# -*- coding: utf-8 -*-
# File generated according to Generator/ClassesRef/Optimization/OptiGenAlgNsga2Deap.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/OptiGenAlgNsga2Deap
"""
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 .OptiGenAlg import OptiGenAlg
# Import all class method
# Try/catch to remove unnecessary dependencies in unused method
try:
from ..Methods.Optimization.OptiGenAlgNsga2Deap.solve import solve
except ImportError as error:
solve = error
try:
from ..Methods.Optimization.OptiGenAlgNsga2Deap.mutate import mutate
except ImportError as error:
mutate = error
try:
from ..Methods.Optimization.OptiGenAlgNsga2Deap.cross import cross
except ImportError as error:
cross = error
try:
from ..Methods.Optimization.OptiGenAlgNsga2Deap.create_toolbox import create_toolbox
except ImportError as error:
create_toolbox = error
try:
from ..Methods.Optimization.OptiGenAlgNsga2Deap.check_optimization_input import (
check_optimization_input,
)
except ImportError as error:
check_optimization_input = error
try:
from ..Methods.Optimization.OptiGenAlgNsga2Deap.delete_toolbox import delete_toolbox
except ImportError as error:
delete_toolbox = error
try:
from ..Methods.Optimization.OptiGenAlgNsga2Deap.plot_pareto import plot_pareto
except ImportError as error:
plot_pareto = error
from ntpath import basename
from os.path import isfile
from ._check import CheckTypeError
import numpy as np
import random
from numpy import isnan
from cloudpickle import dumps, loads
from ._check import CheckTypeError
try:
from deap.base import Toolbox
except ImportError:
Toolbox = ImportError
from ._check import InitUnKnowClassError
[docs]class OptiGenAlgNsga2Deap(OptiGenAlg):
"""Multi-objectives optimization problem with some constraints"""
VERSION = 1
# Check ImportError to remove unnecessary dependencies in unused method
# cf Methods.Optimization.OptiGenAlgNsga2Deap.solve
if isinstance(solve, ImportError):
solve = property(
fget=lambda x: raise_(
ImportError("Can't use OptiGenAlgNsga2Deap method solve: " + str(solve))
)
)
else:
solve = solve
# cf Methods.Optimization.OptiGenAlgNsga2Deap.mutate
if isinstance(mutate, ImportError):
mutate = property(
fget=lambda x: raise_(
ImportError(
"Can't use OptiGenAlgNsga2Deap method mutate: " + str(mutate)
)
)
)
else:
mutate = mutate
# cf Methods.Optimization.OptiGenAlgNsga2Deap.cross
if isinstance(cross, ImportError):
cross = property(
fget=lambda x: raise_(
ImportError("Can't use OptiGenAlgNsga2Deap method cross: " + str(cross))
)
)
else:
cross = cross
# cf Methods.Optimization.OptiGenAlgNsga2Deap.create_toolbox
if isinstance(create_toolbox, ImportError):
create_toolbox = property(
fget=lambda x: raise_(
ImportError(
"Can't use OptiGenAlgNsga2Deap method create_toolbox: "
+ str(create_toolbox)
)
)
)
else:
create_toolbox = create_toolbox
# cf Methods.Optimization.OptiGenAlgNsga2Deap.check_optimization_input
if isinstance(check_optimization_input, ImportError):
check_optimization_input = property(
fget=lambda x: raise_(
ImportError(
"Can't use OptiGenAlgNsga2Deap method check_optimization_input: "
+ str(check_optimization_input)
)
)
)
else:
check_optimization_input = check_optimization_input
# cf Methods.Optimization.OptiGenAlgNsga2Deap.delete_toolbox
if isinstance(delete_toolbox, ImportError):
delete_toolbox = property(
fget=lambda x: raise_(
ImportError(
"Can't use OptiGenAlgNsga2Deap method delete_toolbox: "
+ str(delete_toolbox)
)
)
)
else:
delete_toolbox = delete_toolbox
# cf Methods.Optimization.OptiGenAlgNsga2Deap.plot_pareto
if isinstance(plot_pareto, ImportError):
plot_pareto = property(
fget=lambda x: raise_(
ImportError(
"Can't use OptiGenAlgNsga2Deap method plot_pareto: "
+ str(plot_pareto)
)
)
)
else:
plot_pareto = plot_pareto
# 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,
toolbox=None,
selector=None,
crossover=None,
mutator=None,
p_cross=0.9,
p_mutate=0.1,
size_pop=40,
nb_gen=100,
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 "toolbox" in list(init_dict.keys()):
toolbox = init_dict["toolbox"]
if "selector" in list(init_dict.keys()):
selector = init_dict["selector"]
