# -*- coding: utf-8 -*-
# File generated according to Generator/ClassesRef/Optimization/OptiBayesAlgSmoot.csv
# WARNING! All changes made in this file will be lost!
"""Method code available at https://github.com/Eomys/pyleecan/tree/master/pyleecan/Methods/Optimization/OptiBayesAlgSmoot
"""
from os import linesep
from sys import getsizeof
from logging import getLogger
from ._check import check_var, raise_
from ..Functions.get_logger import get_logger
from ..Functions.save import save
from ..Functions.load import load_init_dict
from ..Functions.Load.import_class import import_class
from copy import deepcopy
from .OptiBayesAlg import OptiBayesAlg
# Import all class method
# Try/catch to remove unnecessary dependencies in unused method
try:
from ..Methods.Optimization.OptiBayesAlgSmoot.solve import solve
except ImportError as error:
solve = error
try:
from ..Methods.Optimization.OptiBayesAlgSmoot.check_optimization_input import (
check_optimization_input,
)
except ImportError as error:
check_optimization_input = error
try:
from ..Methods.Optimization.OptiBayesAlgSmoot.evaluate import evaluate
except ImportError as error:
evaluate = error
try:
from ..Methods.Optimization.OptiBayesAlgSmoot.plot_pareto import plot_pareto
except ImportError as error:
plot_pareto = error
try:
from ..Methods.Optimization.OptiBayesAlgSmoot.eval_const import eval_const
except ImportError as error:
eval_const = error
from numpy import isnan
from ._check import InitUnKnowClassError
[docs]class OptiBayesAlgSmoot(OptiBayesAlg):
"""Multi-objectives optimization problem with some constraints"""
VERSION = 1
# Check ImportError to remove unnecessary dependencies in unused method
# cf Methods.Optimization.OptiBayesAlgSmoot.solve
if isinstance(solve, ImportError):
solve = property(
fget=lambda x: raise_(
ImportError("Can't use OptiBayesAlgSmoot method solve: " + str(solve))
)
)
else:
solve = solve
# cf Methods.Optimization.OptiBayesAlgSmoot.check_optimization_input
if isinstance(check_optimization_input, ImportError):
check_optimization_input = property(
fget=lambda x: raise_(
ImportError(
"Can't use OptiBayesAlgSmoot method check_optimization_input: "
+ str(check_optimization_input)
)
)
)
else:
check_optimization_input = check_optimization_input
# cf Methods.Optimization.OptiBayesAlgSmoot.evaluate
if isinstance(evaluate, ImportError):
evaluate = property(
fget=lambda x: raise_(
ImportError(
"Can't use OptiBayesAlgSmoot method evaluate: " + str(evaluate)
)
)
)
else:
evaluate = evaluate
# cf Methods.Optimization.OptiBayesAlgSmoot.plot_pareto
if isinstance(plot_pareto, ImportError):
plot_pareto = property(
fget=lambda x: raise_(
ImportError(
"Can't use OptiBayesAlgSmoot method plot_pareto: "
+ str(plot_pareto)
)
)
)
else:
plot_pareto = plot_pareto
# cf Methods.Optimization.OptiBayesAlgSmoot.eval_const
if isinstance(eval_const, ImportError):
eval_const = property(
fget=lambda x: raise_(
ImportError(
"Can't use OptiBayesAlgSmoot method eval_const: " + str(eval_const)
)
)
)
else:
eval_const = eval_const
# generic save method is available in all object
save = save
# get_logger method is available in all object
get_logger = get_logger
def __init__(
self,
size_pop=40,
nb_gen=100,
nb_iter=15,
nb_start=300,
criterion="PI",
kernel=0,
problem=-1,
xoutput=-1,
logger_name="Pyleecan.OptiSolver",
is_keep_all_output=False,
init_dict=None,
init_str=None,
):
"""Constructor of the class. Can be use in three ways :
- __init__ (arg1 = 1, arg3 = 5) every parameters have name and default values
for pyleecan type, -1 will call the default constructor
- __init__ (init_dict = d) d must be a dictionary with property names as keys
- __init__ (init_str = s) s must be a string
s is the file path to load
ndarray or list can be given for Vector and Matrix
object or dict can be given for pyleecan Object"""
if init_str is not None: # Load from a file
init_dict = load_init_dict(init_str)[1]
if init_dict is not None: # Initialisation by dict
assert type(init_dict) is dict
# Overwrite default value with init_dict content
if "size_pop" in list(init_dict.keys()):
size_pop = init_dict["size_pop"]
if "nb_gen" in list(init_dict.keys()):
nb_gen = init_dict["nb_gen"]
if "nb_iter" in list(init_dict.keys()):
nb_iter = init_dict["nb_iter"]
if "nb_start" in list(init_dict.keys()):
nb_start = init_dict["nb_start"]
if "criterion" in list(init_dict.keys()):
criterion = init_dict["criterion"]
if "kernel" in list(init_dict.keys()):
kernel = init_dict["kernel"]
if "problem" in list(init_dict.keys()):
problem = init_dict["problem"]
if "xoutput" in list(init_dict.keys()):
xoutput = init_dict["xoutput"]
if "logger_name" in list(init_dict.keys()):
logger_name = init_dict["logger_name"]
if "is_keep_all_output" in list(init_dict.keys()):
is_keep_all_output = init_dict["is_keep_all_output"]
# Set the properties (value check and convertion are done in setter)
self.size_pop = size_pop
self.nb_gen = nb_gen
# Call OptiBayesAlg init
super(OptiBayesAlgSmoot, self).__init__(
