Type:	Package
Title:	'Rcpp' Implementation of 'FSelector' Entropy-Based Feature Selection Algorithms with a Sparse Matrix Support
Version:	0.3.13
Description:	'Rcpp' (free of 'Java'/'Weka') implementation of 'FSelector' entropy-based feature selection algorithms based on an MDL discretization (Fayyad U. M., Irani K. B.: Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. In 13'th International Joint Conference on Uncertainly in Artificial Intelligence (IJCAI93), pages 1022-1029, Chambery, France, 1993.) https://www.ijcai.org/Proceedings/93-2/Papers/022.pdf with a sparse matrix support.
Depends:	R (≥ 3.4)
License:	GPL-2
Imports:	Rcpp (≥ 0.12.12), foreach, iterators
LinkingTo:	Rcpp, BH, RcppArmadillo, testthat
Suggests:	Matrix, testthat, RcppArmadillo, dplyr, RWeka, entropy, FSelector, randomForest, doParallel, rpart, MASS, covr, parallel, htmltools, magrittr, knitr, RTCGA.rnaseq, ggplot2, microbenchmark, pbapply, tibble, rmarkdown, lintr, pkgdown, withr
RoxygenNote:	7.3.2
Encoding:	UTF-8
BugReports:	https://github.com/mi2-warsaw/FSelectorRcpp/issues
URL:	https://github.com/mi2-warsaw/FSelectorRcpp
VignetteBuilder:	knitr
NeedsCompilation:	yes
Packaged:	2024-10-01 10:18:27 UTC; zzawadz
Author:	Zygmunt Zawadzki [aut, cre], Marcin Kosinski [aut], Krzysztof Slomczynski [ctb], Damian Skrzypiec [ctb], Patrick Schratz [ctb]
Maintainer:	Zygmunt Zawadzki <zygmunt@zstat.pl>
Repository:	CRAN
Date/Publication:	2024-10-02 22:40:26 UTC

Direct Interface to Information Gain.

Description

Direct Interface to Information Gain.

Usage

.information_gain(
  x,
  y,
  type = c("infogain", "gainratio", "symuncert"),
  equal = FALSE,
  discIntegers = TRUE,
  nbins = 5,
  threads = 1
)

Arguments

Details

In principle using information_gain is safer.

data.frame with the following columns:

• attributes - variables names.

• importance - worth of the attributes.

Select Attributes by Score Depending on the Cutoff

Description

Select attributes by their score/rank/weights, depending on the cutoff that may be specified by the percentage of the highest ranked attributes or by the number of the highest ranked attributes.

Usage

cut_attrs(attrs, k = 0.5)

Arguments

Author(s)

Damian Skrzypiec damian.j.skrzypiec@gmail.com and Zygmunt Zawadzki zygmunt@zstat.pl

Examples

x <- information_gain(Species ~ ., iris)
cut_attrs(attrs = x)
to_formula(cut_attrs(attrs = x), "Species")
cut_attrs(attrs = x, k = 1)

Discretization

Description

Discretize a range of numeric attributes in the dataset into nominal attributes. Minimum Description Length (MDL) method is set as the default control. There is also available equalsizeControl method.

Usage

discretize(
  x,
  y,
  control = list(mdlControl(), equalsizeControl()),
  all = TRUE,
  discIntegers = TRUE,
  call = NULL
)

mdlControl()

equalsizeControl(k = 10)

customBreaksControl(breaks)

Arguments

Author(s)

Zygmunt Zawadzki zygmunt@zstat.pl

References

U. M. Fayyad and K. B. Irani. Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. In 13th International Joint Conference on Uncertainly in Artificial Intelligence(IJCAI93), pages 1022-1029, 1993.

Examples

# vectors
discretize(x = iris[[1]], y = iris[[5]])

# list and vector
head(discretize(x = list(iris[[1]], iris$Sepal.Width), y = iris$Species))

# formula input
head(discretize(x = Species ~ ., y = iris))
head(discretize(Species ~ ., iris))

# use different methods for specific columns
ir1 <- discretize(Species ~ Sepal.Length, iris)
ir2 <- discretize(Species ~ Sepal.Width, ir1, control = equalsizeControl(3))
ir3 <- discretize(Species ~ Petal.Length, ir2, control = equalsizeControl(5))
head(ir3)

# custom breaks
ir <- discretize(Species ~ Sepal.Length, iris,
  control = customBreaksControl(breaks = c(0, 2, 5, 7.5, 10)))
head(ir)

## Not run: 
# Same results
library(RWeka)
Rweka_disc_out <- RWeka::Discretize(Species ~ Sepal.Length, iris)[, 1]
FSelectorRcpp_disc_out <- FSelectorRcpp::discretize(Species ~ Sepal.Length,
                                                    iris)[, 1]
table(Rweka_disc_out, FSelectorRcpp_disc_out)
# But faster method
library(microbenchmark)
microbenchmark(FSelectorRcpp::discretize(Species ~ Sepal.Length, iris),
               RWeka::Discretize(Species ~ Sepal.Length, iris))


## End(Not run)

Transform a data.frame using split points returned by discretize function.

