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 _fit(self, model: AbstractPriorModel, analysis, log_likelihood_cap=None):
"""
Fit a model using PySwarms and the Analysis class which contains the data and returns the log likelihood from
instances of the model, which the `NonLinearSearch` seeks to maximize.
Parameters
----------
model : ModelMapper
The model which generates instances for different points in parameter space.
analysis : Analysis
Contains the data and the log likelihood function which fits an instance of the model to the data, returning
the log likelihood the `NonLinearSearch` maximizes.
Returns
-------
A result object comprising the Samples object that inclues the maximum log likelihood instance and full
chains used by the fit.
"""
fitness_function = self.fitness_function_from_model_and_analysis(
model=model, analysis=analysis
)
if self.paths.is_object("points"):
init_pos = self.paths.load_object("points")[-1]
total_iterations = self.paths.load_object("total_iterations")
self.logger.info("Existing PySwarms samples found, resuming non-linear search.")
else:
unit_parameter_lists, parameter_lists, log_posterior_list = self.initializer.samples_from_model(
total_points=self.config_dict_search["n_particles"],
model=model,
fitness_function=fitness_function,
)
init_pos = np.zeros(shape=(self.config_dict_search["n_particles"], model.prior_count))
for index, parameters in enumerate(parameter_lists):
init_pos[index, :] = np.asarray(parameters)
total_iterations = 0
self.logger.info("No PySwarms samples found, beginning new non-linear search. ")
## TODO : Use actual limits
vector_lower = model.vector_from_unit_vector(
unit_vector=[1e-6] * model.prior_count,
ignore_prior_limits=True
)
vector_upper = model.vector_from_unit_vector(
unit_vector=[0.9999999] * model.prior_count,
ignore_prior_limits=True
)
lower_bounds = [lower for lower in vector_lower]
upper_bounds = [upper for upper in vector_upper]
bounds = (np.asarray(lower_bounds), np.asarray(upper_bounds))
self.logger.info("Running PySwarmsGlobal Optimizer...")
while total_iterations < self.config_dict_run["iters"]:
pso = self.sampler_from(
model=model,
fitness_function=fitness_function,
bounds=bounds,
init_pos=init_pos
)
iterations_remaining = self.config_dict_run["iters"] - total_iterations
iterations = min(self.iterations_per_update, iterations_remaining)
if iterations > 0:
pso.optimize(objective_func=fitness_function.__call__, iters=iterations)
total_iterations += iterations
self.paths.save_object(
"total_iterations",
total_iterations
)
self.paths.save_object(
"points",
pso.pos_history
)
self.paths.save_object(
"log_posterior_list",
[-0.5 * cost for cost in pso.cost_history]
)
self.perform_update(
model=model, analysis=analysis, during_analysis=True
)
init_pos = self.paths.load_object("points")[-1]
self.logger.info("PySwarmsGlobal complete")
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