# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
from typing import List, Optional, Tuple
import torch
from botorch.test_functions.synthetic import SyntheticTestFunction
from torch import Tensor
[docs]class Ishigami(SyntheticTestFunction):
r"""Ishigami test function.
three-dimensional function (usually evaluated on `[-pi, pi]^3`):
f(x) = sin(x_1) + a sin(x_2)^2 + b x_3^4 sin(x_1)
Here `a` and `b` are constants where a=7 and b=0.1 or b=0.05
Proposed to test sensitivity analysis methods because it exhibits strong
nonlinearity and nonmonotonicity and a peculiar dependence on x_3.
"""
def __init__(
self, b: float = 0.1, noise_std: Optional[float] = None, negate: bool = False
) -> None:
r"""
Args:
b: the b constant, should be 0.1 or 0.05.
noise_std: Standard deviation of the observation noise.
negative: If True, negative the objective.
"""
self._optimizers = None
if b not in (0.1, 0.05):
raise ValueError("b parameter should be 0.1 or 0.05")
self.dim = 3
if b == 0.1:
self.si = [0.3138, 0.4424, 0]
self.si_t = [0.558, 0.442, 0.244]
self.s_ij = [0, 0.244, 0]
self.dgsm_gradient = [-0.0004, -0.0004, -0.0004]
self.dgsm_gradient_abs = [1.9, 4.45, 1.97]
self.dgsm_gradient_square = [7.7, 24.5, 11]
elif b == 0.05:
self.si = [0.218, 0.687, 0]
self.si_t = [0.3131, 0.6868, 0.095]
self.s_ij = [0, 0.094, 0]
self.dgsm_gradient = [-0.0002, -0.0002, -0.0002]
self.dgsm_gradient_abs = [1.26, 4.45, 1.97]
self.dgsm_gradient_square = [2.8, 24.5, 11]
self._bounds = [(-math.pi, math.pi) for _ in range(self.dim)]
self.b = b
super().__init__(noise_std=noise_std, negate=negate)
@property
def _optimal_value(self) -> float:
raise NotImplementedError
[docs] def compute_dgsm(self, X: Tensor) -> Tuple[List[float], List[float], List[float]]:
r"""Compute derivative global sensitivity measures.
This function can be called separately to estimate the dgsm measure
The exact global integrals of these values are already added under
as attributes dgsm_gradient, dgsm_gradient_bas, and dgsm_gradient_square.
Args:
X: Set of points at which to compute derivative measures.
Returns: The average gradient, absolute gradient, and square gradients.
"""
dx_1 = torch.cos(X[..., 0]) * (1 + self.b * (X[..., 2] ** 4))
dx_2 = 14 * torch.cos(X[..., 1]) * torch.sin(X[..., 1])
dx_3 = 0.4 * (X[..., 2] ** 3) * torch.sin(X[..., 0])
gradient_measure = [
torch.mean(dx_1).item(),
torch.mean(dx_1).item(),
torch.mean(dx_1).item(),
]
gradient_absolute_measure = [
torch.mean(torch.abs(dx_1)).item(),
torch.mean(torch.abs(dx_2)).item(),
torch.mean(torch.abs(dx_3)).item(),
]
gradient_square_measure = [
torch.mean(torch.pow(dx_1, 2)).item(),
torch.mean(torch.pow(dx_2, 2)).item(),
torch.mean(torch.pow(dx_3, 2)).item(),
]
return gradient_measure, gradient_absolute_measure, gradient_square_measure
[docs] def evaluate_true(self, X: Tensor) -> Tensor:
self.to(device=X.device, dtype=X.dtype)
t = (
torch.sin(X[..., 0])
+ 7 * (torch.sin(X[..., 1]) ** 2)
+ self.b * (X[..., 2] ** 4) * torch.sin(X[..., 0])
)
return t
[docs]class Gsobol(SyntheticTestFunction):
r"""Gsobol test function.
