nextnanoevo.OptimizerClass module

class nextnanoevo.OptimizerClass.Evolution(nextnanoio: IO, metric: Metric, bounds, algorithm='default', size=20)

Bases: object

The evolution class to run the evolutionary optimization with pygmo algorithms.

Parameters:

nextnanoioIO

input-output object defining input file variables and output datafiles

metricMetric

defines how to retrieve numerical (scalar or vector) metric to optimize from output files

boundstuple of list

A tuple containing two lists, the lower and upper bounds for the input variables. Each list should have the same length as the number of input variables.

algorithmstr, optional

The name of the algorithm to be used for the evolution. If “default”, the default algorithm is used. Default is different for single objective and multi-objective problems.

sizeint, optional

The size of the population for the evolution. Default is 20.

Methods

add_initial_guess(initial_guess)	Adds the initial guess solutions to the population.
add_json_summary(gen, seed, algorithm_kwargs)	Adds a summary of the evolution to the JSON logger.
create_dat_file(filename[, output_dir])	Creates a .dat output file from the JSON logger data.
create_population(seed[, initial_guess])	Creates the initial population for the evolution.
load_algorithm(algorithm)	Loads the algorithm for the evolution.
load_default_algorithm()	Loads the default algorithm based on the problem type.
run([gen, keep_pareto_front, interval, ...])	Runs the evolution with the specified parameters. Parameters ---------- gen : int, optional The number of generations to run the evolution. Default is 20. keep_pareto_front : bool, optional If True, the Pareto front is kept and updated during the evolution. Default is False. interval : int, optional The interval at which the Pareto front is updated. Default is 1. seed : int, optional The seed for the random number generator. If not provided, a random seed is used. initial_guess : list of tuples or numpy arrays, optional A list of initial guesses for the population. Each initial guess will be added to the initial population. algorithm_kwargs : dict, optional A dictionary of keyword arguments to be passed to the algorithm. non_execution_penalty : numbers.Number, optional A penalty to be applied for simulations which are failed to execute. keep_output : str, optional Specifies which output to keep after the evolution is finished. Options are "all", "pareto", or "champion". Default is "all".
set_size(size)	Sets the population size for the evolution.
update_champion(solutions, fits, gen)	Updates the champion solution and fit if a better one is found.
update_pareto_front(solutions, fits, gen)	Updates the Pareto front with new solutions and fits.
validate_initial_guess(initial_guess)	Validates the initial guess provided by the user.

add_initial_guess(initial_guess) → None

Adds the initial guess solutions to the population.

add_json_summary(gen, seed, algorithm_kwargs)

Adds a summary of the evolution to the JSON logger.

create_dat_file(filename, output_dir=None)

Creates a .dat output file from the JSON logger data.

create_population(seed, initial_guess=None)

Creates the initial population for the evolution.

load_algorithm(algorithm: str) → None

Loads the algorithm for the evolution. Only accepts the algorithms defined in defaults. If “default” is passed, the default algorithm is loaded based on the problem type (single or multi-objective).

load_default_algorithm() → None

Loads the default algorithm based on the problem type.

run(gen: int = 20, keep_pareto_front: bool = False, interval: int = 1, seed: int | None = None, initial_guess: list[tuple[Number]] | None = None, algorithm_kwargs: dict | None = None, non_execution_penalty: Number | None = None, keep_output: str = 'all') → population

Runs the evolution with the specified parameters. Parameters ———- gen : int, optional

The number of generations to run the evolution. Default is 20.

keep_pareto_frontbool, optional

If True, the Pareto front is kept and updated during the evolution. Default is False.

intervalint, optional

The interval at which the Pareto front is updated. Default is 1.

seedint, optional

The seed for the random number generator. If not provided, a random seed is used.

initial_guesslist of tuples or numpy arrays, optional

A list of initial guesses for the population. Each initial guess will be added to the initial population.

algorithm_kwargsdict, optional

A dictionary of keyword arguments to be passed to the algorithm.

non_execution_penaltynumbers.Number, optional

A penalty to be applied for simulations which are failed to execute.

keep_outputstr, optional

Specifies which output to keep after the evolution is finished. Options are “all”, “pareto”, or “champion”. Default is “all”.

Raises:

ValueError

If keep_output is not one of “all”, “pareto”, or “champion”.

set_size(size: int) → None

Sets the population size for the evolution.

update_champion(solutions, fits, gen) → None

Updates the champion solution and fit if a better one is found. Champion is the solution with the lowest norm of the fit.

update_pareto_front(solutions, fits, gen) → None

Updates the Pareto front with new solutions and fits.

validate_initial_guess(initial_guess) → int

Validates the initial guess provided by the user.

Returns:

int

The number of valid initial guess solutions. Returns 0 if no initial guess is provided or if the initial guess is None.

Raises:

AssertionError

If the initial guess is not iterable, contains non numbers, or if the solutions do not match the bounds. If the length of the initial guess is not between 1 and population size.

class nextnanoevo.OptimizerClass.NextnanoProblem(opt_manager)

Bases: object

The problem class for the nextnano evolutions.

Methods

fitness
get_bounds
get_nobj

fitness(input_variables)

get_bounds()

get_nobj()

class nextnanoevo.OptimizerClass.Optimizer(nextnanoio: IO, metric: Metric, optimization_method='root', method_function=None)

Bases: object

The optimization class to run the gradient decent optimization with methods from scipy.optimize

Parameters:

nextnanoioIO

input-output object defining input file variables and output datafiles

metricMetric

defines how to retrieve numerical (scalar or vector) metric to optimize from output files

optimization_methodstr

optimization method used from scipy.optimize

Methods

load_method([method_function])	Loads the optimization method function from scipy.optimize
run()	Runs optimization
run_optimization()	Runs optimizationans.
set_optimization_parameters(*args[, rewrite])	Updates the optimization parameters method_args and method_kwargs
set_target(target)	Sets the target, only for root finders optimization methods
validate_optimization()	Validates the optimization parameters.

load_method(method_function=None) → Callable

Loads the optimization method function from scipy.optimize

run()

Runs optimization

run_optimization()

Runs optimizationans.

Returns:

resultscipy.optimize.OptimizeResult

set_optimization_parameters(*args, rewrite=False, **kwargs) → None

Updates the optimization parameters method_args and method_kwargs

Parameters:

argslist

The list of arguments to be passed to the optimization function

rewritebool

If True, the method_args and method_kwargs are rewritten with the new values

kwargsdict

The dictionary of keyword arguments to be passed to the optimization function

set_target(target) → None

Sets the target, only for root finders optimization methods

validate_optimization() → bool

Validates the optimization parameters. This function should do general checks, as well as function specific checks for known scipy functions.

Raises:

AssertionError

If the optimization parameters are not valid.