Source code for iwopy.interfaces.pygmo.optimizer

import numpy as np

from iwopy.core import Optimizer
from iwopy.utils import suppress_stdout
from .problem import UDP
from .algos import AlgoFactory
from . import imports


[docs] class Optimizer_pygmo(Optimizer): """ Interface to the pygmo optimizers for serial runs. Attributes ---------- problem_pars: dict Parameters for the problem algo_pars: dict Parameters for the alorithm setup_pars: dict Parameters for the calculation setup udp: iwopy.interfaces.imports.pygmo.UDA The pygmo problem algo: imports.pygmo.algo The pygmo algorithm :group: interfaces.pygmo """
[docs] def __init__(self, problem, problem_pars={}, algo_pars={}, setup_pars={}): """ Constructor Parameters ---------- problem: iwopy.Problem The problem to optimize problem_pars: dict Parameters for the problem algo_pars: dict Parameters for the alorithm setup_pars: dict Parameters for the calculation setup """ super().__init__(problem) imports.load() self.problem_pars = problem_pars self.algo_pars = algo_pars self.setup_pars = setup_pars self.udp = None self.algo = None
[docs] def initialize(self, verbosity=1): """ Initialize the object. Parameters ---------- verbosity: int The verbosity level, 0 = silent """ # create pygmo problem: pop = self.problem_pars.get("pop", False) self.udp = UDP(self.problem, **self.problem_pars) # create algorithm: self.algo = AlgoFactory.new(pop=pop, **self.algo_pars) # create population: psize = self.setup_pars.get("pop_size", 1) pseed = self.setup_pars.get("seed", None) pnrfi = self.setup_pars.get("norandom_first", psize == 1) self.pop = imports.pygmo.population(self.udp, size=psize, seed=pseed) self.pop.problem.c_tol = [ self.setup_pars.get("c_tol", 1e-4) ] * self.pop.problem.get_nc() # memorize verbosity level: self.verbosity = self.setup_pars.get("verbosity", 1) # set first indiviual to initial values: if pnrfi: x = np.zeros(self.udp.n_vars_all) if self.problem.n_vars_float: x[: self.problem.n_vars_float] = self.problem.initial_values_float() if self.problem.n_vars_int: x[self.problem.n_vars_float :] = self.problem.initial_values_int() # xf = x[: self.problem.n_vars_float] # xi = x[self.problem.n_vars_float :].astype(np.int64) self.udp._active = True self.pop.set_x(0, x) super().initialize(verbosity)
[docs] def print_info(self): """ Print solver info, called before solving """ super().print_info() if self.algo is not None: print() print(self.algo)
[docs] def solve(self, verbosity=1): """ Run the optimization solver. Parameters ---------- verbosity: int The verbosity level, 0 = silent Returns ------- results: iwopy.SingleObjOptResults The optimization results object """ # try pygmo silencing: if self.algo.has_set_verbosity(): self.algo.set_verbosity(verbosity) # general silencing for Python prints: silent = verbosity <= 0 with suppress_stdout(silent): # Run solver: pop = self.algo.evolve(self.pop) return self.udp.finalize(pop, verbosity)