thoi 0.1.14

Torch - Higher Order Interactions

THOI: Torch - Higher Order Interactions

Description

THOI is a Python package designed to compute O information in Higher Order Interactions using batch processing. This package leverages PyTorch for efficient tensor operations.

Installation

Prerequisites

Ensure you have Python 3.6 or higher installed.

Installing THOI with your prefered Versions of PyTorch

Because PyTorch installation can depend on the user environment and requirements (GPU or CPU support or a specific version of PyTorch), you need to install PyTorch separately before installing THOI. Follow these steps:

1. Visit the official PyTorch installation guide:

   • Go to the PyTorch website and navigate to the "Get Started" page.
   • Select your preferences for the following options:
     • PyTorch Build: Stable or LTS (long-term support)
     • Your Operating System: Linux, Mac, or Windows
     • Package: Pip (recommended)
     • Language: Python
     • Compute Platform: CPU, CUDA 10.2, CUDA 11.1, etc.

2. Get the Installation Command:

   • Based on your selections, the PyTorch website will provide the appropriate installation command.

   • For example, for the CPU-only version, the command will look like this:

     pip install torch==1.8.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
     

   • For the GPU version with CUDA 11.1, the command will look like this:

     pip install torch==1.8.1+cu111 -f https://download.pytorch.org/whl/torch_stable.html
     

3. Install PyTorch:

   • Copy and run the command provided by the PyTorch website in your terminal.

4. Install THOI:

   • Once PyTorch is installed, install THOI using:

     pip install thoi
     

Usage

After installation, you can start using THOI in your projects. Here is a simple example:

from thoi.measures.gaussian_copula import multi_order_measures, nplets_measures
from thoi.heuristics import simulated_annealing, greedy
import numpy as np

X = np.random.normal(0,1, (1000, 10))

# Computation of O information for the entire system
measures = nplets_measures(X)

# Computation of O info for the sub-system composed by 0, 1 and 3
measures = nplets_measures(X, [0,1,3])

# Computation of O info for the sub-system composed by 0, 1 and 3
measures = nplets_measures(X, [[0,1,3],[3,7,4],[2,6,3]])

# Extensive computation of O information measures over all combinations of X
measures = multi_order_measures(X)

# compute the best 10 combinations using greedy, starting by exaustive search in 
# lower order and building from there. Result shows best O information for 
# each built optimal orders
best_partitions, best_scores = greedy(X, 3, 5, repeat=10)

# compute the best 10 combinations using simulated annealing: There are two initialization options
# 1. Starting by exaustive search in lower order, then building with gready.
# 2. Selection random sample of initial solutions.
# Result shows best O information for each built optimal orders
best_partitions, best_scores = simulated_annealing(X, 5, repeat=10)

For detailed usage and examples, please refer to the documentation.

Contributing

We welcome contributions from the community. If you encounter any issues or have suggestions for improvements, please open an issue or submit a pull request on GitHub.

License

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

Authors

• Laouen Mayal Louan Belloli
• Ruben Herzog

For more details, visit the GitHub repository.