fast-tmfg 0.0.6

The official package to compute the Triangulated Maximally Filtered Graph (TMFG).

Fast-TMFG

Fast_TMFG is an ultra-fast implementation of the Triangulated Maximally Fileterd Graph (TMFG). It is based on the work by G. P. Massara and is fully implemented by A. Briola.

The interface is fully scikit-learn compatible. Consequently, it has three main methods:

• fit(weights, output): Fits the model to the input matrix weights (e.g. a squared correlation matrix). This method computes the Triangulated Maximal Filtered Graph (TMFG) based on the input matrix. The output parameter specifies what is the nature of the desired output:
  • sparse inverse covariance matrix (output = 'logo')
  • sparse unweighted weights matrix (output = 'unweighted_sparse_W_matrix')
  • sparse weighted weights matrix (output = 'weighted_sparse_W_matrix')
• transform(): Returns the computed cliques and separators set of the model. The method also returns the TMFG adjacency matrix.
• fit_transform(weights, output): Fits the model to the input matrix weights (e.g. a squared correlation matrix) and returns the computed cliques and separators set and the TMFG adjacency matrix. The output parameter specifies what is the nature of the desired output:
  • sparse inverse covariance matrix (output = 'logo')
  • sparse unweighted weights matrix (output = 'unweighted_sparse_W_matrix')
  • sparse weighted weights matrix (output = 'weighted_sparse_W_matrix')

We provide a detailed explanation of each function/method. Such an explanation is entirely generated through ChatGPT.

For a fully understanding of the TMFG, we refer the interested reader to the follwing papers:

• Parsimonious modeling with information filtering networks
• Network filtering for big data: Triangulated maximally filtered graph
• Dependency structures in cryptocurrency market from high to low frequency

Installation

Install the latest version of the package using PyPI: pip3 install fast-tmfg

Usage Example

import numpy as np
import pandas as pd

from fast_tmfg import *

def generate_random_df(num_rows, num_columns):
  data = np.random.randint(0, 100, size=(num_rows, num_columns))
  df = pd.DataFrame(data, columns=['col_{}'.format(i) for i in range(num_columns)])
  return df

df = generate_random_df(100, 50)
corr = np.square(df.corr())
model = TMFG()
cliques, seps, adj_matrix = model.fit_transform(corr, output='unweighted_sparse_W_matrix')

How to cite us

If you use TMFG in a scientific publication, we would appreciate citations to the following paper:

@article{briola2022dependency,
  title={Dependency structures in cryptocurrency market from high to low frequency},
  author={Briola, Antonio and Aste, Tomaso},
  journal={arXiv preprint arXiv:2206.03386},
  year={2022}
}