Commit ⋅ rivolli/mfe

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@@ -11,6 +11,9 @@

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#' @param features A list of features names or \code{"all"} to include all them.
#'  The supported values are described in the details section. (Default: 
#'  \code{"all"})
#' @param summary A list of summarization functions or empty for all values. See
#'  \link{post.processing} method to more information. (Default: 
#'  \code{c("mean", "sd")})
#' @param formula A formula to define the class column.
#' @param data A data.frame dataset contained the input attributes and class
#' @param ... Not used.

@@ -75,7 +78,8 @@

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#' @rdname complexity
#' @export
complexity.default <- function(x, y, features="all", ...) {
complexity.default <- function(x, y, features="all", 
                               summary=c("mean", "sd"), ...) {
  if(!is.data.frame(x)) {
    stop("data argument must be a data.frame")
  }

@@ -98,27 +102,31 @@

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  }
  
  if (any(features %in% ls.complexity.groups("class"))) {
    features <- unique(c(features, unlist(sapply(features, ls.complexity.groups))))
    features <- unique(c(features, unlist(sapply(features, 
                                                 ls.complexity.groups))))
  }
  features <- match.arg(features, ls.complexity(), TRUE)
  colnames(x) <- make.names(colnames(x), unique=TRUE)
  
  groups <- names(which(sapply(ls.complexity.groups("class"), 
                 function(x) any(features %in% ls.complexity.groups(x)))))
  groups <- gsub("linearity", "linearity.class", groups)

  unlist(
    lapply(groups, function(group) {
      fmethod <- get(group, asNamespace("ECoL"))
      measures <- intersect(features, ls.complexity.groups(group))
      do.call(fmethod, list(x=x, y=y, measures=measures, summary="return", ...))
    })
  )[features]
  do.call(c, lapply(groups, function(group) {
    fmethod <- get(group, asNamespace("ECoL"))
    measures <- intersect(features, ls.complexity.groups(group))
    subgroups <- do.call(fmethod, list(x=x, y=y, measures=measures, 
                                       summary="return", ...))
    sapply(names(subgroups), function(measure){
      post.processing(subgroups[[measure]], summary, 
                      measure %in% ls.complexity.multiples(), ...)
    }, simplify = FALSE)
  }))[features]
}

#' @rdname complexity
#' @export
complexity.formula <- function(formula, data, features="all", ...) {
complexity.formula <- function(formula, data, features="all", 
                               summary=c("mean", "sd"), ...) {
  if(!inherits(formula, "formula")) {
    stop("method is only for formula datas")
  }

@@ -130,7 +138,7 @@

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  modFrame <- stats::model.frame(formula, data)
  attr(modFrame, "terms") <- NULL
  
  complexity.default(modFrame[-1], modFrame[1], features, ...)
  complexity.default(modFrame[-1], modFrame[1], features, summary, ...)
}

#' List the complexity meta-features

@@ -145,15 +153,20 @@

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}

ls.complexity.multiples <- function() {
  c()
  c(c("F1", "F1v", "F2", "F3", "F4"), #overlapping
    c("N2", "N3", "N4", "T1"), #neighborhood
    c("L1", "L2", "L3"), #linearity
    c(), #dimensionality
    c(), #balance
    c("Hubs") #network
  )
}

ls.complexity.groups <- function(type) {
  
  switch(type,
         class = {
           c("overlapping", "neighborhood", 
             "linearity", "dimensionality",
           c("overlapping", "neighborhood", "linearity", "dimensionality",
             "balance", "network")
         }, regr = {
           c("correlation", "linearity", 

@@ -168,9 +181,6 @@

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168	181		linearity = {
169	182		c("L1", "L2", "L3")
170	183		},
171		-	linearity.class = {
172		-	c("L1", "L2", "L3")
173		-	},
174	184		dimensionality = {
175	185		c("T2", "T3", "T4")
176	186		},

Files		•	•	•	Coverage
R	733	660	0	73	90.04%
Project Totals (13 files)	733	660	0	73	90.04%

