parallel-hill-climber 0.1.3

A Python package for hill climbing optimization with simulated annealing

Hill Climber

A Python package for hill climbing optimization of user-supplied objective functions with simulated annealing. Designed for flexible multi-objective optimization with support for N-dimensional data.

Documentation

View Full Documentation on GitHub Pages

Features

• Simulated Annealing: Temperature-based acceptance of suboptimal solutions to escape local minima
• Parallel Execution: Run multiple replicates simultaneously for diverse solutions
• Flexible Objectives: Support for any objective function with multiple metrics
• N-Dimensional Support: Optimize distributions with any number of dimensions
• Checkpoint/Resume: Save and resume long-running optimizations
• Boundary Handling: Reflection-based strategy prevents point accumulation at boundaries
• Visualization: Built-in plotting for both input data and optimization results
• JIT Compilation: Numba-optimized core functions for performance

Installation

pip install -r requirements.txt

Requirements

• Python 3.8+
• NumPy
• Pandas
• SciPy
• Matplotlib
• Numba

Quick Start

from hill_climber import HillClimber
import pandas as pd
import numpy as np

# Create sample data
data = pd.DataFrame({
    'x': np.random.rand(100),
    'y': np.random.rand(100)
})

# Define objective function
def my_objective(x, y):
    correlation = pd.Series(x).corr(pd.Series(y))
    metrics = {'correlation': correlation}

    return metrics, correlation

# Create optimizer
climber = HillClimber(
    data=data,
    objective_func=my_objective,
    max_time=1,  # minutes
    step_size=0.5,
    mode='maximize'
)

# Run optimization with multiple replicates
results = climber.climb_parallel(replicates=4, initial_noise=0.1)

# Visualize results
climber.plot_results(results, plot_type='histogram')

For detailed usage, configuration options, and advanced features, see the full documentation.

Example Notebooks

The notebooks/ directory contains complete worked examples demonstrating various use cases:

1. Simulated Annealing: Introduction to the algorithm
2. Pearson & Spearman: Optimizing for different correlation measures
3. Mean & Std: Creating distributions with matching statistics but diverse structures
4. Entropy & Correlation: Low correlation with internal structure
5. Feature Interactions: Machine learning feature engineering demonstrations
6. Checkpointing: Long-running optimization with save/resume

See the documentation for rendered versions of all notebooks.

Testing

# Run all tests
python -m pytest tests/

# Run specific test file
python -m pytest tests/test_hill_climber.py

# Run with coverage
python -m pytest tests/ --cov=hill_climber

License

See LICENSE file for details.

Contributing

Contributions welcome! Please ensure all tests pass before submitting pull requests.

Citation

If you use this package in your research, please cite appropriately.