optimpv.scipyOpti package

Submodules

optimpv.scipyOpti.scipyOptimizer module

scipyOptimizer module. This module contains the ScipyOptimizer class, which is used to optimize a given function using the scipy library.

class optimpv.scipyOpti.scipyOptimizer.ScipyOptimizer(params=None, agents=None, method='L-BFGS-B', options=None, name='scipy_opti', **kwargs)

Bases: BaseAgent

Initialize the ScipyOptimizer class. This class is used to optimize a given function using the scipy library.

Parameters:

• params (list of Fitparam() objects, optional) – List of Fitparam() objects, by default None

• agents (list of Agent() objects, optional) – List of Agent() objects see optimpv/general/BaseAgent.py for a base class definition, by default None

• method (str, optional) – Optimization method to use with scipy.minimize, by default ‘L-BFGS-B’

• options (dict, optional) – Options to pass to scipy.minimize, by default None

• name (str, optional) – Name of the optimization process, by default ‘scipy_opti’

• **kwargs (dict) – Additional keyword arguments including: - parallel_agents: bool, whether to run agents in parallel - max_parallelism: int, maximum number of parallel processes - verbose_logging: bool, whether to log verbose information

create_metrics_list()

Create a list of all metrics from all agents.

Returns:

List of metric names

Return type:

list

create_objective(multi_objective=False)

Create an objective function for scipy.minimize based on agent evaluations.

Parameters:

multi_objective (bool, optional) – Whether to handle multiple objectives, by default False

Returns:

Objective function that takes a parameter vector and returns a scalar value

Return type:

callable

create_search_space(params)

Create a search space suitable for scipy.optimize.minimize based on parameters.

Parameters:

params (list of Fitparam() objects) – List of parameter objects

Returns:

A tuple containing (x0, bounds) where x0 is the initial parameter vector and bounds is a list of (lower, upper) bound tuples

Return type:

tuple

evaluate(args)

Evaluate the agent on a parameter point.

Parameters:

args (tuple) – Tuple containing the index of the agent, the agent, the index of the parameter point and the parameter point

Returns:

Tuple containing the index of the parameter point and the results of the agent on the parameter point

Return type:

tuple

optimize(multi_objective=False)

Run the optimization process using scipy.minimize.

Parameters:

multi_objective (bool, optional) – Whether to use multi-objective optimization, by default False

Returns:

The optimization results

Return type:

object

reconstruct_params(x)

Reconstruct a parameter dictionary from an optimization vector.

Parameters:

x (array-like) – Parameter vector from the optimizer

Returns:

Dictionary mapping parameter names to values

Return type:

dict

update_params_with_best_balance(return_best_balance=False)

Update the parameters with the best balance of all metrics. The best balance is defined by ranking the results for each metric and taking the parameters that has the lowest sum of ranks.

Parameters:

return_best_balance (bool, optional) – Whether to return the best balance index and parameters, by default False

Returns:

Tuple containing the best balance index and parameters, if return_best_balance is True

Return type:

tuple, optional

Raises:

ValueError – We need at least one metric to update the parameters

Module contents