balltree-erwin 0.1.0

Fast parallel ball tree construction for machine learning

Ball Tree

A fast, parallel ball tree implementation with PyTorch integration, built using Cython and OpenMP.

Features

• High Performance: Optimized C++ implementation with OpenMP parallelization
• PyTorch Integration: Native support for PyTorch tensors
• Flexible API: Multiple interfaces for different use cases
• Memory Efficient: Efficient memory usage and management

Installation

From PyPI

pip install balltree-erwin

From Source

git clone https://github.com/maxxxzdn/balltree.git
cd balltree
pip install -e .

Requirements

• Python >= 3.8
• NumPy
• PyTorcn
• Cython >= 0.29.0 (for building from source)

System Dependencies

macOS

brew install libomp

Quick Start

import torch
import balltree

# Create some sample data
data = torch.randn(1000, 3)  # 1000 points in 3D
batch_idx = torch.zeros(1000, dtype=torch.long)  # Single batch

# Build a ball tree
tree_indices, tree_mask = balltree.build_balltree(data, batch_idx)

# Build with rotations for cross-ball interactions
tree_idx, tree_mask, rot_indices = balltree.build_balltree_with_rotations(
    data, batch_idx, 
    strides=[2, 2], 
    ball_sizes=[64, 32, 16],
    angle=45.0
)

API Reference

Core Functions

build_balltree(data, batch_idx)

Build ball trees for batched data.

Parameters:

• data: torch.Tensor of shape (num_samples, num_features)
• batch_idx: torch.Tensor of shape (num_samples,) - batch assignment

Returns:

• tree_indices: Indices of tree nodes
• tree_mask: Boolean mask for tree structure

partition_balltree(data, batch_idx, target_level)

Partition ball trees to a specific level.

Parameters:

• data: torch.Tensor of shape (num_samples, num_features)
• batch_idx: torch.Tensor of shape (num_samples,)
• target_level: int - target partitioning level

Returns:

• partitioned_indices: Partitioned tree indices

build_balltree_with_rotations(data, batch_idx, strides, ball_sizes, angle=45.0)

Build ball trees with rotational variants for enhanced modeling.

Parameters:

• data: torch.Tensor of shape (num_samples, num_features)
• batch_idx: torch.Tensor of shape (num_samples,)
• strides: list of int - pooling strides
• ball_sizes: list of int - ball sizes for each layer
• angle: float - rotation angle in degrees

Returns:

• tree_idx: Original tree indices
• tree_mask: Tree structure mask
• rot_tree_indices: List of rotated tree indices

Use Cases

This library is designed for applications requiring efficient spatial data structures:

• Machine Learning: Spatial attention mechanisms, geometric deep learning
• Computer Graphics: Collision detection, ray tracing acceleration
• Robotics: Path planning, nearest neighbor queries
• Scientific Computing: N-body simulations, clustering algorithms

License

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