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A collection and visualization of black-box objective functions

Project description

Surfaces


A collection and visualization of single objective black-box functions for optimization benchmarking


Visualizations

Objective Function Heatmap Surface Plot
Sphere function

Rastrigin function

Ackley function

Rosenbrock function

Beale function

Himmelblaus function

Hölder Table function

Cross-In-Tray function


Installation

The most recent version of Surfaces is available on PyPi:

pip install surfaces

Example

import numpy as np

from surfaces.convex import SphereFunction
from surfaces.non_convex import AckleyFunction
from surfaces.visualize import plotly_surface


sphere_function = SphereFunction(n_dim=2, metric="score")
ackley_function = AckleyFunction(metric="loss")


step_ = 0.05
min_ = 10
max_ = 10
search_space = {
    "x0": np.arange(-min_, max_, step_),
    "x1": np.arange(-min_, max_, step_),
}

plotly_surface(sphere_function, search_space).show()
plotly_surface(ackley_function, search_space).show()

API reference

Objective Function Classes

All objective function classes have the following parameters:

  • metric: "score" or "loss"
  • input_type: "dictionary" or "arrays"

Each objective function class has the following parameters:

SphereFunction
  • A = 1
AckleyFunction
  • A = 20
  • B = 2 * pi
RastriginFunction
  • A = 10
  • B = 2 * pi
RosenbrockFunction
  • A = 1
  • B = 100
BealeFunction
  • A = 1.5
  • B = 2.25
  • C = 2.652
HimmelblausFunction
  • A = -11
  • B = -7
HölderTableFunction
  • A = 10
  • B = 1
CrossInTrayFunction
  • A = -0.0001
  • B = 100
  • C = 1

Project details


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