hypdelta is a Python library for calculating delta hyperbolicity of distance matrices using various strategies and computational devices (CPU/GPU). It provides flexibility in choosing the method and device for computation to balance between accuracy and performance.

• Multiple Strategies: Supports naive, condensed, heuristic, CCL, and cartesian strategies for calculating delta hyperbolicity.
• Device Flexibility: Can run on both CPU and GPU.
• Customizable Parameters: Allows for setting parameters like block size, number of tries, and heuristic options.

To install hypdelta, you can clone the repository and install the requirements:

pip install hypdelta

Here's a basic example to get you started with hypdelta:

import numpy as np
from hypdelta import hypdelta

# Generate a synthetic distance matrix
def generate_synthetic_points(dimensions, num_points):
    points = np.random.rand(num_points, dimensions)
    return points

def build_dist_matrix(data):
    arr_all_dist = []
    for p in data:
        arr_dist = list(
            map(lambda x: 0 if (p == x).all() else np.linalg.norm(p - x), data)
        )
        arr_all_dist.append(arr_dist)
    arr_all_dist = np.asarray(arr_all_dist)
    return arr_all_dist

def generate_dists(dim=100, num_points=100):
    points = generate_synthetic_points(dim, num_points)
    dist_arr = build_dist_matrix(points)
    return dist_arr

distance_matrix = generate_dists(dim=10, num_points=50)

# Calculate delta hyperbolicity using the naive strategy on CPU
delta = hypdelta(distance_matrix, device="cpu", strategy="naive")
print(f"Delta hyperbolicity (naive, CPU): {delta}")

# Calculate delta hyperbolicity using the CCL strategy on GPU
delta = hypdelta(distance_matrix, device="gpu", strategy="CCL", l=0.1)
print(f"Delta hyperbolicity (CCL, GPU): {delta}")

The hypdelta function supports the following strategies:

• "naive": A straightforward approach to calculate delta hyperbolicity.
• "condensed": A strategy that uses condensed data representation.
• "heuristic": A heuristic-based approach for faster computation.
• "CCL": A strategy using far-away pairs for computation.
• "cartesian": A strategy that utilizes the cartesian product of pairs.

And the following devices:

• "cpu": Computation on the CPU.
• "gpu": Computation on the GPU.

• distance_matrix: The distance matrix for which delta hyperbolicity is to be computed.
• device: The device to use for computation, can be "cpu" or "gpu".
• strategy: The strategy to use for computation. Options are "naive", "condensed", "heuristic", "CCL", and "cartesian".
• l: A parameter for certain strategies like "CCL". Default is 0.05.
• tries: Number of tries for the "condensed" strategy. Default is 25.
• heuristic: Whether to use heuristic methods for the "condensed" strategy. Default is True.
• threadsperblock: The number of threads per block for GPU computation. Default is (16, 16, 4).
• max_threads: The maximum number of threads to use for GPU computation in the "cartesian" strategy. Default is 1024.
• max_gpu_mem : The maximum gpu memory in Gb. Used in "cartesian" strategy. Default is 16.

Contributions are welcome! Please fork the repository and submit a pull request for any enhancements or bug fixes.

This project is licensed under the MIT License. See the LICENSE file for details.

Feel free to explore the repository and experiment with different strategies and devices to find the optimal settings for your use case.