| title | tags | authors | affiliations | date | bibliography | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Philentropy: Information Theory and Distance Quantification with R |
|
|
|
22 May 2018 |
paper.bib |
Comparison is a fundamental method of scientific research leading to insights about the processes that generate similarity or dissimilarity. In statistical terms comparisons between probability functions are performed to infer connections, correlations, or relationships between objects or samples [@Cha2007]. Most quantification methods rely on distance or similarity measures, but the right choice for each individual application is not always clear and sometimes poorly explored.
The reason for this is partly that diverse measures are either implemented in different R packages with very different notations or are not implemented at all. Thus, a comprehensive framework implementing the most common similarity and distance measures using a uniform notation is still missing.
The R [@R2018] package Philentropy aims to fill this gap by implementing forty-six fundamental distance and similarity measures [@Cha2007] for comparing probability functions. These comparisons between probability functions have their foundations in a broad range of scientific disciplines from mathematics to ecology. The aim of this package is to provide a comprehensive and computationally optimized base framework for clustering, classification, statistical inference, goodness-of-fit, non-parametric statistics, information theory, and machine learning tasks that are based on comparing univariate or multivariate probability functions. All functions are written in C++ and are integrated into the R package using the Rcpp Application Programming Interface (API) [@Eddelbuettel2013].
Together, this framework allows building new similarity or distance based (statistical) models and algorithms in R which are computationally efficient and scalable. The comprehensive availability of diverse metrics and measures furthermore enables a systematic assessment of choosing the most optimal similarity or distance measure for individual applications in diverse scientific disciplines.
The following probability distance/similarity and information theory measures are implemented in Philentropy.
- Euclidean :
$d = \sqrt{\sum_{i = 1}^N | P_i - Q_i |^2)}$ - Manhattan :
$d = \sum_{i = 1}^N | P_i - Q_i |$ - Minkowski :
$d = ( \sum_{i = 1}^N | P_i - Q_i |^p)^{1/p}$ - Chebyshev :
$d = max | P_i - Q_i |$
- Sorensen :
$d = \frac{\sum_{i = 1}^N | P_i - Q_i |}{\sum_{i = 1}^N (P_i + Q_i)}$ - Gower :
$d = \frac{1}{N} \dot \sum_{i = 1}^N | P_i - Q_i |$ , where$N$ is the total number of elements$i$ in$P_i$ and$Q_i$ - Soergel :
