Source code for pyleecan.Methods.Optimization.OptiGenAlgNsga2Deap.check_optimization_input

from logging import Logger, FileHandler, Formatter, INFO, NOTSET
from datetime import datetime
from ....Classes.OptiObjective import OptiObjective


[docs]class OptimizationAttributeError(Exception): """Class to Raise an error""" def __init__(self, message): self.message = message
[docs]def check_optimization_input(self): """Check optimization parameters before solving the problem Parameters ---------- solver : Solver solver to perform the genetic algorithm with DEAP """ logger = self.get_logger() # Check problem existence if self.problem == None: raise OptimizationAttributeError( "The problem has not been defined, please add a problem to OptiGenAlgNsga2Deap." ) # Check population size if self.size_pop % 4 > 0: mess = "Change population size from {} to {} to fit with the tournament selection".format( self.size_pop, self.size_pop + (self.size_pop % 4) ) self.size_pop += self.size_pop % 4 logger.warning(mess) # Check the problem contains at least one design variable if self.problem.obj_func == None or ( isinstance(self.problem.obj_func, dict) and len(self.problem.obj_func) == 0 ): raise OptimizationAttributeError( "Optimization problem must contain at least one objective function" ) else: for obj_func in self.problem.obj_func: if not isinstance(obj_func, OptiObjective): raise TypeError( "Wrong obj_func type: OptiObjective expected, got {}".format( type(obj_func).__name__ ) ) elif not callable(obj_func.keeper): mess = "The objective function '{}' is not callable, please define the attribute 'keeper'.".format( obj_func.name ) raise OptimizationAttributeError(mess) # Check if objectives and other datakeepers have different symbol if isinstance(self.problem.datakeeper_list, list): symbol_list = [of.symbol for of in self.problem.obj_func] + [ dk.symbol for dk in self.problem.datakeeper_list ] else: symbol_list = [of.symbol for of in self.problem.obj_func] if len(symbol_list) != len(set(symbol_list)): mess = "Every objective function and datakeeper must have a unique symbol." raise OptimizationAttributeError(mess) # Check the problem contains at least one objective function if self.problem.design_var == None or ( isinstance(self.problem.design_var, list) and len(self.problem.design_var) == 0 ): raise OptimizationAttributeError( "Optimization problem must contain at least one design variable" ) else: for design_var in self.problem.design_var: if design_var.type_var not in ["set", "interval"]: mess = 'The design variable \'{}\' has a wrong type_var got {} expected "set" or "interval".'.format( design_var.name, design_var.type_var ) raise OptimizationAttributeError(mess) elif design_var.symbol in [None, ""]: mess = "The design variable '{}' has no symbol.".format(design_var.name) raise OptimizationAttributeError(mess) elif not callable(design_var.get_value): mess = "OptiDesignVar '{}' get_value is not callable.".format( design_var.name ) raise OptimizationAttributeError(mess) elif not callable(design_var.setter): mess = "OptiDesignVar '{}' setter is not callable.".format( design_var.name ) raise OptimizationAttributeError(mess) # Check constraints type if self.problem.constraint != None: for cstr in self.problem.constraint: # Check type of constraint if cstr.type_const not in ["<=", "<", "==", "=", ">=", ">"]: mess = "The constraint '{}' has a wrong type: expected one of {} received '{}'.".format( cstr.name, ["<=", "<", "==", "=", ">=", ">"], cstr.type_const ) raise OptimizationAttributeError(mess) # Check getter elif not callable(cstr.get_variable): mess = ( "The constraint '{}' function get_variable is not callable.".format( cstr.name ) ) raise OptimizationAttributeError(mess)