if "crossover" in list(init_dict.keys()):
crossover = init_dict["crossover"]
if "mutator" in list(init_dict.keys()):
mutator = init_dict["mutator"]
if "p_cross" in list(init_dict.keys()):
p_cross = init_dict["p_cross"]
if "p_mutate" in list(init_dict.keys()):
p_mutate = init_dict["p_mutate"]
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 "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.toolbox = toolbox
# Call OptiGenAlg init
super(OptiGenAlgNsga2Deap, self).__init__(
selector=selector,
crossover=crossover,
mutator=mutator,
p_cross=p_cross,
p_mutate=p_mutate,
size_pop=size_pop,
nb_gen=nb_gen,
problem=problem,
xoutput=xoutput,
logger_name=logger_name,
is_keep_all_output=is_keep_all_output,
)
# The class is frozen (in OptiGenAlg init), for now it's impossible to
# add new properties
def __str__(self):
"""Convert this object in a readeable string (for print)"""
OptiGenAlgNsga2Deap_str = ""
# Get the properties inherited from OptiGenAlg
OptiGenAlgNsga2Deap_str += super(OptiGenAlgNsga2Deap, self).__str__()
OptiGenAlgNsga2Deap_str += "toolbox = " + str(self.toolbox) + linesep + linesep
return OptiGenAlgNsga2Deap_str
def __eq__(self, other):
"""Compare two objects (skip parent)"""
if type(other) != type(self):
return False
# Check the properties inherited from OptiGenAlg
if not super(OptiGenAlgNsga2Deap, self).__eq__(other):
return False
if other.toolbox != self.toolbox:
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 OptiGenAlg
diff_list.extend(
super(OptiGenAlgNsga2Deap, self).compare(
other, name=name, ignore_list=ignore_list, is_add_value=is_add_value
)
)
if (other.toolbox is None and self.toolbox is not None) or (
other.toolbox is not None and self.toolbox is None
):
diff_list.append(name + ".toolbox None mismatch")
elif self.toolbox is not None and self.toolbox != other.toolbox:
diff_list.append(name + ".toolbox")
# 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 OptiGenAlg
S += super(OptiGenAlgNsga2Deap, self).__sizeof__()
S += getsizeof(self.toolbox)
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 OptiGenAlg
OptiGenAlgNsga2Deap_dict = super(OptiGenAlgNsga2Deap, self).as_dict(
type_handle_ndarray=type_handle_ndarray,
keep_function=keep_function,
**kwargs
)
if self.toolbox is None:
OptiGenAlgNsga2Deap_dict["toolbox"] = None
else:
# Store serialized data (using cloudpickle) and str
# to read it in json save files
OptiGenAlgNsga2Deap_dict["toolbox"] = {
"__class__": str(type(self._toolbox)),
"__repr__": str(self._toolbox.__repr__()),
"serialized": dumps(self._toolbox).decode("ISO-8859-2"),
}
# The class name is added to the dict for deserialisation purpose
# Overwrite the mother class name
OptiGenAlgNsga2Deap_dict["__class__"] = "OptiGenAlgNsga2Deap"
return OptiGenAlgNsga2Deap_dict
[docs] def copy(self):
"""Creates a deepcopy of the object"""
# Handle deepcopy of all the properties
if self.toolbox is None:
toolbox_val = None
else:
toolbox_val = self.toolbox.copy()
if self._selector_str is not None:
selector_val = self._selector_str
else:
selector_val = self._selector_func
if self._crossover_str is not None:
crossover_val = self._crossover_str
else:
crossover_val = self._crossover_func
if self._mutator_str is not None:
mutator_val = self._mutator_str
else:
mutator_val = self._mutator_func
p_cross_val = self.p_cross
p_mutate_val = self.p_mutate
size_pop_val = self.size_pop
nb_gen_val = self.nb_gen
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)(
toolbox=toolbox_val,
selector=selector_val,
crossover=crossover_val,
mutator=mutator_val,
p_cross=p_cross_val,
p_mutate=p_mutate_val,
size_pop=size_pop_val,
nb_gen=nb_gen_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.toolbox = None
# Set to None the properties inherited from OptiGenAlg
super(OptiGenAlgNsga2Deap, self)._set_None()
def _get_toolbox(self):
"""getter of toolbox"""
return self._toolbox
def _set_toolbox(self, value):
"""setter of toolbox"""
if value == -1:
value = Toolbox()
check_var("toolbox", value, "Toolbox")
self._toolbox = value
toolbox = property(
fget=_get_toolbox,
fset=_set_toolbox,
doc=u"""DEAP toolbox
:Type: deap.base.Toolbox
""",
)
