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#
# Description of this R script:
# R interface rotine for fitting a sparse group lasso regularization path
#
# Intended for use with R.
# Copyright (C) 2014 Martin Vincent
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>
#
#' @title Fit a Sparse Group Lasso Regularization Path.
#'
#' @description
#' Computes a sequence of minimizers (one for each lambda given in the \code{lambda} argument) of
#' \deqn{\mathrm{loss}(\beta) + \lambda \left( (1-\alpha) \sum_{J=1}^m \gamma_J \|\beta^{(J)}\|_2 + \alpha \sum_{i=1}^{n} \xi_i |\beta_i| \right)}
#' where \eqn{\mathrm{loss}} is the loss/objective function specified by \code{module_name}.
#' The parameters are organized in the parameter matrix \eqn{\beta} with dimension \eqn{q\times p}.
#' The vector \eqn{\beta^{(J)}} denotes the \eqn{J} parameter group.
#' The group weights \eqn{\gamma \in [0,\infty)^m} and the parameter weights \eqn{\xi = (\xi^{(1)},\dots, \xi^{(m)}) \in [0,\infty)^n}
#' with \eqn{\xi^{(1)}\in [0,\infty)^{n_1},\dots, \xi^{(m)} \in [0,\infty)^{n_m}}.
#'
#' @param module_name reference to objective specific C++ routines.
#' @param PACKAGE name of the calling package.
#' @param data a list of data objects -- will be parsed to the specified module.
#' @param parameterGrouping grouping of parameters, a vector of length \eqn{p}. Each element of the vector specifying the group of the parameters in the corresponding column of \eqn{\beta}.
#' @param groupWeights the group weights, a vector of length \code{length(unique(parameterGrouping))} (the number of groups).
#' @param parameterWeights a matrix of size \eqn{q \times p}.
#' @param alpha the \eqn{\alpha} value 0 for group lasso, 1 for lasso, between 0 and 1 gives a sparse group lasso penalty.
#' @param lambda lambda.min relative to lambda.max (if \code{compute_lambda = TRUE}) or the lambda sequence for the regularization path, a vector or a list of vectors (of the same length) with the lambda sequence for the subsamples.
#' @param d length of lambda sequence (ignored if \code{compute_lambda = FALSE})
#' @param compute_lambda should the lambda sequence be computed
#' @param return_indices the indices of lambda values for which to return fitted parameters.
#' @param algorithm.config the algorithm configuration to be used.
#' @return
#' \item{Y.true}{the response, that is the \code{y} object in data as created by \code{create.sgldata}.}
#' \item{beta}{the fitted parameters -- a list of length \code{length(return)} with each entry a matrix of size \eqn{q\times (p+1)} holding the fitted parameters.}
#' \item{loss}{the values of the loss function.}
#' \item{objective}{the values of the objective function (i.e. loss + penalty).}
#' \item{lambda}{the lambda values used.}
#' @author Martin Vincent
#' @export
#' @useDynLib sglOptim, .registration=TRUE
#' @importFrom utils packageVersion
#' @import Matrix
sgl_fit <- function(module_name, PACKAGE,
data,
parameterGrouping = NULL,
groupWeights = NULL,
parameterWeights = NULL,
alpha,
lambda,
d = 100,
compute_lambda = length(lambda) == 1,
return_indices = NULL,
algorithm.config = sgl.standard.config) {
# Compute lambda sequence
if( compute_lambda ) {
if( length(lambda) != 1 || lambda > 1 || lambda < 0) {
stop("lambda must be a single number in the range (0,1) ")
}
if( length(d) != 1 || as.integer(d) != d || d < 1) {
stop("d must be a single integer larger than 1")
}
lambda <- sgl_lambda_sequence(
module_name = module_name,
PACKAGE = PACKAGE,
data = data,
parameterGrouping = parameterGrouping,
groupWeights = groupWeights,
parameterWeights = parameterWeights,
alpha = alpha,
d = d,
lambda.min = lambda,
algorithm.config = algorithm.config,
lambda.min.rel = TRUE
)
}
# Prapare arguments
args <- prepare.args(
data = data,
parameterGrouping = parameterGrouping,
groupWeights = groupWeights,
parameterWeights = parameterWeights,
alpha = alpha
)
if( is.null(return_indices) ) {
return_indices <- 1:length(lambda)
}
idx <- as.integer(sort(unique(return_indices))) - 1L
if( any(idx < 0) || any(idx >= length(lambda)) ) {
stop("return_indices indvalid")
}
call_sym <- get(paste(module_name, "sgl_fit", "R", sep="_"),
asNamespace(PACKAGE))
res <- do.call(call_sym, args =
list(data = args$data,
block_dim = args$block_dim,
groupWeights = args$groupWeights,
parameterWeights = args$parameterWeights,
alpha = args$alpha,
lambda = lambda,
idx = idx,
algorithm.config = algorithm.config)
)
# Add true response
res$Y.true <- data$data$Y
## Create R sparse matrix
res$beta <- lapply(1:length(res$beta), function(i) sparseMatrix_from_C_format(res$beta[[i]]))
# Dim names
if( ! is.null(args$parameterNames) ) {
res$beta <- lapply(res$beta, function(x) { dimnames(x) <- args$parameterNames; x })
}
# Restore org order
res$beta <- lapply(res$beta, function(x) x[, order(args$group_order), drop = FALSE])
res$nmod <- length(res$beta)
# Set version, type and class and return
res$module_name <- module_name
res$PACKAGE <- PACKAGE
res$sglOptim_version <- packageVersion("sglOptim")
res$type <- "fit"
class(res) <- "sgl"
return(res)
}
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