hypdelta 0.1.1

A Python library for calculating delta hyperbolicity.

hypdelta

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.

Features

• 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.

Installation

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

git clone https://github.com/tnmtvv/hypdelta.git
cd hypdelta
pip install -r requirements.txt

Usage

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}")

Strategies and Devices

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.

Parameters

• 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.

Contributing

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

License

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.