Description

Transform a data.frame using split points returned by discretize function.

Usage

discretize_transform(disc, data, dropColumns = NA)

extract_discretize_transformer(disc)

Arguments

Value

A new data.frame with discretized columns using cutpoints from the result of discretize function.

Examples

set.seed(123)
idx <- sort(sample.int(150, 100))
iris1 <- iris[idx, ]
iris2 <- iris[-idx, ]
disc <- discretize(Species ~ ., iris)
head(discretize_transform(disc, iris2))

# Chain discretization:
ir1 <- discretize(Species ~ Sepal.Length, iris1)
ir2 <- discretize(Species ~ Sepal.Width, ir1, control = equalsizeControl(3))
ir3 <- discretize(Species ~ Petal.Length, ir2, control = equalsizeControl(5))

## note that Petal.Width is untouched:
head(discretize_transform(ir3, iris2))

## extract_discretize_transformer
discObj <- extract_discretize_transformer(ir3)
head(discretize_transform(discObj, iris2))

General Feature Searching Engine

Description

A convenience wrapper for greedy and exhaustive feature selection algorithms that extract valuable attributes depending on the evaluation method (called evaluator). This function is a reimplementation of FSelector's exhaustive.search and greedy.search.

Usage

feature_search(
  attributes,
  fun,
  data,
  mode = c("greedy", "exhaustive"),
  type = c("forward", "backward"),
  sizes = 1:length(attributes),
  parallel = TRUE,
  ...
)

Arguments

Details

The evaluator function passed with fun is used to determine the importance score of current features' subset. The score is used in a multiple-way (backward or forward) greedy algorithm as a stopping moment or as a selection criterion in the exhaustive search that checks all possible attributes' subset combinations (of sizes passed in sizes).

Value

A list with following components

• best - a data.frame with the best subset and it's score (1 - feature used, 0 - feature not used),

• all - a data.frame with all checked features' subsets and their score (1 - feature used, 0 - feature not used),

• data - the data used in the feature selection,

• fun - the evaluator used to compute the score of importance for features' subsets,

• call - an origin call of the feature_search,

• mode - the mode used in the call.

Note

Note that score depends on the evaluator you provide in the fun parameter.

Author(s)

Zygmunt Zawadzki zygmunt@zstat.pl

Krzysztof Slomczynski krzysztofslomczynski@gmail.com

Examples

# Enable parallelization in examples
## Not run: 
 library(doParallel)
 cl <- makeCluster(2)
 registerDoParallel(cl)

## End(Not run)
# Close at the end
# stopCluster(cl) #nolint
# registerDoSEQ() #nolint

if(require("rpart")) {
# 1) Evaluator from FSelector package.
evaluator <- function(subset, data, dependent = names(iris)[5]) {
  library(rpart)
  k <- 5
  splits <- runif(nrow(data))
  results <- sapply(1:k, function(i) {
    test.idx <- (splits >= (i - 1) / k) & (splits < i / k)
    train.idx <- !test.idx
    test <- data[test.idx, , drop = FALSE]
    train <- data[train.idx, , drop = FALSE]
    tree <- rpart(to_formula(subset, dependent), train)
    error.rate <- sum(test[[dependent]] != predict(tree, test, type = "c")) /
    nrow(test)
    return(1 - error.rate)
  })
  return(mean(results))
}

set.seed(123)
# Default greedy search.
system.time(
  feature_search(attributes = names(iris)[-5],
                 fun = evaluator,
                 data = iris)
)
system.time(
  feature_search(attributes = names(iris)[-5],
                 fun = evaluator,
                 data = iris,
                 parallel = FALSE)
)

# Optional exhaustive search.
system.time(
  feature_search(attributes = names(iris)[-5],
                 fun = evaluator,
                 data = iris,
                 mode = "exhaustive")
)
system.time(
  feature_search(attributes = names(iris)[-5],
                 fun = evaluator,
                 data = iris,
                 mode = "exhaustive",
                 parallel = FALSE)
)
}

# 2) Maximize R^2 statistics in the linear regression model/problem.

evaluator_R2_lm <- function(attributes, data, dependent = names(iris)[1]) {
  summary(
    lm(to_formula(attributes, dependent), data = data)
  )$r.squared
}

feature_search(attributes = names(iris)[-1],
               fun = evaluator_R2_lm, data = iris,
               mode = "exhaustive")

