botorch.test_functions¶
Abstract Test Function API¶
Base class for test functions for optimization benchmarks.
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class
botorch.test_functions.base.BaseTestProblem(noise_std=None, negate=False)[source]¶ Bases:
torch.nn.modules.module.Module,abc.ABCBase class for test functions.
Base constructor for test functions.
- Parameters
noise_std (
Optional[float]) – Standard deviation of the observation noise.negate (
bool) – If True, negate the function.
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dim: int = None¶
Synthetic Test Functions¶
Synthetic functions for optimization benchmarks. Reference: https://www.sfu.ca/~ssurjano/optimization.html
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class
botorch.test_functions.synthetic.SyntheticTestFunction(noise_std=None, negate=False)[source]¶ Bases:
botorch.test_functions.base.BaseTestProblemBase class for synthetic test functions.
Base constructor for synthetic test functions.
- Parameters
noise_std (
Optional[float]) – Standard deviation of the observation noise.negate (
bool) – If True, negate the function.
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num_objectives: int = 1¶
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property
optimal_value¶ The global minimum (maximum if negate=True) of the function.
- Return type
float
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class
botorch.test_functions.synthetic.Ackley(dim=2, noise_std=None, negate=False)[source]¶ Bases:
botorch.test_functions.synthetic.SyntheticTestFunctionAckley test function.
d-dimensional function (usually evaluated on [-32.768, 32.768]^d):
- f(x) = -A exp(-B sqrt(1/d sum_{i=1}^d x_i^2)) -
exp(1/d sum_{i=1}^d cos(c x_i)) + A + exp(1)
f has one minimizer for its global minimum at z_1 = (0, 0, …, 0) with f(z_1) = 0.
Base constructor for synthetic test functions.
- Parameters
noise_std (
Optional[float]) – Standard deviation of the observation noise.negate (
bool) – If True, negate the function.
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class
botorch.test_functions.synthetic.Beale(noise_std=None, negate=False)[source]¶ Bases:
botorch.test_functions.synthetic.SyntheticTestFunctionBase constructor for synthetic test functions.
- Parameters
noise_std (
Optional[float]) – Standard deviation of the observation noise.negate (
bool) – If True, negate the function.
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dim= 2¶
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class
botorch.test_functions.synthetic.Branin(noise_std=None, negate=False)[source]¶ Bases:
botorch.test_functions.synthetic.SyntheticTestFunctionBranin test function.
Two-dimensional function (usually evaluated on [-5, 10] x [0, 15]):
B(x) = (x_2 - b x_1^2 + c x_1 - r)^2 + 10 (1-t) cos(x_1) + 10
Here b, c, r and t are constants where b = 5.1 / (4 * math.pi ** 2) c = 5 / math.pi, r = 6, t = 1 / (8 * math.pi) B has 3 minimizers for its global minimum at z_1 = (-pi, 12.275), z_2 = (pi, 2.275), z_3 = (9.42478, 2.475) with B(z_i) = 0.397887.
Base constructor for synthetic test functions.
- Parameters
noise_std (
Optional[float]) – Standard deviation of the observation noise.negate (
bool) – If True, negate the function.
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dim= 2¶
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class
botorch.test_functions.synthetic.Bukin(noise_std=None, negate=False)[source]¶ Bases:
botorch.test_functions.synthetic.SyntheticTestFunctionBase constructor for synthetic test functions.
- Parameters
noise_std (
Optional[float]) – Standard deviation of the observation noise.negate (
bool) – If True, negate the function.
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dim= 2¶
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class
botorch.test_functions.synthetic.Cosine8(noise_std=None, negate=False)[source]¶ Bases:
botorch.test_functions.synthetic.SyntheticTestFunctionCosine Mixture test function.
8-dimensional function (usually evaluated on [-1, 1]^8):
f(x) = 0.1 sum_{i=1}^8 cos(5 pi x_i) - sum_{i=1}^8 x_i^2
f has one maximizer for its global maximum at z_1 = (0, 0, …, 0) with f(z_1) = 0.8
Base constructor for synthetic test functions.
