# -*- coding: utf-8 -*-
# File generated according to Generator/ClassesRef/Optimization/OptiBayesAlg.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/OptiBayesAlg
"""
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 .OptiSolver import OptiSolver
from numpy import isnan
from ._check import InitUnKnowClassError
[docs]class OptiBayesAlg(OptiSolver):
"""Bayesian algorithm class"""
VERSION = 1
# 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,
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 "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.nb_iter = nb_iter
self.nb_start = nb_start
self.criterion = criterion
self.kernel = kernel
# Call OptiSolver init
super(OptiBayesAlg, self).__init__(
problem=problem,
xoutput=xoutput,
logger_name=logger_name,
is_keep_all_output=is_keep_all_output,
)
# The class is frozen (in OptiSolver init), for now it's impossible to
# add new properties
def __str__(self):
"""Convert this object in a readeable string (for print)"""
OptiBayesAlg_str = ""
# Get the properties inherited from OptiSolver
OptiBayesAlg_str += super(OptiBayesAlg, self).__str__()
OptiBayesAlg_str += "nb_iter = " + str(self.nb_iter) + linesep
OptiBayesAlg_str += "nb_start = " + str(self.nb_start) + linesep
OptiBayesAlg_str += 'criterion = "' + str(self.criterion) + '"' + linesep
OptiBayesAlg_str += "kernel = " + str(self.kernel) + linesep
return OptiBayesAlg_str
def __eq__(self, other):
"""Compare two objects (skip parent)"""
if type(other) != type(self):
return False
# Check the properties inherited from OptiSolver
if not super(OptiBayesAlg, self).__eq__(other):
return False
if other.nb_iter != self.nb_iter:
return False
if other.nb_start != self.nb_start:
return False
if other.criterion != self.criterion:
return False
if other.kernel != self.kernel:
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 OptiSolver
diff_list.extend(
super(OptiBayesAlg, self).compare(
other, name=name, ignore_list=ignore_list, is_add_value=is_add_value
)
)
if other._nb_iter != self._nb_iter:
if is_add_value:
val_str = (
" (self="
+ str(self._nb_iter)
+ ", other="
+ str(other._nb_iter)
+ ")"
)
diff_list.append(name + ".nb_iter" + val_str)
else:
diff_list.append(name + ".nb_iter")
if other._nb_start != self._nb_start:
if is_add_value:
val_str = (
" (self="
+ str(self._nb_start)
+ ", other="
+ str(other._nb_start)
+ ")"
)
diff_list.append(name + ".nb_start" + val_str)
else:
diff_list.append(name + ".nb_start")
if other._criterion != self._criterion:
if is_add_value:
val_str = (
" (self="
+ str(self._criterion)
+ ", other="
+ str(other._criterion)
+ ")"
)
diff_list.append(name + ".criterion" + val_str)
else:
diff_list.append(name + ".criterion")
if other._kernel != self._kernel:
if is_add_value:
val_str = (
" (self="
+ str(self._kernel)
+ ", other="
+ str(other._kernel)
+ ")"
)
diff_list.append(name + ".kernel" + val_str)
else:
diff_list.append(name + ".kernel")
# 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 OptiSolver
S += super(OptiBayesAlg, self).__sizeof__()
S += getsizeof(self.nb_iter)
S += getsizeof(self.nb_start)
S += getsizeof(self.criterion)
S += getsizeof(self.kernel)
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 OptiSolver
OptiBayesAlg_dict = super(OptiBayesAlg, self).as_dict(
type_handle_ndarray=type_handle_ndarray,
keep_function=keep_function,
**kwargs
)
OptiBayesAlg_dict["nb_iter"] = self.nb_iter
OptiBayesAlg_dict["nb_start"] = self.nb_start
OptiBayesAlg_dict["criterion"] = self.criterion
OptiBayesAlg_dict["kernel"] = self.kernel
# The class name is added to the dict for deserialisation purpose
# Overwrite the mother class name
OptiBayesAlg_dict["__class__"] = "OptiBayesAlg"
return OptiBayesAlg_dict
[docs] def copy(self):
"""Creates a deepcopy of the object"""
# Handle deepcopy of all the properties
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)(
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.nb_iter = None
self.nb_start = None
self.criterion = None
self.kernel = None
# Set to None the properties inherited from OptiSolver
super(OptiBayesAlg, self)._set_None()
def _get_nb_iter(self):
"""getter of nb_iter"""
return self._nb_iter
def _set_nb_iter(self, value):
"""setter of nb_iter"""
check_var("nb_iter", value, "int", Vmin=1)
self._nb_iter = value
nb_iter = property(
fget=_get_nb_iter,
fset=_set_nb_iter,
doc=u"""Number of iterations
:Type: int
:min: 1
""",
)
def _get_nb_start(self):
"""getter of nb_start"""
return self._nb_start
def _set_nb_start(self, value):
"""setter of nb_start"""
check_var("nb_start", value, "int", Vmin=1)
self._nb_start = value
nb_start = property(
fget=_get_nb_start,
fset=_set_nb_start,
doc=u"""Number of starting points
:Type: int
:min: 1
""",
)
def _get_criterion(self):
"""getter of criterion"""
return self._criterion
def _set_criterion(self, value):
"""setter of criterion"""
check_var("criterion", value, "str")
self._criterion = value
criterion = property(
fget=_get_criterion,
fset=_set_criterion,
doc=u"""Point selection criteria
:Type: str
""",
)
def _get_kernel(self):
"""getter of kernel"""
return self._kernel
def _set_kernel(self, value):
"""setter of kernel"""
check_var("kernel", value, "int", Vmin=0, Vmax=0)
self._kernel = value
kernel = property(
fget=_get_kernel,
fset=_set_kernel,
doc=u"""Type of kernel
:Type: int
:min: 0
:max: 0
""",
)
