Source code for botorch.test_functions.synthetic

#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

r"""
Synthetic functions for optimization benchmarks.
Reference: https://www.sfu.ca/~ssurjano/optimization.html
"""

from __future__ import annotations

import math
from typing import List, Optional, Tuple

import torch
from botorch.test_functions.base import BaseTestProblem
from torch import Tensor


[docs]class SyntheticTestFunction(BaseTestProblem): r"""Base class for synthetic test functions.""" _optimizers: List[Tuple[float, ...]] _optimal_value: float num_objectives: int = 1 def __init__(self, noise_std: Optional[float] = None, negate: bool = False) -> None: r"""Base constructor for synthetic test functions. Args: noise_std: Standard deviation of the observation noise. negate: If True, negate the function. """ super().__init__(noise_std=noise_std, negate=negate) if self._optimizers is not None: self.register_buffer( "optimizers", torch.tensor(self._optimizers, dtype=torch.float) ) @property def optimal_value(self) -> float: r"""The global minimum (maximum if negate=True) of the function.""" return -self._optimal_value if self.negate else self._optimal_value
[docs]class Ackley(SyntheticTestFunction): r"""Ackley 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`. """ _optimal_value = 0.0 _check_grad_at_opt: bool = False def __init__( self, dim: int = 2, noise_std: Optional[float] = None, negate: bool = False ) -> None: self.dim = dim self._bounds = [(-32.768, 32.768) for _ in range(self.dim)] self._optimizers = [tuple(0.0 for _ in range(self.dim))] super().__init__(noise_std=noise_std, negate=negate) self.a = 20 self.b = 0.2 self.c = 2 * math.pi
[docs] def evaluate_true(self, X: Tensor) -> Tensor: a, b, c = self.a, self.b, self.c part1 = -a * torch.exp(-b / math.sqrt(self.dim) * torch.norm(X, dim=-1)) part2 = -(torch.exp(torch.mean(torch.cos(c * X), dim=-1))) return part1 + part2 + a + math.e
[docs]class Beale(SyntheticTestFunction): dim = 2 _optimal_value = 0.0 _bounds = [(-4.5, 4.5), (-4.5, 4.5)] _optimizers = [(3.0, 0.5)]
[docs] def evaluate_true(self, X: Tensor) -> Tensor: x1, x2 = X[..., 0], X[..., 1] part1 = (1.5 - x1 + x1 * x2) ** 2 part2 = (2.25 - x1 + x1 * x2 ** 2) ** 2 part3 = (2.625 - x1 + x1 * x2 ** 3) ** 2 return part1 + part2 + part3
[docs]class Branin(SyntheticTestFunction): r"""Branin 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`. """ dim = 2 _bounds = [(-5.0, 10.0), (0.0, 15.0)] _optimal_value = 0.397887 _optimizers = [(-math.pi, 12.275), (math.pi, 2.275), (9.42478, 2.475)]
[docs] def evaluate_true(self, X: Tensor) -> Tensor: t1 = ( X[..., 1] - 5.1 / (4 * math.pi ** 2) * X[..., 0] ** 2 + 5 / math.pi * X[..., 0] - 6 ) t2 = 10 * (1 - 1 / (8 * math.pi)) * torch.cos(X[..., 0]) return t1 ** 2 + t2 + 10
[docs]class Bukin(SyntheticTestFunction): dim = 2 _bounds = [(-15.0, -5.0), (-3.0, 3.0)] _optimal_value = 0.0 _optimizers = [(-10.0, 1.0)] _check_grad_at_opt: bool = False
[docs] def evaluate_true(self, X: Tensor) -> Tensor: part1 = 100.0 * torch.sqrt(torch.abs(X[..., 1] - 0.01 * X[..., 0] ** 2)) part2 = 0.01 * torch.abs(X[..., 0] + 10.0) return part1 + part2
[docs]class Cosine8(SyntheticTestFunction): r"""Cosine 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` """ dim = 8 _bounds = [(-1.0, 1.0) for _ in range(8)] _optimal_value = 0.8 _optimizers = [tuple(0.0 for _ in range(8))]
[docs] def evaluate_true(self, X: Tensor) -> Tensor: return torch.sum(0.1 * torch.cos(5 * math.pi * X) - X ** 2, dim=-1)