- Parameters
noise_std (
Optional[float]) – Standard deviation of the observation noise.negate (
bool) – If True, negate the function.
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dim= 8¶
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class
botorch.test_functions.synthetic.DropWave(noise_std=None, negate=False)[source]¶ Bases:
botorch.test_functions.synthetic.SyntheticTestFunctionBase constructor for synthetic test functions.
- Parameters
noise_std (
Optional[float]) – Standard deviation of the observation noise.negate (
bool) – If True, negate the function.
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dim= 2¶
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class
botorch.test_functions.synthetic.DixonPrice(dim=2, noise_std=None, negate=False)[source]¶ Bases:
botorch.test_functions.synthetic.SyntheticTestFunctionBase constructor for synthetic test functions.
- Parameters
noise_std (
Optional[float]) – Standard deviation of the observation noise.negate (
bool) – If True, negate the function.
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class
botorch.test_functions.synthetic.EggHolder(noise_std=None, negate=False)[source]¶ Bases:
botorch.test_functions.synthetic.SyntheticTestFunctionEggholder test function.
Two-dimensional function (usually evaluated on [-512, 512]^2):
E(x) = (x_2 + 47) sin(R1(x)) - x_1 * sin(R2(x))
where R1(x) = sqrt(|x_2 + x_1 / 2 + 47|), R2(x) = sqrt|x_1 - (x_2 + 47)|).
Base constructor for synthetic test functions.
- Parameters
noise_std (
Optional[float]) – Standard deviation of the observation noise.negate (
bool) – If True, negate the function.
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dim= 2¶
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class
botorch.test_functions.synthetic.Griewank(dim=2, noise_std=None, negate=False)[source]¶ Bases:
botorch.test_functions.synthetic.SyntheticTestFunctionBase constructor for synthetic test functions.
- Parameters
noise_std (
Optional[float]) – Standard deviation of the observation noise.negate (
bool) – If True, negate the function.
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class
botorch.test_functions.synthetic.Hartmann(dim=6, noise_std=None, negate=False)[source]¶ Bases:
botorch.test_functions.synthetic.SyntheticTestFunctionHartmann synthetic test function.
Most commonly used is the six-dimensional version (typically evaluated on [0, 1]^6):
H(x) = - sum_{i=1}^4 ALPHA_i exp( - sum_{j=1}^6 A_ij (x_j - P_ij)**2 )
H has a 6 local minima and a global minimum at
z = (0.20169, 0.150011, 0.476874, 0.275332, 0.311652, 0.6573)
with H(z) = -3.32237.
Base constructor for synthetic test functions.
- Parameters
noise_std (
Optional[float]) – Standard deviation of the observation noise.negate (
bool) – If True, negate the function.
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property
optimal_value¶ The global minimum (maximum if negate=True) of the function.
- Return type
float
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property
optimizers¶ - Return type
Tensor
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class
botorch.test_functions.synthetic.HolderTable(noise_std=None, negate=False)[source]¶ Bases:
botorch.test_functions.synthetic.SyntheticTestFunctionHolder Table synthetic test function.
Two-dimensional function (typically evaluated on [0, 10] x [0, 10]):
H(x) = - | sin(x_1) * cos(x_2) * exp(| 1 - ||x|| / pi | ) |
H has 4 global minima with H(z_i) = -19.2085 at
z_1 = ( 8.05502, 9.66459) z_2 = (-8.05502, -9.66459) z_3 = (-8.05502, 9.66459) z_4 = ( 8.05502, -9.66459)
Base constructor for synthetic test functions.
- Parameters
noise_std (
Optional[float]) – Standard deviation of the observation noise.negate (
bool) – If True, negate the function.