# 3) Optimize BIC crietion in generalized linear model.
# Aim of Bayesian approach it to identify the model with the highest
# probability of being the true model. - Kuha 2004
if(require("MASS")) {
utils::data(anorexia, package = "MASS")

evaluator_BIC_glm <- function(attributes, data, dependent = "Postwt") {
  extractAIC(
    fit = glm(to_formula(attributes, dependent), family = gaussian,
              data = data),
    k = log(nrow(data))
  )[2]
}

feature_search(attributes = c("Prewt", "Treat", "offset(Prewt)"),
               fun = evaluator_BIC_glm,
               data = anorexia,
               mode = "exhaustive")
}
# Close parallelization
## Not run: 
stopCluster(cl)
registerDoSEQ()

## End(Not run)

Entropy-based Filters

Description

Algorithms that find ranks of importance of discrete attributes, basing on their entropy with a continous class attribute. This function is a reimplementation of FSelector's information.gain, gain.ratio and symmetrical.uncertainty.

Usage

information_gain(
  formula,
  data,
  x,
  y,
  type = c("infogain", "gainratio", "symuncert"),
  equal = FALSE,
  discIntegers = TRUE,
  nbins = 5,
  threads = 1
)

Arguments

Details

type = "infogain" is

H(Class) + H(Attribute) - H(Class, Attribute)

type = "gainratio" is

\frac{H(Class) + H(Attribute) - H(Class, Attribute)}{H(Attribute)}

type = "symuncert" is

2\frac{H(Class) + H(Attribute) - H(Class, Attribute)}{H(Attribute) + H(Class)}

where H(X) is Shannon's Entropy for a variable X and H(X, Y) is a joint Shannon's Entropy for a variable X with a condition to Y.

Value

data.frame with the following columns:

• attributes - variables names.

• importance - worth of the attributes.

Author(s)

Zygmunt Zawadzki zygmunt@zstat.pl

Examples

irisX <- iris[-5]
y <- iris$Species

## data.frame interface
information_gain(x = irisX, y = y)

# formula interface
information_gain(formula = Species ~ ., data = iris)
information_gain(formula = Species ~ ., data = iris, type = "gainratio")
information_gain(formula = Species ~ ., data = iris, type = "symuncert")

# sparse matrix interface
if(require("Matrix")) {
  library(Matrix)
  i <- c(1, 3:8); j <- c(2, 9, 6:10); x <- 7 * (1:7)
  x <- sparseMatrix(i, j, x = x)
  y <- c(1, 1, 1, 1, 2, 2, 2, 2)

  information_gain(x = x, y = y)
  information_gain(x = x, y = y, type = "gainratio")
  information_gain(x = x, y = y, type = "symuncert")
}

RReliefF filter

Description

The algorithm finds weights of continuous and discrete attributes basing on a distance between instances.

Usage

relief(formula, data, x, y, neighboursCount = 5, sampleSize = 10)

Arguments

Details

The function and it's manual page taken directly from FSelector: Piotr Romanski and Lars Kotthoff (2018). FSelector: Selecting Attributes. R package version 0.31. https://CRAN.R-project.org/package=FSelector

Value

a data.frame containing the worth of attributes in the first column and their names as row names

References

Igor Kononenko: Estimating Attributes: Analysis and Extensions of RELIEF. In: European Conference on Machine Learning, 171-182, 1994.

Marko Robnik-Sikonja, Igor Kononenko: An adaptation of Relief for attribute estimation in regression. In: Fourteenth International Conference on Machine Learning, 296-304, 1997.

Examples

data(iris)

weights <- relief(Species~., iris, neighboursCount = 5, sampleSize = 20)
print(weights)
subset <- cut_attrs(weights, 2)
f <- to_formula(subset, "Species")
print(f)

Create a formula Object

Description

Utility function to create a formula object. Note that it may be very useful when you use pipes.

Usage

to_formula(attrs, class)

Arguments

Examples

if(require("rpart")) {
# evaluator from FSelector package
evaluator <- function(subset, data, dependent = names(iris)[5]) {
  library(rpart)
  k <- 5
  splits <- runif(nrow(data))
  results <- sapply(1:k, function(i) {
    test.idx <- (splits >= (i - 1) / k) & (splits < i / k)
    train.idx <- !test.idx
    test <- data[test.idx, , drop = FALSE]
    train <- data[train.idx, , drop = FALSE]
    tree <- rpart(to_formula(subset, dependent), train)
    error.rate <- sum(test[[dependent]] != predict(tree, test, type = "c")) /
    nrow(test)
    return(1 - error.rate)
  })
  return(mean(results))
}

set.seed(123)
fit <- feature_search(attributes = names(iris)[-5], fun = evaluator, data = iris,
                mode = "exhaustive", parallel = FALSE)
fit$best
names(fit$best)[fit$best == 1]
# with to_formula
to_formula(names(fit$best)[fit$best == 1], "Species")
}