#' @rdname FLXMCL
# @aliases FLXMCLsvm-class
#'
#' @family mixtures svm
#'
#' @import flexmix
#' @export
setClass("FLXMCLsvm", contains = "FLXMCL")
#' This is a model driver for \code{\link[flexmix]{flexmix}} implementing mixtures of Support Vector Machines for classification.
#'
#' @title Mixtures of Support Vector Machines
#' @param formula A formula which is interpreted relative to the formula specified in the call to \code{\link[flexmix]{flexmix}}
#' using \code{\link[stats]{update.formula}}.
#' Only the left-hand side (response) of the formula is used. Default is to use the original \code{\link[flexmix]{flexmix}} model formula.
#' @param \dots Further arguments to and from other methods, especially to \code{\link{wsvm}}.
#'
#' @return Returns an object of class \code{FLXMCLsvm} inheriting from \code{FLXMCL}.
#'
#' @rdname FLXMCLsvm
# @aliases FLXMCLsvm
#'
#' @family mixtures svm
#'
#' @import flexmix
#' @export
#'
#' @examples
#' library(benchData)
#' data <- flashData(1000)
#' data$x <- scale(data$x)
#' grid <- expand.grid(x.1 = seq(-6,6,0.2), x.2 = seq(-4,4,0.2))
#'
#' cluster <- kmeans(data$x, center = 2)$cluster
#' model <- FLXMCLsvm(kernel = "linear", fitted = FALSE)
#' fit <- flexmix(y ~ ., data = as.data.frame(data), concomitant = FLXPmultinom(~ x.1 + x.2), model = model, cluster = cluster)
#'
#' ## prediction for single component models without aggregation
#' pred.grid <- predict(fit, newdata = grid)
#' image(seq(-6,6,0.2), seq(-4,4,0.2), matrix(pred.grid[[1]][,1], length(seq(-6,6,0.2))))
#' contour(seq(-6,6,0.2), seq(-4,4,0.2), matrix(pred.grid[[1]][,1], length(seq(-6,6,0.2))), add = TRUE)
#' points(data$x, pch = as.character(data$y))
#'
#' image(seq(-6,6,0.2), seq(-4,4,0.2), matrix(pred.grid[[2]][,1], length(seq(-6,6,0.2))))
#' contour(seq(-6,6,0.2), seq(-4,4,0.2), matrix(pred.grid[[2]][,1], length(seq(-6,6,0.2))), add = TRUE)
#' points(data$x, pch = as.character(data$y))
#'
#' ## prediction with aggregation depending on membership in mixture components
#' pred.grid <- mypredict(fit, newdata = grid, aggregate = TRUE)
#' image(seq(-6,6,0.2), seq(-4,4,0.2), matrix(pred.grid[[1]][,1], length(seq(-6,6,0.2))))
#' contour(seq(-6,6,0.2), seq(-4,4,0.2), matrix(pred.grid[[1]][,1], length(seq(-6,6,0.2))), add = TRUE)
#' points(data$x, pch = as.character(data$y))
#'
#' ## local membership
#' loc.grid <- prior(fit, newdata = grid)
#' contour(seq(-6,6,0.2), seq(-4,4,0.2), matrix(loc.grid[,1], length(seq(-6,6,0.2))), add = TRUE)
# library(mlr)
# task <- makeClassifTask(data = as.data.frame(data), target = "y")
# lrn <- makeLearner("classif.FLXMCLsvm", kernel = "linear", centers = 2)
# tr <- train(lrn, task = task)
# pr <- predict(tr, newdata = grid)
## report
# getMethod("determinePrior", signature = c("ANY","FLXPmultinom"))
# function (prior, concomitant, group)
# {
# exps <- exp(concomitant@x %*% concomitant@coef)
# exps/rowSums(exps)
# }
# <environment: namespace:flexmix>
# exps kann Inf enthalten, wenn argument groß
# ergebnis ist NaN
FLXMCLsvm <- function(formula = . ~ ., ...) {
z <- new("FLXMCLsvm", weighted = TRUE, formula = formula,
name = "Mixture of SVM models")
z@defineComponent <- expression({
predict <- function(x) {
## returns class membership values (exp(-hinge loss)), these are not scaled and need not sum to unity
nl <- length(fit$labels) # number of present classes
lev <- fit$levels
ng <- length(lev) # number of classes
naidx <- apply(x, 1, function(z) any(is.na(z))) # obs with missing values
if (nl == 1) { # no decision values are returned
posterior <- matrix(0, nrow(x), ng)
rownames(posterior) <- rownames(x)
colnames(posterior) <- lev
posterior[,fit$labels] <- 1 # since the only present class is predicted, set posterior to largest possible value
} else {
decs <- attr(getS3method("predict", "wsvm")(fit, newdata = x, decision.values = TRUE, ...), "decision.values")
problems <- cbind(rep(fit$labels, nl:1-1), unlist(sapply(2:nl, function(x) fit$labels[x:nl]))) # binary classification problems
classidx <- lapply(fit$labels, function(k) problems == k) # binary problems where class k is involved
y <- matrix(sapply(classidx, function(x) colSums(t(x)*c(1,-1))), ncol = nl) # encoding of class k in individual binary problems
classidx <- matrix(sapply(classidx, rowSums), ncol = nl)
mode(classidx) <- "logical"
colnames(classidx) <- colnames(y) <- fit$labels
posterior <- matrix(0, nrow(x), ng) # unscaled posteriors
rownames(posterior) <- rownames(x)
colnames(posterior) <- lev
post <- sapply(as.character(fit$labels), function(z) {