d-dimensional function (usually evaluated on `[0, 1]^d`):
f(x) = Prod_{i=1}\^{d} ((\|4x_i-2\|+a_i)/(1+a_i)), a_i >=0
common combinations of dimension and a vector:
dim=8, a= [0, 1, 4.5, 9, 99, 99, 99, 99]
dim=6, a=[0, 0.5, 3, 9, 99, 99]
dim = 15, a= [1, 2, 5, 10, 20, 50, 100, 500, 1000, ..., 1000]
Proposed to test sensitivity analysis methods
First order Sobol indices have closed form expression S_i=V_i/V with :
V_i= 1/(3(1+a_i)\^2)
V= Prod_{i=1}\^{d} (1+V_i) - 1
"""
def __init__(
self,
dim: int,
a: List = None,
noise_std: Optional[float] = None,
negate: bool = False,
) -> None:
r"""
Args:
dim: Dimensionality of the problem. If 6, 8, or 15, will use standard a.
a: a parameter, unless dim is 6, 8, or 15.
noise_std: Standard deviation of observation noise.
negate: Return negatie of function.
"""
self._optimizers = None
self.dim = dim
self._bounds = [(0, 1) for _ in range(self.dim)]
if self.dim == 6:
self.a = [0, 0.5, 3, 9, 99, 99]
elif self.dim == 8:
self.a = [0, 1, 4.5, 9, 99, 99, 99, 99]
elif self.dim == 15:
self.a = [
1,
2,
5,
10,
20,
50,
100,
500,
1000,
1000,
1000,
1000,
1000,
1000,
1000,
]
else:
self.a = a
self.optimal_sobol_indicies()
super().__init__(noise_std=noise_std, negate=negate)
@property
def _optimal_value(self) -> float:
raise NotImplementedError
[docs] def optimal_sobol_indicies(self):
vi = []
for i in range(self.dim):
vi.append(1 / (3 * ((1 + self.a[i]) ** 2)))
self.vi = Tensor(vi)
self.V = torch.prod((1 + self.vi)) - 1
self.si = self.vi / self.V
si_t = []
for i in range(self.dim):
si_t.append(
(
self.vi[i]
* torch.prod(self.vi[:i] + 1)
* torch.prod(self.vi[i + 1 :] + 1)
)
/ self.V
)
self.si_t = Tensor(si_t)
[docs] def evaluate_true(self, X: Tensor) -> Tensor:
self.to(device=X.device, dtype=X.dtype)
t = 1
for i in range(self.dim):
t = t * (torch.abs(4 * X[..., i] - 2) + self.a[i]) / (1 + self.a[i])
return t
[docs]class Morris(SyntheticTestFunction):
r"""Morris test function.
20-dimensional function (usually evaluated on `[0, 1]^20`):
f(x) = sum_{i=1}\^20 beta_i w_i + sum_{i<j}\^20 beta_ij w_i w_j
+ sum_{i<j<l}\^20 beta_ijl w_i w_j w_l + 5w_1 w_2 w_3 w_4
Proposed to test sensitivity analysis methods
"""
def __init__(self, noise_std: Optional[float] = None, negate: bool = False) -> None:
r"""
Args:
noise_std: Standard deviation of observation noise.
negate: Return negative of function.
"""
self._optimizers = None
self.dim = 20
self._bounds = [(0, 1) for _ in range(self.dim)]
self.si = [
0.005,
0.008,
0.017,
0.009,
0.016,
0,
0.069,
0.1,
0.15,
0.1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
]
super().__init__(noise_std=noise_std, negate=negate)
@property
def _optimal_value(self) -> float:
raise NotImplementedError
[docs] def evaluate_true(self, X: Tensor) -> Tensor:
self.to(device=X.device, dtype=X.dtype)
W = []
t1 = 0
t2 = 0
t3 = 0
for i in range(self.dim):
if i in [2, 4, 6]:
wi = 2 * (1.1 * X[..., i] / (X[..., i] + 0.1) - 0.5)
else:
wi = 2 * (X[..., i] - 0.5)
W.append(wi)
if i < 10:
betai = 20
else:
betai = (-1) ** (i + 1)
t1 = t1 + betai * wi
for i in range(self.dim):
for j in range(i + 1, self.dim):
if i < 6 or j < 6:
beta_ij = -15
else:
beta_ij = (-1) ** (i + j + 2)
t2 = t2 + beta_ij * W[i] * W[j]
for k in range(j + 1, self.dim):
if i < 5 or j < 5 or k < 5:
beta_ijk = -10
else:
beta_ijk = 0
t3 = t3 + beta_ijk * W[i] * W[j] * W[k]
t4 = 5 * W[0] * W[1] * W[2] * W[3]
return t1 + t2 + t3 + t4