nb_iter=nb_iter,
nb_start=nb_start,
criterion=criterion,
kernel=kernel,
problem=problem,
xoutput=xoutput,
logger_name=logger_name,
is_keep_all_output=is_keep_all_output,
)
# The class is frozen (in OptiBayesAlg init), for now it's impossible to
# add new properties
def __str__(self):
"""Convert this object in a readeable string (for print)"""
OptiBayesAlgSmoot_str = ""
# Get the properties inherited from OptiBayesAlg
OptiBayesAlgSmoot_str += super(OptiBayesAlgSmoot, self).__str__()
OptiBayesAlgSmoot_str += "size_pop = " + str(self.size_pop) + linesep
OptiBayesAlgSmoot_str += "nb_gen = " + str(self.nb_gen) + linesep
return OptiBayesAlgSmoot_str
def __eq__(self, other):
"""Compare two objects (skip parent)"""
if type(other) != type(self):
return False
# Check the properties inherited from OptiBayesAlg
if not super(OptiBayesAlgSmoot, self).__eq__(other):
return False
if other.size_pop != self.size_pop:
return False
if other.nb_gen != self.nb_gen:
return False
return True
[docs] def compare(self, other, name="self", ignore_list=None, is_add_value=False):
"""Compare two objects and return list of differences"""
if ignore_list is None:
ignore_list = list()
if type(other) != type(self):
return ["type(" + name + ")"]
diff_list = list()
# Check the properties inherited from OptiBayesAlg
diff_list.extend(
super(OptiBayesAlgSmoot, self).compare(
other, name=name, ignore_list=ignore_list, is_add_value=is_add_value
)
)
if other._size_pop != self._size_pop:
if is_add_value:
val_str = (
" (self="
+ str(self._size_pop)
+ ", other="
+ str(other._size_pop)
+ ")"
)
diff_list.append(name + ".size_pop" + val_str)
else:
diff_list.append(name + ".size_pop")
if other._nb_gen != self._nb_gen:
if is_add_value:
val_str = (
" (self="
+ str(self._nb_gen)
+ ", other="
+ str(other._nb_gen)
+ ")"
)
diff_list.append(name + ".nb_gen" + val_str)
else:
diff_list.append(name + ".nb_gen")
# Filter ignore differences
diff_list = list(filter(lambda x: x not in ignore_list, diff_list))
return diff_list
def __sizeof__(self):
"""Return the size in memory of the object (including all subobject)"""
S = 0 # Full size of the object
# Get size of the properties inherited from OptiBayesAlg
S += super(OptiBayesAlgSmoot, self).__sizeof__()
S += getsizeof(self.size_pop)
S += getsizeof(self.nb_gen)
return S
[docs] def as_dict(self, type_handle_ndarray=0, keep_function=False, **kwargs):
"""
Convert this object in a json serializable dict (can be use in __init__).
type_handle_ndarray: int
How to handle ndarray (0: tolist, 1: copy, 2: nothing)
keep_function : bool
True to keep the function object, else return str
Optional keyword input parameter is for internal use only
and may prevent json serializability.
"""
# Get the properties inherited from OptiBayesAlg
OptiBayesAlgSmoot_dict = super(OptiBayesAlgSmoot, self).as_dict(
type_handle_ndarray=type_handle_ndarray,
keep_function=keep_function,
**kwargs
)
OptiBayesAlgSmoot_dict["size_pop"] = self.size_pop
OptiBayesAlgSmoot_dict["nb_gen"] = self.nb_gen
# The class name is added to the dict for deserialisation purpose
# Overwrite the mother class name
OptiBayesAlgSmoot_dict["__class__"] = "OptiBayesAlgSmoot"
return OptiBayesAlgSmoot_dict
[docs] def copy(self):
"""Creates a deepcopy of the object"""
# Handle deepcopy of all the properties
size_pop_val = self.size_pop
nb_gen_val = self.nb_gen
nb_iter_val = self.nb_iter
nb_start_val = self.nb_start
criterion_val = self.criterion
kernel_val = self.kernel
if self.problem is None:
problem_val = None
else:
problem_val = self.problem.copy()
if self.xoutput is None:
xoutput_val = None
else:
xoutput_val = self.xoutput.copy()
logger_name_val = self.logger_name
is_keep_all_output_val = self.is_keep_all_output
# Creates new object of the same type with the copied properties
obj_copy = type(self)(
size_pop=size_pop_val,
nb_gen=nb_gen_val,
nb_iter=nb_iter_val,
nb_start=nb_start_val,
criterion=criterion_val,
kernel=kernel_val,
problem=problem_val,
xoutput=xoutput_val,
logger_name=logger_name_val,
is_keep_all_output=is_keep_all_output_val,
)
return obj_copy
def _set_None(self):
"""Set all the properties to None (except pyleecan object)"""
self.size_pop = None
self.nb_gen = None
# Set to None the properties inherited from OptiBayesAlg
super(OptiBayesAlgSmoot, self)._set_None()
def _get_size_pop(self):
"""getter of size_pop"""
return self._size_pop
def _set_size_pop(self, value):
"""setter of size_pop"""
check_var("size_pop", value, "int", Vmin=1)
self._size_pop = value
size_pop = property(
fget=_get_size_pop,
fset=_set_size_pop,
doc=u"""Number of individuals for each generation
:Type: int
:min: 1
""",
)
def _get_nb_gen(self):
"""getter of nb_gen"""
return self._nb_gen
def _set_nb_gen(self, value):
"""setter of nb_gen"""
check_var("nb_gen", value, "int", Vmin=1)
self._nb_gen = value
nb_gen = property(
fget=_get_nb_gen,
fset=_set_nb_gen,
doc=u"""Number of generations for the genetic part
:Type: int
:min: 1
""",
)
