miMatrix: Mutual information matrix

In praznik: Tools for Information-Based Feature Selection and Scoring

Mutual information matrix

Description

Calculates mutual information between each two features, that is

I(X_i,X_j).

Usage

miMatrix(X, zeroDiag = TRUE, threads = 0)

Arguments

X	Attribute table, given as a data frame with either factors (preferred), booleans, integers (treated as categorical) or reals (which undergo automatic categorisation; see below for details). Single vector will be interpreted as a data.frame with one column. NAs are not allowed.
zeroDiag	Boolean flag, whether the diagonal should be filled with zeroes, or with degenerated scores for two identical copies of a feature.
threads	Number of threads to use; default value, 0, means all available to OpenMP.

Value

A numerical matrix with scores, with row and column names copied from X.

Note

The method requires input to be discrete to use empirical estimators of distribution, and, consequently, information gain or entropy. To allow smoother user experience, praznik automatically coerces non-factor vectors in inputs, which requires additional time, memory and may yield confusing results – the best practice is to convert data to factors prior to feeding them in this function. Real attributes are cut into about 10 equally-spaced bins, following the heuristic often used in literature. Precise number of cuts depends on the number of objects; namely, it is n/3, but never less than 2 and never more than 10. Integers (which technically are also numeric) are treated as categorical variables (for compatibility with similar software), so in a very different way – one should be aware that an actually numeric attribute which happens to be an integer could be coerced into a n-level categorical, which would have a perfect mutual information score and would likely become a very disruptive false positive.

Examples

miMatrix(iris)

praznik documentation built on May 20, 2022, 5:06 p.m.