lonkit 0.8.0

Local Optima Networks

lonkit is a Python library for constructing, analyzing, and visualizing Local Optima Networks (LONs) for both continuous and discrete optimization problems. LONs provide a powerful way to understand the structure of fitness landscapes, revealing how local optima are connected and how difficult it may be to find global optima.

Features

• Basin-Hopping Sampling: Efficient exploration of continuous fitness landscapes using configurable Basin-Hopping
• Iterated Local Search: Discrete LON construction via ILS with built-in problems (Number Partitioning, NK Landscape, OneMax)
• LON Construction: Automatic construction of Local Optima Networks from sampling data
• CMLON Support: Compressed Monotonic LONs for cleaner landscape analysis
• Rich Metrics: Compute landscape metrics including funnel analysis and neutrality
• Beautiful Visualizations: 2D and 3D plots with support for animated GIFs

Installation

pip install lonkit

Or install from source:

git clone https://github.com/helix-agh/lonkit.git
cd lonkit
pip install -e .

Quick Start

import numpy as np
from lonkit import compute_lon, LONVisualizer, BasinHoppingSamplerConfig

# Define an objective function
def rastrigin(x: np.ndarray) -> float:
    return 10 * len(x) + np.sum(x**2 - 10 * np.cos(2 * np.pi * x))

# Construct the LON
config = BasinHoppingSamplerConfig(
    n_runs=20,
    n_iter_no_change=500,
    seed=42
)
lon = compute_lon(
    rastrigin,
    dim=2,
    lower_bound=-5.12,
    upper_bound=5.12,
    config=config
)

metrics = lon.compute_metrics()
print(f"Number of funnels: {metrics['n_funnels']}")
print(f"Global funnels: {metrics['n_global_funnels']}")

# Visualize
viz = LONVisualizer()
viz.plot_2d(lon, output_path="lon_2d.png")
viz.plot_3d(lon, output_path="lon_3d.png")

Compressed Monotonic LONs (CMLONs)

CMLONs are a compressed representation where nodes with equal fitness that are connected get merged. This provides a cleaner view of the landscape's funnel structure.

# Convert LON to CMLON
cmlon = lon.to_cmlon()

# Analyze CMLON-specific metrics
cmlon_metrics = cmlon.compute_metrics()

Custom Sampling Configuration

from lonkit import BasinHoppingSampler, BasinHoppingSamplerConfig

config = BasinHoppingSamplerConfig(
    n_runs=50,                   # Number of independent runs
    n_iter_no_change=1000,       # Stop after this many consecutive non-improving perturbations
    step_size=0.05,              # Perturbation size
    step_mode="percentage",      # "percentage" or "fixed"
    coordinate_precision=4,      # Precision for identifying optima
    fitness_precision=None,      # Precision for fitness values (None = full double)
    seed=42                      # For reproducibility
)

sampler = BasinHoppingSampler(config)

# Define search domain
domain = [(-5.12, 5.12), (-5.12, 5.12)]

# Run sampling
result = sampler.sample(rastrigin, domain)
lon = sampler.sample_to_lon(result)

Discrete Optimization Problems

lonkit also supports Local Optima Networks for discrete optimization problems using Iterated Local Search (ILS).

Quick Start (Discrete)

from lonkit import NumberPartitioning, ILSSampler, ILSSamplerConfig, LONVisualizer

# Define problem instance
problem = NumberPartitioning(n=20, k=0.5, instance_seed=1)

# Configure and run ILS sampling
config = ILSSamplerConfig(n_runs=50, n_iter_no_change=100, seed=42)
sampler = ILSSampler(config)
result = sampler.sample(problem)

# Build LON and CMLON
lon = sampler.sample_to_lon(result)
cmlon = lon.to_cmlon()

# Compute metrics
print(lon.compute_metrics())
print(cmlon.compute_metrics())

# Visualize
viz = LONVisualizer()
viz.plot_2d(cmlon, output_path="npp_cmlon.png")

Custom Discrete Problems

You can define your own discrete problem by subclassing DiscreteProblem. For bitstring problems, inherit from BitstringProblem instead it provides random_solution(), local_search(), perturb(), and solution_id() out of the box. You only need to implement evaluate().

Documentation

For full documentation, visit: https://helix-agh.github.io/lonkit

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.