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Estimating Copula Entropy and Transfer Entropy

Project description

PyPI version

copent

Estimating Copula Entropy and Transfer Entropy

Introduction

The nonparametric methods for estimating copula entropy and transfer entropy are implemented.

The method for estimating copula entropy composes of two simple steps: estimating empirical copula by rank statistic and estimating copula entropy with k-Nearest-Neighbour method. Copula Entropy is a mathematical concept for multivariate statistical independence measuring and testing, and proved to be equivalent to mutual information. Different from Pearson Correlation Coefficient, Copula Entropy is defined for non-linear, high-order and multivariate cases, which makes it universally applicable. Estimating copula entropy can be applied to many cases, including but not limited to variable selection [2] and causal discovery (by estimating transfer entropy) [3]. Please refer to Ma and Sun (2011) doi:10.1016/S1007-0214(11)70008-6 for more information. For more information in Chinese, please follow this link.

The nonparametric method for estimating transfer entropy composes of two steps: estimating three copula entropy and calculating transfer entropy from the estimated copula entropy. A function for conditional independence testing is also provided. Please refer to Ma (2019) (arXiv:1910.04375) for more information.

Functions

  • copent -- estimating copula entropy;

  • construct_empirical_copula -- the first step of the copent function, which estimates empirical copula for data by rank statistics;

  • entknn -- the second step of the copent function, which estimates copula entropy from empirical copula with kNN method;

  • ci -- conditional independence testing based on copula entropy

  • transent -- estimating transfer entropy via copula entropy

Installation

The package can be installed from PyPI directly:

pip install copent

The package can be installed from Github:

pip install git+https://github.com/majianthu/pycopent.git

Usage Example

estimating copula entropy
from numpy.random import multivariate_normal as mnorm
import copent
rho = 0.6
mean1 = [0,0]
cov1 = [ [1,rho],[rho,1] ]
x = mnorm(mean1,cov1,200) # bivariate gaussian 
ce1 = copent.copent(x) # estimated copula entropy
estimating transfer entropy
from copent import transent
from pandas import read_csv
import numpy as np
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/00381/PRSA_data_2010.1.1-2014.12.31.csv"
prsa2010 = read_csv(url)
# index: 5(PM2.5),6(Dew Point),7(Temperature),8(Pressure),10(Cumulative Wind Speed)
data = prsa2010.iloc[2200:2700,[5,8]].values
te = np.zeros(24)
for lag in range(1,25):
	te[lag-1] = transent(data[:,0],data[:,1],lag)
	str = "TE from pressure to PM2.5 at %d hours lag : %f" %(lag,te[lag-1])
	print(str)

References

  1. Ma Jian, Sun Zengqi. Mutual information is copula entropy. Tsinghua Science & Technology, 2011, 16(1): 51-54. See also arXiv preprint, arXiv:0808.0845, 2008.

  2. Ma Jian. Variable Selection with Copula Entropy. arXiv preprint arXiv:1910.12389, 2019.

  3. Ma Jian. Estimating Transfer Entropy via Copula Entropy. arXiv preprint arXiv:1910.04375, 2019.

Project details


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