$d = \frac{\sum_{i = 1}^N | P_i - Q_i |}{\sum_{i = 1}^N max(P_i , Q_i)}$ - Kulczynski d :
$d = \frac{\sum_{i = 1}^N | P_i - Q_i |}{\sum_{i = 1}^N min(P_i , Q_i)}$ - Canberra :
$d = \frac{\sum_{i = 1}^N | P_i - Q_i |}{(P_i + Q_i)}$ - Lorentzian :
$d = \sum_{i = 1}^N ln(1 + | P_i - Q_i |)$
- Intersection :
$s = \sum_{i = 1}^N min(P_i , Q_i)$ - Non-Intersection :
$d = 1 - \sum_{i = 1}^N min(P_i , Q_i)$ - Wave Hedges :
$d = \frac{\sum_{i = 1}^N | P_i - Q_i |}{max(P_i , Q_i)}$ - Czekanowski :
$d = \frac{\sum_{i = 1}^N | P_i - Q_i |}{\sum_{i = 1}^N | P_i + Q_i |}$ - Motyka :
$d = \frac{\sum_{i = 1}^N min(P_i , Q_i)}{(P_i + Q_i)}$ - Kulczynski s :
$d = \frac{\sum_{i = 1}^N min(P_i , Q_i)}{\sum_{i = 1}^N | P_i - Q_i |}$ - Tanimoto :
$d = \frac{\sum_{i = 1}^N (max(P_i , Q_i) - min(P_i , Q_i))}{\sum_{i = 1}^N max(P_i , Q_i)}$ ; equivalent to Soergel - Ruzicka :
$s = \frac{\sum_{i = 1}^N min(P_i , Q_i)}{\sum_{i = 1}^N max(P_i , Q_i)}$ ; equivalent to 1 - Tanimoto = 1 - Soergel
- Inner Product :
$s = \sum_{i = 1}^N P_i \dot Q_i$ - Harmonic mean :
$s = 2 \cdot \frac{ \sum_{i = 1}^N P_i \cdot Q_i}{P_i + Q_i}$ - Cosine :
$s = \frac{\sum_{i = 1}^N P_i \cdot Q_i}{\sqrt{\sum_{i = 1}^N P_i^2} \cdot \sqrt{\sum_{i = 1}^N Q_i^2}}$ - Kumar-Hassebrook (PCE) :
$s = \frac{\sum_{i = 1}^N (P_i \cdot Q_i)}{(\sum_{i = 1}^N P_i^2 + \sum_{i = 1}^N Q_i^2 - \sum_{i = 1}^N (P_i \cdot Q_i))}$ - Jaccard :
$d = 1 - \frac{\sum_{i = 1}^N P_i \cdot Q_i}{\sum_{i = 1}^N P_i^2 + \sum_{i = 1}^N Q_i^2 - \sum_{i = 1}^N P_i \cdot Q_i}$ ; equivalent to 1 - Kumar-Hassebrook - Dice :
$d = \frac{\sum_{i = 1}^N (P_i - Q_i)^2}{(\sum_{i = 1}^N P_i^2 + \sum_{i = 1}^N Q_i^2)}$
- Fidelity :
$s = \sum_{i = 1}^N \sqrt{P_i \cdot Q_i}$ - Bhattacharyya :
$d = - ln \sum_{i = 1}^N \sqrt{P_i \cdot Q_i}$ - Hellinger :
$d = 2 \cdot \sqrt{1 - \sum_{i = 1}^N \sqrt{P_i \cdot Q_i}}$ - Matusita :
$d = \sqrt{2 - 2 \cdot \sum_{i = 1}^N \sqrt{P_i \cdot Q_i}}$ - Squared-chord :
$d = \sum_{i = 1}^N ( \sqrt{P_i} - \sqrt{Q_i} )^2$
- Squared Euclidean :
$d = \sum_{i = 1}^N ( P_i - Q_i )^2$ - Pearson
$X^2$ :$d = \sum_{i = 1}^N ( \frac{(P_i - Q_i )^2}{Q_i} )$ - Neyman
$X^2$ :$d = \sum_{i = 1}^N ( \frac{(P_i - Q_i )^2}{P_i} )$ - Squared
$X^2$ :$d = \sum_{i = 1}^N ( \frac{(P_i - Q_i )^2}{(P_i + Q_i)} )$ - Probabilistic Symmetric
$X^2$ :$d = 2 \cdot \sum_{i = 1}^N ( \frac{(P_i - Q_i )^2}{(P_i + Q_i)} )$ - Divergence :
$X^2$ :$d = 2 \cdot \sum_{i = 1}^N ( \frac{(P_i - Q_i )^2}{(P_i + Q_i)^2} )$ - Clark :
$d = \sqrt{\sum_{i = 1}^N (\frac{| P_i - Q_i |}{(P_i + Q_i)^2}}$ - Additive Symmetric
$X^2$ :$d = \sum_{i = 1}^N ( \frac{((P_i - Q_i)^2 \cdot (P_i + Q_i))}{(P_i \cdot Q_i)} )$
- Kullback-Leibler :
