from iwopy.core.problem import Problem
[docs]class ProblemWrapper(Problem):
"""
Generic abstract problem wrapper class.
Attributes
----------
base_problem: iwopy.Problem
The underlying concrete problem
:group: wrappers
"""
[docs] def __init__(self, base_problem, name, **kwargs):
"""
Constructor
Parameters
----------
base_problem: iwopy.Problem
The underlying concrete problem
name: str
The problem name
kwargs: dict, optional
Additional parameters for the Problem class
"""
super().__init__(name, **kwargs)
self.base_problem = base_problem
def __getattr__(self, name):
return super().__getattribute__("base_problem").__getattribute__(name)
[docs] def var_names_int(self):
"""
The names of integer variables.
Returns
-------
names: list of str
The names of the integer variables
"""
return self.base_problem.var_names_int()
[docs] def initial_values_int(self):
"""
The initial values of the integer variables.
Returns
-------
values: numpy.ndarray
Initial int values, shape: (n_vars_int,)
"""
return self.base_problem.initial_values_int()
[docs] def min_values_int(self):
"""
The minimal values of the integer variables.
Use -self.INT_INF for unbounded.
Returns
-------
values: numpy.ndarray
Minimal int values, shape: (n_vars_int,)
"""
return self.base_problem.min_values_int()
[docs] def max_values_int(self):
"""
The maximal values of the integer variables.
Use self.INT_INF for unbounded.
Returns
-------
values: numpy.ndarray
Maximal int values, shape: (n_vars_int,)
"""
return self.base_problem.max_values_int()
[docs] def var_names_float(self):
"""
The names of float variables.
Returns
-------
names: list of str
The names of the float variables
"""
return self.base_problem.var_names_float()
[docs] def initial_values_float(self):
"""
The initial values of the float variables.
Returns
-------
values: numpy.ndarray
Initial float values, shape: (n_vars_float,)
"""
return self.base_problem.initial_values_float()
[docs] def min_values_float(self):
"""
The minimal values of the float variables.
Use -numpy.inf for unbounded.
Returns
-------
values: numpy.ndarray
Minimal float values, shape: (n_vars_float,)
"""
return self.base_problem.min_values_float()
[docs] def max_values_float(self):
"""
The maximal values of the float variables.
Use numpy.inf for unbounded.
Returns
-------
values: numpy.ndarray
Maximal float values, shape: (n_vars_float,)
"""
return self.base_problem.max_values_float()
[docs] def initialize(self, verbosity=0):
"""
Initialize the problem.
Parameters
----------
verbosity: int
The verbosity level, 0 = silent
"""
if not self.base_problem.initialized:
self.base_problem.initialize(verbosity)
self.objs = self.base_problem.objs
self.cons = self.base_problem.cons
for f in self.objs.functions:
f.problem = self
self.objs.problem = self
for f in self.cons.functions:
f.problem = self
self.cons.problem = self
super().initialize(verbosity)
[docs] def apply_individual(self, vars_int, vars_float):
"""
Apply new variables to the problem.
Parameters
----------
vars_int: np.array
The integer variable values, shape: (n_vars_int,)
vars_float: np.array
The float variable values, shape: (n_vars_float,)
Returns
-------
problem_results: Any
The results of the variable application
to the problem
"""
return self.base_problem.apply_individual(vars_int, vars_float)
[docs] def apply_population(self, vars_int, vars_float):
"""
Apply new variables to the problem,
for a whole population.
Parameters
----------
vars_int: np.array
The integer variable values, shape: (n_pop, n_vars_int)
vars_float: np.array
The float variable values, shape: (n_pop, n_vars_float)
Returns
-------
problem_results: Any
The results of the variable application
to the problem
"""
return self.base_problem.apply_population(vars_int, vars_float)
[docs] def finalize_individual(self, vars_int, vars_float, verbosity=1):
"""
Finalization, given the champion data.
Parameters
----------
vars_int: np.array
The optimal integer variable values, shape: (n_vars_int,)
vars_float: np.array
The optimal float variable values, shape: (n_vars_float,)
verbosity: int
The verbosity level, 0 = silent
Returns
-------
problem_results: Any
The results of the variable application
to the problem
objs: np.array
The objective function values, shape: (n_objectives,)
cons: np.array
The constraints values, shape: (n_constraints,)
"""
return self.base_problem.finalize_individual(vars_int, vars_float, verbosity)
[docs] def finalize_population(self, vars_int, vars_float, verbosity=0):
"""
Finalization, given the final population data.
Parameters
----------
vars_int: np.array
The integer variable values of the final
generation, shape: (n_pop, n_vars_int)
vars_float: np.array
The float variable values of the final
generation, shape: (n_pop, n_vars_float)
verbosity: int
The verbosity level, 0 = silent
Returns
-------
problem_results: Any
The results of the variable application
to the problem
objs: np.array
The final objective function values, shape: (n_pop, n_components)
cons: np.array
The final constraint values, shape: (n_pop, n_constraints)
"""
return self.base_problem.finalize_population(vars_int, vars_float, verbosity)