def samples_in_test_mode(self, total_points: int,
model: AbstractPriorModel):
"""
Generate the initial points of the non-linear search in test mode. Like normal, test model draws points, by
randomly drawing unit values from a uniform distribution between the ball_lower_limit and ball_upper_limit
values.
However, the log likelihood function is bypassed and all likelihoods are returned with a value -1.0e99. This
is so that integration testing of large-scale model-fitting projects can be performed efficiently by bypassing
sampling of points using the `log_likelihood_function`.
Parameters
----------
total_points
The number of points in non-linear paramemter space which initial points are created for.
model
An object that represents possible instances of some model with a given dimensionality which is the number
of free dimensions of the model.
"""
unit_parameter_lists = []
parameter_lists = []
figure_of_merit_list = []
point_index = 0
while point_index < total_points:
unit_parameter_list = model.random_unit_vector_within_limits(
lower_limit=self.lower_limit, upper_limit=self.upper_limit)
parameter_list = model.vector_from_unit_vector(
unit_vector=unit_parameter_list)
unit_parameter_lists.append(unit_parameter_list)
parameter_lists.append(parameter_list)
figure_of_merit_list.append(-1.0e99)
point_index += 1
return unit_parameter_lists, parameter_lists, figure_of_merit_list
def samples_from_model(self,
total_points: int,
model: AbstractPriorModel,
fitness_function,
use_prior_medians: bool = False):
"""
Generate the initial points of the non-linear search, by randomly drawing unit values from a uniform
distribution between the ball_lower_limit and ball_upper_limit values.
Parameters
----------
total_points
The number of points in non-linear paramemter space which initial points are created for.
model
An object that represents possible instances of some model with a given dimensionality which is the number
of free dimensions of the model.
"""
if conf.instance["general"]["test"]["test_mode"]:
return self.samples_in_test_mode(total_points=total_points,
model=model)
logger.info(
"Generating initial samples of model, which are subject to prior limits and other constraints."
)
unit_parameter_lists = []
parameter_lists = []
figures_of_merit_list = []
point_index = 0
while point_index < total_points:
if not use_prior_medians:
unit_parameter_list = model.random_unit_vector_within_limits(
lower_limit=self.lower_limit, upper_limit=self.upper_limit)
try:
parameter_list = model.vector_from_unit_vector(
unit_vector=unit_parameter_list)
except exc.PriorLimitException:
continue
else:
unit_parameter_list = [0.5] * model.prior_count
parameter_list = model.vector_from_unit_vector(
unit_vector=unit_parameter_list)
try:
figure_of_merit = fitness_function.figure_of_merit_from(
parameter_list=parameter_list)
if np.isnan(figure_of_merit):
raise exc.FitException
unit_parameter_lists.append(unit_parameter_list)
parameter_lists.append(parameter_list)
figures_of_merit_list.append(figure_of_merit)
point_index += 1
except exc.FitException:
pass
return unit_parameter_lists, parameter_lists, figures_of_merit_list