$d = \sum_{i = 1}^N P_i \cdot log(\frac{P_i}{Q_i})$ - Jeffreys :
$d = \sum_{i = 1}^N (P_i - Q_i) \cdot log(\frac{P_i}{Q_i})$ - K divergence :
$d = \sum_{i = 1}^N P_i \cdot log(\frac{2 \cdot P_i}{P_i + Q_i})$ - Topsoe :
$d = \sum_{i = 1}^N ( P_i \cdot log(\frac{2 \cdot P_i}{P_i + Q_i}) ) + ( Q_i \cdot log(\frac{2 \cdot Q_i}{P_i + Q_i}) )$ - Jensen-Shannon :
$d = 0.5 \cdot ( \sum_{i = 1}^N P_i \cdot log(\frac{2 \cdot P_i}{P_i + Q_i}) + \sum_{i = 1}^N Q_i \cdot log(\frac{2 * Q_i}{P_i + Q_i}))$ - Jensen difference :
$d = \sum_{i = 1}^N ( (\frac{P_i \cdot log(P_i) + Q_i \cdot log(Q_i)}{2}) - (\frac{P_i + Q_i}{2}) \cdot log(\frac{P_i + Q_i}{2}) )$
- Taneja :
$d = \sum_{i = 1}^N ( \frac{P_i + Q_i}{2}) \cdot log( \frac{P_i + Q_i}{( 2 \cdot \sqrt{P_i \cdot Q_i})} )$ - Kumar-Johnson :
$d = \sum_{i = 1}^N \frac{(P_i^2 - Q_i^2)^2}{2 \cdot (P_i \cdot Q_i)^{\frac{3}{2}}}$ - Avg(
$L_1$ ,$L_n$ ) :$d = \frac{\sum_{i = 1}^N | P_i - Q_i| + max{ | P_i - Q_i |}}{2}$
Note:
- Shannon's Entropy H(X) :
$H(X) = -\sum\limits_{i=1}^n P(x_i) \cdot log_b(P(x_i))$ - Shannon's Joint-Entropy H(X,Y) :
$H(X,Y) = -\sum\limits_{i=1}^n\sum\limits_{j=1}^m P(x_i, y_j) \cdot log_b(P(x_i, y_j))$ - Shannon's Conditional-Entropy H(X | Y) :
$H(Y|X) = \sum\limits_{i=1}^n\sum\limits_{j=1}^m P(x_i, y_j) \cdot log_b( \frac{P(x_i)}{P(x_i, y_j)})$ - Mutual Information I(X,Y) :
$MI(X,Y) = \sum\limits_{i=1}^n\sum\limits_{j=1}^m P(x_i, y_j) \cdot log_b( \frac{P(x_i, y_j)}{( P(x_i) * P(y_j) )})$ - Kullback-Leibler Divergence :
$KL(P || Q) = \sum\limits_{i=1}^n P(p_i) \cdot log_2(\frac{P(p_i) }{P(q_i)}) = H(P, Q) - H(P)$ - Jensen-Shannon Divergence :
$JSD(P || Q) = 0.5 * (KL(P || R) + KL(Q || R))$ - Generalized Jensen-Shannon Divergence :
$gJSD_{\pi_1,...,\pi_n}(P_1, ..., P_n) = H(\sum_{i = 1}^n \pi_i \cdot P_i) - \sum_{i = 1}^n \pi_i \cdot H(P_i)$
Philentropy already enabled the robust comparison of similarity measures in
analogy-based software effort estimation [@Phannachitta2017] as well as in evolutionary transcriptomics applications [@Drost2018]. The package aims to assist efforts
to determine optimal similarity or distance measures when developing new (statistical) models or algorithms. In addition, Philentropy is implemented to be applicable to large-scale datasets that were previously inaccessible using other R packages. The software is open source and currently available on GitHub (https://github.com/HajkD/philentropy) and CRAN (https://cran.r-project.org/web/packages/philentropy/index.html). A comprehensive documentation of Philentropy can be found at https://hajkd.github.io/philentropy/.
I would like to thank Jerzy Paszkowski for providing me an inspiring scientific environment and for supporting my projects.