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98	102		}
99	103
100	104		if (any(features %in% ls.complexity.groups("class"))) {
101		-	features <- unique(c(features, unlist(sapply(features, ls.complexity.groups))))
	105	+	features <- unique(c(features, unlist(sapply(features,
	106	+	ls.complexity.groups))))
102	107		}
103	108		features <- match.arg(features, ls.complexity(), TRUE)
104	109		colnames(x) <- make.names(colnames(x), unique=TRUE)
105	110
106	111		groups <- names(which(sapply(ls.complexity.groups("class"),
107	112		function(x) any(features %in% ls.complexity.groups(x)))))
108		-	groups <- gsub("linearity", "linearity.class", groups)
109	113
110		-	unlist(
111		-	lapply(groups, function(group) {
112		-	fmethod <- get(group, asNamespace("ECoL"))
113		-	measures <- intersect(features, ls.complexity.groups(group))
114		-	do.call(fmethod, list(x=x, y=y, measures=measures, summary="return", ...))
115		-	})
116		-	)[features]
	114	+	do.call(c, lapply(groups, function(group) {
	115	+	fmethod <- get(group, asNamespace("ECoL"))
	116	+	measures <- intersect(features, ls.complexity.groups(group))
	117	+	subgroups <- do.call(fmethod, list(x=x, y=y, measures=measures,
	118	+	summary="return", ...))
	119	+	sapply(names(subgroups), function(measure){
	120	+	post.processing(subgroups[[measure]], summary,
	121	+	measure %in% ls.complexity.multiples(), ...)
	122	+	}, simplify = FALSE)
	123	+	}))[features]
117	124		}
118	125
119	126		#' @rdname complexity
120	127		#' @export
121		-	complexity.formula <- function(formula, data, features="all", ...) {
	128	+	complexity.formula <- function(formula, data, features="all",
	129	+	summary=c("mean", "sd"), ...) {
122	130		if(!inherits(formula, "formula")) {
123	131		stop("method is only for formula datas")
124	132		}

130	138		modFrame <- stats::model.frame(formula, data)
131	139		attr(modFrame, "terms") <- NULL
132	140
133		-	complexity.default(modFrame[-1], modFrame[1], features, ...)
	141	+	complexity.default(modFrame[-1], modFrame[1], features, summary, ...)
134	142		}
135	143
136	144		#' List the complexity meta-features

145	153		}
146	154
147	155		ls.complexity.multiples <- function() {
148		-	c()
	156	+	c(c("F1", "F1v", "F2", "F3", "F4"), #overlapping
	157	+	c("N2", "N3", "N4", "T1"), #neighborhood
	158	+	c("L1", "L2", "L3"), #linearity
	159	+	c(), #dimensionality
	160	+	c(), #balance
	161	+	c("Hubs") #network
	162	+	)
149	163		}
150	164
151	165		ls.complexity.groups <- function(type) {
152	166
153	167		switch(type,
154	168		class = {
155		-	c("overlapping", "neighborhood",
156		-	"linearity", "dimensionality",
	169	+	c("overlapping", "neighborhood", "linearity", "dimensionality",
157	170		"balance", "network")
158	171		}, regr = {
159	172		c("correlation", "linearity",

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11	11		#' @param features A list of features names or \code{"all"} to include all them.
12	12		#' The supported values are described in the details section. (Default:
13	13		#' \code{"all"})
	14	+	#' @param summary A list of summarization functions or empty for all values. See
	15	+	#' \link{post.processing} method to more information. (Default:
	16	+	#' \code{c("mean", "sd")})
14	17		#' @param formula A formula to define the class column.
15	18		#' @param data A data.frame dataset contained the input attributes and class
16	19		#' @param ... Not used.

75	78
76	79		#' @rdname complexity
77	80		#' @export
78		-	complexity.default <- function(x, y, features="all", ...) {
	81	+	complexity.default <- function(x, y, features="all",
	82	+	summary=c("mean", "sd"), ...) {
79	83		if(!is.data.frame(x)) {
80	84		stop("data argument must be a data.frame")
81	85		}