FuncCraft

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FuncCraft is a Python package backed by a C++17 benchmark-function generator for black-box optimization research. It is designed for scalable benchmark suite generation across dimensions: one editable suite specification can generate hundreds, thousands, or practically unlimited numbers of distinct benchmark functions while controlling the constructed optimum location and optimum value.

Install

python -m pip install --upgrade funccraft
python -m pip install numpy scipy minionpy

YAML-First Workflow

Suite YAML is the easiest way to configure FuncCraft:

base_functions: [1, 9, 10, 11, 12]
composition_base_functions: [9, 10, 11, 12]
coordinate_transforms:
  - kind: rotation
    probability: 0.5
  - kind: blockrotation
    probability: 0.5
value_transforms:
  - kind: none
    probability: 0.5
  - kind: osc
    probability: 0.5
compositions:
  - kind: cpmsum
    probability: 0.5
  - kind: dpmsoftmax
    probability: 0.5
    parameters: [0.005]
min_components: 2
max_components: 4
requested_number_of_functions: 500
master_seed: 1
lower_bound: -100
upper_bound: 100
assigned_fopt: 100.0

Load the YAML and evaluate a function:

import funccraft as fc

dimension = 10
function_index = 0

spec = fc.load_suite_spec("my_suite.yaml")
suite = fc.BenchmarkSuite(spec, dimension)
f = suite.function(function_index)

points = [[0.0] * dimension, [1.0] * dimension]
values = f.evaluate(points)
print(values)

The packaged 2026 suite is also YAML-backed and exposed through a shortcut:

import funccraft as fc

dimension = 10
function_index = 0
year = 2026
version = 1
suite = fc.suite_collection(year, version).benchmark_suite(dimension)
values = suite.evaluate(function_index, [[0.0] * dimension])

All evaluations are batched: pass a list of points, not one flat point vector.

Optimization

SciPy:

import numpy as np
from scipy.optimize import differential_evolution
import funccraft as fc

dimension = 10
function_index = 0
year = 2026
version = 1
suite = fc.suite_collection(year, version).benchmark_suite(dimension)
f = suite.function(function_index)
domain = f.domain
bounds = list(zip(domain.lower_bound, domain.upper_bound))

def objective(x):
    return f.evaluate([np.asarray(x, dtype=float).tolist()])[0]

result = differential_evolution(objective, bounds, seed=1, polish=False)
print(result.x, result.fun)

MinionPy:

import funccraft as fc
import minionpy as mpy

dimension = 10
function_index = 0
year = 2026
version = 1
suite = fc.suite_collection(year, version).benchmark_suite(dimension)
f = suite.function(function_index)
domain = f.domain

optimizer = mpy.Minimizer(
    func=f.evaluate,
    x0=[
        [0.0] * dimension,
        [1.0] * dimension,
        [-0.5] * dimension,
    ],
    bounds=list(zip(domain.lower_bound, domain.upper_bound)),
    algo="ARRDE",
    maxevals=10000,
    callback=None,
    seed=None,
    options=None,
)
result = optimizer.optimize()
print(result.x, result.fun)

Mechanism Summary

FuncCraft builds functions from primitive benchmark landscapes, coordinate transforms, value transforms, and composition rules:

f(x) = assigned_fopt + scale_factor * psi(x, z_1(x), ..., z_m(x))

Implemented mechanism families include:

  • 34 primitive base functions, including Sphere, Rosenbrock, Ackley, Rastrigin, Griewank, Schwefel, Katsuura, Levy, BentCigar, HappyCat, HGBat, and StyblinskiTang.
  • coordinate transforms: none, rotation, affine, block-rotation.
  • value transforms: none, power, oscillatory, cosine-zero.
  • compositions: none, cpm-wsum, cpm-power-mean, cpm-level-well, dpm-softmax, dpm-bgsoftmax.

Names are parsed permissively: case, spaces, hyphens, and underscores are normalized before matching.

Exported Manifests

Input YAML is for configuration and editing. Exported YAML is a materialized record of what FuncCraft built:

f.export_spec("function_materialized.yaml")
suite.export_manifest("suite_manifest.yaml")

Exported specs include generated matrices, selected subspaces, DPM centers and biases, assigned optima, scale factors, labels, and metadata.

Links

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.3.1)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for muzakka from gravatar.com muzakka

Credits

Author: Khoirul Faiq Muzakka

Requires

Python >=3.9

Classifiers

Report project as malware

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0.3.1

24 files

0.3.0

24 files

0.2.1

24 files

This release

0.1.2 This release

24 files

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