[docs]class DropWave(SyntheticTestFunction): dim = 2 _bounds = [(-5.12, 5.12), (-5.12, 5.12)] _optimal_value = -1.0 _optimizers = [(0.0, 0.0)] _check_grad_at_opt = False
[docs] def evaluate_true(self, X: Tensor) -> Tensor: norm = torch.norm(X, dim=-1) part1 = 1.0 + torch.cos(12.0 * norm) part2 = 0.5 * norm.pow(2) + 2.0 return -part1 / part2
[docs]class DixonPrice(SyntheticTestFunction): _optimal_value = 0.0 def __init__( self, dim=2, noise_std: Optional[float] = None, negate: bool = False ) -> None: self.dim = dim self._bounds = [(-10.0, 10.0) for _ in range(self.dim)] self._optimizers = [ tuple( math.pow(2.0, -(1.0 - 2.0 ** (-(i - 1)))) for i in range(1, self.dim + 1) ) ] super().__init__(noise_std=noise_std, negate=negate)
[docs] def evaluate_true(self, X: Tensor) -> Tensor: d = self.dim part1 = (X[..., 0] - 1) ** 2 i = X.new(range(2, d + 1)) part2 = torch.sum(i * (2.0 * X[..., 1:] ** 2 - X[..., :-1]) ** 2, dim=-1) return part1 + part2
[docs]class EggHolder(SyntheticTestFunction): r"""Eggholder 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)|)`. """ dim = 2 _bounds = [(-512.0, 512.0), (-512.0, 512.0)] _optimal_value = -959.6407 _optimizers = [(512.0, 404.2319)] _check_grad_at_opt: bool = False
[docs] def evaluate_true(self, X: Tensor) -> Tensor: x1, x2 = X[..., 0], X[..., 1] part1 = -(x2 + 47.0) * torch.sin(torch.sqrt(torch.abs(x2 + x1 / 2.0 + 47.0))) part2 = -x1 * torch.sin(torch.sqrt(torch.abs(x1 - (x2 + 47.0)))) return part1 + part2
[docs]class Griewank(SyntheticTestFunction): _optimal_value = 0.0 def __init__( self, dim=2, noise_std: Optional[float] = None, negate: bool = False ) -> None: self.dim = dim self._bounds = [(-600.0, 600.0) for _ in range(self.dim)] self._optimizers = [tuple(0.0 for _ in range(self.dim))] super().__init__(noise_std=noise_std, negate=negate)
[docs] def evaluate_true(self, X: Tensor) -> Tensor: part1 = torch.sum(X ** 2 / 4000.0, dim=-1) d = X.shape[-1] part2 = -(torch.prod(torch.cos(X / torch.sqrt(X.new(range(1, d + 1)))), dim=-1)) return part1 + part2 + 1.0
[docs]class Hartmann(SyntheticTestFunction): r"""Hartmann 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`. """ def __init__( self, dim=6, noise_std: Optional[float] = None, negate: bool = False ) -> None: if dim not in (3, 4, 6): raise ValueError(f"Hartmann with dim {dim} not defined") self.dim = dim self._bounds = [(0.0, 1.0) for _ in range(self.dim)] # optimizers and optimal values for dim=4 not implemented optvals = {3: -3.86278, 6: -3.32237} optimizers = { 3: [(0.114614, 0.555649, 0.852547)], 6: [(0.20169, 0.150011, 0.476874, 0.275332, 0.311652, 0.6573)], } self._optimal_value = optvals.get(self.dim) self._optimizers = optimizers.get(self.dim) super().__init__(noise_std=noise_std, negate=negate) self.register_buffer("ALPHA", torch.tensor([1.0, 1.2, 3.0, 3.2])) if dim == 3: A = [[3.0, 10, 30], [0.1, 10, 35], [3.0, 10, 30], [0.1, 10, 35]] P = [ [3689, 1170, 2673], [4699, 4387, 7470], [1091, 8732, 5547], [381, 5743, 8828], ] elif dim == 4: A = [ [10, 3, 17, 3.5], [0.05, 10, 17, 0.1], [3, 3.5, 1.7, 10], [17, 8, 0.05, 10], ] P = [ [1312, 1696, 5569, 124], [2329, 4135, 8307, 3736], [2348, 1451, 3522, 2883], [4047, 8828, 8732, 5743], ] elif dim == 6: A = [ [10, 3, 17, 3.5, 1.7, 8], [0.05, 10, 17, 0.1, 8, 14], [3, 3.5, 1.7, 10, 17, 8], [17, 8, 0.05, 10, 0.1, 14], ] P = [ [1312, 1696, 5569, 124, 8283, 5886], [2329, 4135, 8307, 3736, 1004, 9991], [2348, 1451, 3522, 2883, 3047, 6650], [4047, 8828, 8732, 5743, 1091, 381], ] self.register_buffer("A", torch.tensor(A, dtype=torch.float)) self.register_buffer("P", torch.tensor(P, dtype=torch.float)) @property def optimal_value(self) -> float: if self.dim == 4: raise NotImplementedError() return super().optimal_value @property def optimizers(self) -> Tensor: if self.dim == 4: raise NotImplementedError() return super().optimizers