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dim= 2¶
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class
botorch.test_functions.synthetic.Levy(dim=2, noise_std=None, negate=False)[source]¶ Bases:
botorch.test_functions.synthetic.SyntheticTestFunctionLevy synthetic test function.
d-dimensional function (usually evaluated on [-10, 10]^d):
- f(x) = sin^2(pi w_1) +
sum_{i=1}^{d-1} (w_i-1)^2 (1 + 10 sin^2(pi w_i + 1)) + (w_d - 1)^2 (1 + sin^2(2 pi w_d))
where w_i = 1 + (x_i - 1) / 4 for all i.
f has one minimizer for its global minimum at z_1 = (1, 1, …, 1) with f(z_1) = 0.
Base constructor for synthetic test functions.
- Parameters
noise_std (
Optional[float]) – Standard deviation of the observation noise.negate (
bool) – If True, negate the function.
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class
botorch.test_functions.synthetic.Michalewicz(dim=2, noise_std=None, negate=False)[source]¶ Bases:
botorch.test_functions.synthetic.SyntheticTestFunctionMichalewicz synthetic test function.
d-dim function (usually evaluated on hypercube [0, pi]^d):
M(x) = sum_{i=1}^d sin(x_i) (sin(i x_i^2 / pi)^20)
Base constructor for synthetic test functions.
- Parameters
noise_std (
Optional[float]) – Standard deviation of the observation noise.negate (
bool) – If True, negate the function.
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property
optimizers¶ - Return type
Tensor
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class
botorch.test_functions.synthetic.Powell(dim=4, noise_std=None, negate=False)[source]¶ Bases:
botorch.test_functions.synthetic.SyntheticTestFunctionBase constructor for synthetic test functions.
- Parameters
noise_std (
Optional[float]) – Standard deviation of the observation noise.negate (
bool) – If True, negate the function.
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class
botorch.test_functions.synthetic.Rastrigin(dim=2, noise_std=None, negate=False)[source]¶ Bases:
botorch.test_functions.synthetic.SyntheticTestFunctionBase constructor for synthetic test functions.
- Parameters
noise_std (
Optional[float]) – Standard deviation of the observation noise.negate (
bool) – If True, negate the function.
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class
botorch.test_functions.synthetic.Rosenbrock(dim=2, noise_std=None, negate=False)[source]¶ Bases:
botorch.test_functions.synthetic.SyntheticTestFunctionRosenbrock synthetic test function.
d-dimensional function (usually evaluated on [-5, 10]^d):
f(x) = sum_{i=1}^{d-1} (100 (x_{i+1} - x_i^2)^2 + (x_i - 1)^2)
f has one minimizer for its global minimum at z_1 = (1, 1, …, 1) with f(z_i) = 0.0.
Base constructor for synthetic test functions.
- Parameters
noise_std (
Optional[float]) – Standard deviation of the observation noise.negate (
bool) – If True, negate the function.
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class
botorch.test_functions.synthetic.Shekel(m=10, noise_std=None, negate=False)[source]¶ Bases:
botorch.test_functions.synthetic.SyntheticTestFunctionShekel synthtetic test function.
4-dimensional function (usually evaluated on [0, 10]^4):
f(x) = -sum_{i=1}^10 (sum_{j=1}^4 (x_j - A_{ji})^2 + C_i)^{-1}
f has one minimizer for its global minimum at z_1 = (4, 4, 4, 4) with f(z_1) = -10.5363.
Base constructor for synthetic test functions.
- Parameters
noise_std (
Optional[float]) – Standard deviation of the observation noise.negate (
bool) – If True, negate the function.
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dim= 4¶
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class
botorch.test_functions.synthetic.SixHumpCamel(noise_std=None, negate=False)[source]¶ Bases:
botorch.test_functions.synthetic.SyntheticTestFunctionBase constructor for synthetic test functions.
- Parameters
noise_std (
Optional[float]) – Standard deviation of the observation noise.negate (
bool) – If True, negate the function.
