower < unit_parameter_lists[index][param_index] < upper
@pytest.mark.parametrize("index, param_index, lower, upper", [
(0, 0, 0.099, 0.101),
(1, 0, 0.099, 0.101),
(0, 1, 0.199, 0.201),
(1, 1, 0.199, 0.201),
(0, 2, 0.299, 0.301),
(1, 2, 0.299, 0.301),
(0, 3, 0.399, 0.401),
(1, 3, 0.399, 0.401),
])
def test_parameter_lists(model_and_samples, index, param_index, lower, upper):
_, parameter_lists = model_and_samples
assert lower < parameter_lists[index][param_index] < upper
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Methods which found the maximum likelihood model were previously called
optimize, which this PR renames to MLE (maximum likelihood estimator).This PR improves the MLE methods with functionality including:
InitializerAPI.The
BFGSandLBFGSMLE searches have been improved in this PR, including visualization.The main use case is fits where a good starting point is known and simple gradient descent can find the maximum likelihood solution.
The intent is to use this for strong lens sensitivity mapping.