[docs] def evaluate_true(self, X: Tensor) -> Tensor: self.to(device=X.device, dtype=X.dtype) inner_sum = torch.sum(self.A * (X.unsqueeze(-2) - 0.0001 * self.P) ** 2, dim=-1) H = -(torch.sum(self.ALPHA * torch.exp(-inner_sum), dim=-1)) if self.dim == 4: H = (1.1 + H) / 0.839 return H
[docs]class HolderTable(SyntheticTestFunction): r"""Holder 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) """ dim = 2 _bounds = [(-10.0, 10.0), (-10.0, 10.0)] _optimal_value = -19.2085 _optimizers = [ (8.05502, 9.66459), (-8.05502, -9.66459), (-8.05502, 9.66459), (8.05502, -9.66459), ]
[docs] def evaluate_true(self, X: Tensor) -> Tensor: term = torch.abs(1 - torch.norm(X, dim=-1) / math.pi) return -( torch.abs(torch.sin(X[..., 0]) * torch.cos(X[..., 1]) * torch.exp(term)) )
[docs]class Levy(SyntheticTestFunction): r"""Levy 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`. """ _optimal_value = 0.0 def __init__( self, dim=2, noise_std: Optional[float] = None, negate: bool = False ) -> None: self.dim = dim self._bounds = [(-10.0, 10.0) for _ in range(self.dim)] self._optimizers = [tuple(1.0 for _ in range(self.dim))] super().__init__(noise_std=noise_std, negate=negate)
[docs] def evaluate_true(self, X: Tensor) -> Tensor: w = 1.0 + (X - 1.0) / 4.0 part1 = torch.sin(math.pi * w[..., 0]) ** 2 part2 = torch.sum( (w[..., :-1] - 1.0) ** 2 * (1.0 + 10.0 * torch.sin(math.pi * w[..., :-1] + 1.0) ** 2), dim=-1, ) part3 = (w[..., -1] - 1.0) ** 2 * ( 1.0 + torch.sin(2.0 * math.pi * w[..., -1]) ** 2 ) return part1 + part2 + part3
[docs]class Michalewicz(SyntheticTestFunction): r"""Michalewicz 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) """ def __init__( self, dim=2, noise_std: Optional[float] = None, negate: bool = False ) -> None: self.dim = dim self._bounds = [(0.0, math.pi) for _ in range(self.dim)] optvals = {2: -1.80130341, 5: -4.687658, 10: -9.66015} optimizers = {2: [(2.20290552, 1.57079633)]} self._optimal_value = optvals.get(self.dim) self._optimizers = optimizers.get(self.dim) super().__init__(noise_std=noise_std, negate=negate) self.register_buffer( "i", torch.tensor(tuple(range(1, self.dim + 1)), dtype=torch.float) ) @property def optimizers(self) -> Tensor: if self.dim in (5, 10): raise NotImplementedError() return super().optimizers
[docs] def evaluate_true(self, X: Tensor) -> Tensor: self.to(device=X.device, dtype=X.dtype) m = 10 return -( torch.sum( torch.sin(X) * torch.sin(self.i * X ** 2 / math.pi) ** (2 * m), dim=-1 ) )
[docs]class Powell(SyntheticTestFunction): _optimal_value = 0.0 def __init__( self, dim=4, noise_std: Optional[float] = None, negate: bool = False ) -> None: self.dim = dim self._bounds = [(-4.0, 5.0) for _ in range(self.dim)] self._optimizers = [tuple(0.0 for _ in range(self.dim))] super().__init__(noise_std=noise_std, negate=negate)
[docs] def evaluate_true(self, X: Tensor) -> Tensor: result = torch.zeros_like(X[..., 0]) for i in range(self.dim // 4): i_ = i + 1 part1 = (X[..., 4 * i_ - 4] + 10.0 * X[..., 4 * i_ - 3]) ** 2 part2 = 5.0 * (X[..., 4 * i_ - 2] - X[..., 4 * i_ - 1]) ** 2 part3 = (X[..., 4 * i_ - 3] - 2.0 * X[..., 4 * i_ - 2]) ** 4 part4 = 10.0 * (X[..., 4 * i_ - 4] - X[..., 4 * i_ - 1]) ** 4 result += part1 + part2 + part3 + part4 return result