H <- 1 - t(y[classidx[,z],z] * t(decs[,classidx[,z], drop = FALSE]))
H[H < 0] <- 0 # hinge loss
return(exp(-rowSums(H)))
})
posterior[!naidx,fit$labels] <- post
}
posterior[naidx,] <- NA
return(posterior)
}
logLik <- function(x, y) {
# fit <- wsvm(Species ~ ., data = iris[1:100,])
# y <- iris$Species[1:100]
# l <- fit$decision.values
# library(mlbench)
# data(Glass)
# fit <- wsvm(Type ~ ., data = Glass)
# y <- Glass$Type
# l <- fit$decision.values
#lev <- fit$levels
#ng <- length(lev)
y <- factor(y, levels = attr(y, "lev"))
nl <- length(fit$labels) # number of present classes
if (nl == 1) {
lpost <- rep(0, length(y))
reg <- 0
} else {
l <- attr(getS3method("predict", "wsvm")(fit, newdata = x, decision.values = TRUE, ...), "decision.values")
# print(summary(l))
ng <- length(fit$levels) # number of classes
problems <- cbind(rep(fit$labels, nl:1-1), unlist(sapply(2:nl, function(x) fit$labels[x:nl]))) # labels involved in particular binary problems
npr <- nl*(nl-1)/2 # number of binary classification problems
m <- matrix(NA, npr, ng)
m[cbind(1:npr,problems[,1])] <- 1
m[cbind(1:npr,problems[,2])] <- -1 # y coding matrix
yind <- t(m[, as.numeric(y), drop = FALSE]) # -1/1 class indicators for binary problems, n x npr matrix
lpost <- 1 - yind * l
lpost[lpost < 0] <- 0 # Hinge loss max(1 - yind*l, 0), n x npr matrix
lpost <- rowSums(-lpost, na.rm = TRUE) # sum of negative Hinge loss over all binary problems = log posterior, n x 1 matrix
#print(lpost)
# print(str(lpost))
# plot(yind,l)
# print(cbind(y,yind,l)[sample(nrow(x), size = 30),])
co <- t(yind) * 0
co[,fit$index][!is.na(co[,fit$index])] <- t(fit$coefs) # coefficients: alpha_n * y_n, npr x n matrix
lambda <- 1/(2 * fit$cost) # regularization parameter
reg <- lambda * t(co * (t(l) - fit$rho)) # n x npr matrix
# -fit$rho is correct, because obj is -rowSums(abs(co), na.rm = TRUE) + 0.5 * colSums(t(co) * t((t(l) - fit$rho)), na.rm = TRUE)
reg <- sum(-reg, na.rm = TRUE) # sum of regularization terms over all binary problems
}
# cat("sum alpha\n")
# print(rowSums(abs(co), na.rm = TRUE))
# cat("regularization\n")
# print(reg)
# cat("negative hinge loss\n")
# print(sum(fit$case.weight * lpost, na.rm = TRUE))
# cat("loglik\n")
# print(sum(fit$case.weight * lpost, na.rm = TRUE) + reg)
# cat("obj\n")
# print(sum(fit$obj))
# print(-rowSums(abs(co), na.rm = TRUE) + 0.5 * colSums(t(co) * t((t(l) - fit$rho)), na.rm = TRUE))
return(list(lpost = lpost, reg = reg))
}
new("FLXcomponent", parameters = list(coefs = fit$coefs, kernel = fit$kernel, cost = fit$cost,
degree = fit$degree, coef0 = fit$coef0, gamma = fit$gamma, fitted = !is.null(fit$fitted)), logLik = logLik, predict = predict, df = fit$df)
})
z@preproc.y <- function(y) {
if (!is.factor(y))
warning("'grouping' was coerced to a factor")
g <- as.factor(y)
lev <- levels(g)
g <- as.matrix(g)
attr(g, "lev") <- lev
return(g)
# if (is.factor(y)) {
# lev <- levels(y)
# y <- as.matrix(y)
# attr(y, "lev") <- lev
# return(y)
# } else
# return(as.matrix(y))
}
z@fit <- function(x, y, w) {
lev <- attr(y, "lev")
# if (is.null(lev))
# fit <- wsvm(x, as.vector(y), case.weights = w, ...)
# else
#print(head(w,30))
fit <- wsvm(x, factor(y, levels = lev), case.weights = w, ...)
fit$df <- sum(fit$nSV)
with(fit, eval(z@defineComponent))
}
z
}
#' @rdname FLXMCLsvm
# @aliases FLXgetModelmatrix,FLXMCLsvm-method
#'
#' @family mixtures svm
#'
#' @import flexmix
#' @export
setMethod("FLXgetModelmatrix", signature(model = "FLXMCLsvm"),
function (model, data, formula, lhs = TRUE, ...) {
formula <- flexmix:::RemoveGrouping(formula)
if (length(grep("\\|", deparse(model@formula))))
stop("no grouping variable allowed in the model")
if (is.null(model@formula))
model@formula = formula
model@fullformula = update(terms(formula, data = data),
model@formula)
if (lhs) {
mf <- if (is.null(model@terms))
model.frame(model@fullformula, data = data, na.action = NULL)
else model.frame(model@terms, data = data, na.action = NULL)
model@terms <- attr(mf, "terms")
modely <- model.response(mf)
model@y <- model@preproc.y(modely)
}
else {
mt1 <- if (is.null(model@terms))
terms(model@fullformula, data = data)
else model@terms
mf <- model.frame(delete.response(mt1), data = data,
na.action = NULL)
model@terms <- attr(mf, "terms")
}
attr(model@terms, "intercept") <- 0 ## intercept removed
X <- model.matrix(model@terms, data = mf)
model@contrasts <- attr(X, "contrasts")
model@x <- X
model@x <- model@preproc.x(model@x)
model@xlevels <- .getXlevels(model@terms, mf)
model
})
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