General Examples

These examples demonstrate Hyperactive’s core functionality with simple, illustrative use cases.

Running Examples

All examples are available in the examples directory on GitHub. You can run any example directly:

# Clone the repository
git clone https://github.com/SimonBlanke/Hyperactive.git
cd Hyperactive/examples

# Run an example
python gfo/hill_climbing_example.py

Custom Function Optimization

The simplest use case: optimizing a mathematical function.

import numpy as np
from hyperactive.opt.gfo import HillClimbing


def objective(params):
    x = params["x"]
    y = params["y"]
    return -(x**2 + y**2)  # Maximize (minimize the parabola)


search_space = {
    "x": np.arange(-5, 5, 0.1),
    "y": np.arange(-5, 5, 0.1),
}

optimizer = HillClimbing(
    search_space=search_space,
    n_iter=100,
    experiment=objective,
)
best_params = optimizer.solve()
print(f"Best parameters: {best_params}")

Scikit-learn Model Tuning

Hyperparameter optimization for machine learning models.

from sklearn.datasets import load_wine
from sklearn.ensemble import RandomForestClassifier
from hyperactive.experiment.integrations import SklearnCvExperiment
from hyperactive.opt.gfo import HillClimbing

# Load data
X, y = load_wine(return_X_y=True)

# Create experiment
experiment = SklearnCvExperiment(
    estimator=RandomForestClassifier(random_state=42),
    X=X, y=y, cv=3,
)

# Define search space
search_space = {
    "n_estimators": list(range(10, 201)),
    "max_depth": list(range(1, 21)),
    "min_samples_split": list(range(2, 21)),
    "min_samples_leaf": list(range(1, 11)),
}

# Optimize
optimizer = HillClimbing(
    search_space=search_space,
    n_iter=40,
    random_state=42,
    experiment=experiment,
)
best_params = optimizer.solve()