[docs]class Rastrigin(SyntheticTestFunction): _optimal_value = 0.0 def __init__( self, dim=2, noise_std: Optional[float] = None, negate: bool = False ) -> None: self.dim = dim self._bounds = [(-5.12, 5.12) for _ in range(self.dim)] self._optimizers = [tuple(0.0 for _ in range(self.dim))] super().__init__(noise_std=noise_std, negate=negate)
[docs] def evaluate_true(self, X: Tensor) -> Tensor: return 10.0 * self.dim + torch.sum( X ** 2 - 10.0 * torch.cos(2.0 * math.pi * X), dim=-1 )
[docs]class Rosenbrock(SyntheticTestFunction): r"""Rosenbrock 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`. """ _optimal_value = 0.0 def __init__( self, dim=2, noise_std: Optional[float] = None, negate: bool = False ) -> None: self.dim = dim self._bounds = [(-5.0, 10.0) for _ in range(self.dim)] self._optimizers = [tuple(1.0 for _ in range(self.dim))] super().__init__(noise_std=noise_std, negate=negate)
[docs] def evaluate_true(self, X: Tensor) -> Tensor: return torch.sum( 100.0 * (X[..., 1:] - X[..., :-1] ** 2) ** 2 + (X[..., :-1] - 1) ** 2, dim=-1, )
[docs]class Shekel(SyntheticTestFunction): r"""Shekel 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`. """ dim = 4 _bounds = [(0.0, 10.0), (0.0, 10.0), (0.0, 10.0), (0.0, 10.0)] _optimizers = [(4.000747, 3.99951, 4.00075, 3.99951)] def __init__( self, m: int = 10, noise_std: Optional[float] = None, negate: bool = False ) -> None: self.m = m optvals = {5: -10.1532, 7: -10.4029, 10: -10.536443} self._optimal_value = optvals[self.m] super().__init__(noise_std=noise_std, negate=negate) self.register_buffer( "beta", torch.tensor([1, 2, 2, 4, 4, 6, 3, 7, 5, 5], dtype=torch.float) ) C_t = torch.tensor( [ [4, 1, 8, 6, 3, 2, 5, 8, 6, 7], [4, 1, 8, 6, 7, 9, 3, 1, 2, 3.6], [4, 1, 8, 6, 3, 2, 5, 8, 6, 7], [4, 1, 8, 6, 7, 9, 3, 1, 2, 3.6], ], dtype=torch.float, ) self.register_buffer("C", C_t.transpose(-1, -2))
[docs] def evaluate_true(self, X: Tensor) -> Tensor: self.to(device=X.device, dtype=X.dtype) beta = self.beta / 10.0 result = -sum( 1 / (torch.sum((X - self.C[i]) ** 2, dim=-1) + beta[i]) for i in range(self.m) ) return result
[docs]class SixHumpCamel(SyntheticTestFunction): dim = 2 _bounds = [(-3.0, 3.0), (-2.0, 2.0)] _optimal_value = -1.0316 _optimizers = [(0.0898, -0.7126), (-0.0898, 0.7126)]
[docs] def evaluate_true(self, X: Tensor) -> Tensor: x1, x2 = X[..., 0], X[..., 1] return ( (4 - 2.1 * x1 ** 2 + x1 ** 4 / 3) * x1 ** 2 + x1 * x2 + (4 * x2 ** 2 - 4) * x2 ** 2 )
[docs]class StyblinskiTang(SyntheticTestFunction): r"""Styblinski-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` """ def __init__( self, dim=2, noise_std: Optional[float] = None, negate: bool = False ) -> None: self.dim = dim self._bounds = [(-5.0, 5.0) for _ in range(self.dim)] self._optimal_value = -39.166166 * self.dim self._optimizers = [tuple(-2.903534 for _ in range(self.dim))] super().__init__(noise_std=noise_std, negate=negate)
[docs] def evaluate_true(self, X: Tensor) -> Tensor: return 0.5 * (X ** 4 - 16 * X ** 2 + 5 * X).sum(dim=-1)
[docs]class ThreeHumpCamel(SyntheticTestFunction): dim = 2 _bounds = [(-5.0, 5.0), (-5.0, 5.0)] _optimal_value = 0.0 _optimizers = [(0.0, 0.0)]
[docs] def evaluate_true(self, X: Tensor) -> Tensor: x1, x2 = X[..., 0], X[..., 1] return 2.0 * x1 ** 2 - 1.05 * x1 ** 4 + x1 ** 6 / 6.0 + x1 * x2 + x2 ** 2