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dim= 2¶
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class
botorch.test_functions.synthetic.StyblinskiTang(dim=2, noise_std=None, negate=False)[source]¶ Bases:
botorch.test_functions.synthetic.SyntheticTestFunctionStyblinski-Tang synthtetic test function.
d-dimensional function (usually evaluated on the hypercube [-5, 5]^d):
H(x) = 0.5 * sum_{i=1}^d (x_i^4 - 16 * x_i^2 + 5 * x_i)
H has a single global mininimum H(z) = -39.166166 * d at z = [-2.903534]^d
Base constructor for synthetic test functions.
- Parameters
noise_std (
Optional[float]) – Standard deviation of the observation noise.negate (
bool) – If True, negate the function.
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class
botorch.test_functions.synthetic.ThreeHumpCamel(noise_std=None, negate=False)[source]¶ Bases:
botorch.test_functions.synthetic.SyntheticTestFunctionBase constructor for synthetic test functions.
- Parameters
noise_std (
Optional[float]) – Standard deviation of the observation noise.negate (
bool) – If True, negate the function.
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dim= 2¶
Multi-Fidelity Synthetic Test Functions¶
Synthetic functions for multi-fidelity optimization benchmarks.
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class
botorch.test_functions.multi_fidelity.AugmentedBranin(noise_std=None, negate=False)[source]¶ Bases:
botorch.test_functions.synthetic.SyntheticTestFunctionAugmented Branin test function for multi-fidelity optimization.
3-dimensional function with domain [-5, 10] x [0, 15] * [0,1], where the last dimension of is the fidelity parameter:
- B(x) = (x_2 - (b - 0.1 * (1 - x_3))x_1^2 + c x_1 - r)^2 +
10 (1-t) cos(x_1) + 10
Here b, c, r and t are constants where b = 5.1 / (4 * math.pi ** 2) c = 5 / math.pi, r = 6, t = 1 / (8 * math.pi). B has infinitely many minimizers with x_1 = -pi, pi, 3pi and B_min = 0.397887
Base constructor for synthetic test functions.
- Parameters
noise_std (
Optional[float]) – Standard deviation of the observation noise.negate (
bool) – If True, negate the function.
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dim= 3¶
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class
botorch.test_functions.multi_fidelity.AugmentedHartmann(noise_std=None, negate=False)[source]¶ Bases:
botorch.test_functions.synthetic.SyntheticTestFunctionAugmented Hartmann synthetic test function.
7-dimensional function (typically evaluated on [0, 1]^7), where the last dimension is the fidelity parameter.
- H(x) = -(ALPHA_1 - 0.1 * (1-x_7)) * exp(- sum_{j=1}^6 A_1j (x_j - P_1j) ** 2) -
sum_{i=2}^4 ALPHA_i exp( - sum_{j=1}^6 A_ij (x_j - P_ij) ** 2)
H has a unique global minimizer x = [0.20169, 0.150011, 0.476874, 0.275332, 0.311652, 0.6573, 1.0]
with H_min = -3.32237
Base constructor for synthetic test functions.
- Parameters
noise_std (
Optional[float]) – Standard deviation of the observation noise.negate (
bool) – If True, negate the function.
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dim= 7¶
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class
botorch.test_functions.multi_fidelity.AugmentedRosenbrock(dim=3, noise_std=None, negate=False)[source]¶ Bases:
botorch.test_functions.synthetic.SyntheticTestFunctionAugmented Rosenbrock synthetic test function for multi-fidelity optimization.
d-dimensional function (usually evaluated on [-5, 10]^(d-2) * [0, 1]^2), where the last two dimensions are the fidelity parameters:
- f(x) = sum_{i=1}^{d-1} (100 (x_{i+1} - x_i^2 + 0.1 * (1-x_{d-1}))^2 +
(x_i - 1 + 0.1 * (1 - x_d)^2)^2)
f has one minimizer for its global minimum at z_1 = (1, 1, …, 1) with f(z_i) = 0.0.
Base constructor for synthetic test functions.
- Parameters
noise_std (
Optional[float]) – Standard deviation of the observation noise.negate (
bool) – If True, negate the function.