# SPDX-License-Identifier: MPL-2.0
# Copyright (C) 2020- The University of Tokyo
#
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at https://mozilla.org/MPL/2.0/.
from __future__ import annotations
from abc import ABC, abstractmethod
import copy
import numpy as np
[docs]
class TestFunction(ABC):
"""Abstract class for test functions.
Test functions are used to evaluate the performance of the optimization algorithms.
Note
=====
The test function f should be defined as a minimization problem.
"""
def __init__(
self,
nobj: int,
dim: int,
min_X: np.ndarray | list[float] | float,
max_X: np.ndarray | list[float] | float,
test_maximizer: bool = True,
):
"""Initialize the test function.
Arguments
=========
nobj: int
Number of objectives.
dim: int
Number of dimensions.
min_X: np.ndarray | list[float] | float
Minimum value of search space for each dimension.
max_X: np.ndarray | list[float] | float
Maximum value of search space for each dimension.
test_maximizer: bool, default=True
If True, the test function is negated for testing a maximization problem solver.
"""
self._nobj = nobj
self._dim = dim
self._test_maximizer = test_maximizer
if isinstance(min_X, float):
self._min_X = np.full(dim, min_X)
elif isinstance(min_X, list):
self._min_X = np.array(min_X)
else:
self._min_X = copy.deepcopy(min_X)
if isinstance(max_X, float):
self._max_X = np.full(dim, max_X)
elif isinstance(max_X, list):
self._max_X = np.array(max_X)
else:
self._max_X = copy.deepcopy(max_X)
if self._min_X.shape[0] != self._dim:
raise ValueError(
f"ERROR: dimension mismatch: self._min_X.shape[0] = {self._min_X.shape[0]}, self._dim = {self._dim}"
)
if self._max_X.shape[0] != self._dim:
raise ValueError(
f"ERROR: dimension mismatch: self._max_X.shape[0] = {self._max_X.shape[0]}, self._dim = {self._dim}"
)
def __call__(self, x: np.ndarray) -> np.ndarray:
"""Evaluate the test function at the given point.
Arguments
=========
x: np.ndarray
The point at which to evaluate the test function.
x is a numpy array of shape (n, d), where n is the number of points and d is the dimension of the input space.
Returns
=======
f: np.ndarray
The value of the test function at the given point.
The output value is a numpy array of shape (n, k), where k is the number of objectives.
"""
if x.shape[1] != self._dim:
raise ValueError(
f"ERROR: dimension mismatch: x.shape[1] = {x.shape[1]}, self._dim = {self._dim}"
)
f = self.f(x)
# This is assertion because it is the Developer's responsibility to ensure that the number of objectives is correct
assert f.shape[1] == self._nobj
if self._test_maximizer:
return -f
else:
return f
@property
def dim(self) -> int:
"""Get the number of dimensions of the test function.
Returns
=======
int
The number of dimensions of the test function d.
"""
return self._dim
@property
def nobj(self) -> int:
"""Get the number of objectives of the test function.
Returns
=======
int
The number of objectives of the test function k.
"""
return self._nobj
@property
def min_X(self) -> np.ndarray:
"""Get the minimum values of the search space of the test function.
Returns
=======
np.ndarray
The minimum value of the test function for each dimension.
"""
return copy.deepcopy(self._min_X)
@property
def max_X(self) -> np.ndarray:
"""Get the maximum values of the search space of the test function.
Returns
=======
np.ndarray
The maximum value of the test function for each dimension.
"""
return copy.deepcopy(self._max_X)
[docs]
@abstractmethod
def f(self, x: np.ndarray) -> np.ndarray:
"""Evaluate the test function at the given point.
Arguments
=========
x: np.ndarray
The point at which to evaluate the test function.
x is a numpy array of shape (n, d), where n is the number of points and d is the dimension of the input space.
Returns
=======
f: np.ndarray
The value of the test function at the given point.
The output value is a numpy array of shape (n, k), where k is the number of objectives.
"""
...
[docs]
def constraint(self, x: np.ndarray) -> np.ndarray:
"""Evaluate the constraint function at the given point.
Arguments
=========
x: np.ndarray
The point at which to evaluate the constraint function.
x is a numpy array of shape (n, d), where n is the number of points and d is the dimension of the input space.
Returns
=======
np.ndarray
The boolean values indicating whether the point is valid or not.
The output value is a numpy array of shape (n,), where n is the number of points.
"""
# default implementation is that all points are valid
return np.ones(x.shape[0], dtype=bool)