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#
# Description of this R script:
# R interface for multinomial sparse group lasso rutines.
#
# 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 Computes a lambda sequence for the regularization path
#'
#' @description
#' Computes a decreasing lambda sequence of length \code{d}.
#' The sequence ranges from a data determined maximal lambda \eqn{\lambda_\textrm{max}} to the user inputed \code{lambda.min}.
#'
#' @param x design matrix, matrix of size \eqn{N \times p}.
#' @param classes classes, factor of length \eqn{N}.
#' @param sampleWeights sample weights, a vector of length \eqn{N}.
#' @param grouping grouping of features, a vector of length \eqn{p}. Each element of the vector specifying the group of the covariate.
#' @param groupWeights the group weights, a vector of length \eqn{m+1} (the number of groups).
#' The first element of the vector is the intercept weight.
#' If \code{groupWeights = NULL} default weights will be used.
#' Default weights are 0 for the intercept and \deqn{\sqrt{K\cdot\textrm{number of features in the group}}} for all other weights.
#' @param parameterWeights a matrix of size \eqn{K \times (p+1)}.
#' The first column of the matrix is the intercept weights.
#' Default weights are is 0 for the intercept weights and 1 for all other weights.
#' @param alpha the \eqn{\alpha} value 0 for group lasso, 1 for lasso, between 0 and 1 gives a sparse group lasso penalty.
#' @param d the length of lambda sequence
#' @param standardize if TRUE the features are standardize before fitting the model. The model parameters are returned in the original scale.
#' @param lambda.min the smallest lambda value in the computed sequence.
#' @param intercept should the model include intercept parameters
#' @param sparse.data if TRUE \code{x} will be treated as sparse, if \code{x} is a sparse matrix it will be treated as sparse by default.
#' @param lambda.min.rel is lambda.min relative to lambda.max ? (i.e. actual lambda min used is \code{lambda.min*lambda.max}, with \code{lambda.max} the computed maximal lambda value)
#' @param algorithm.config the algorithm configuration to be used.
#' @return a vector of length \code{d} containing the computed lambda sequence.
#' @examples
#' data(SimData)
#'
#' # A quick look at the data
#' dim(x)
#' table(classes)
#'
#' lambda <- msgl::lambda(x, classes, alpha = .5, d = 100, lambda.min = 0.01)
#' @author Martin Vincent
#' @importFrom methods is
#' @importFrom sglOptim sgl_lambda_sequence
#' @importFrom sglOptim transpose_response_elements
#' @export
lambda <- function(
x,
classes,
sampleWeights = NULL,
grouping = NULL,
groupWeights = NULL,
parameterWeights = NULL,
alpha = 0.5,
d = 100L,
standardize = TRUE,
lambda.min,
intercept = TRUE,
sparse.data = is(x, "sparseMatrix"),
lambda.min.rel = FALSE,
algorithm.config = msgl.standard.config) {
setup <- .process_args(
x = x,
classes = classes,
weights = sampleWeights,
intercept = intercept,
grouping = grouping,
groupWeights = groupWeights,
parameterWeights = parameterWeights,
standardize = standardize,
sparse.data = sparse.data
)
data <- setup$data
lambda <- sgl_lambda_sequence(
module_name = setup$callsym,
PACKAGE = "msgl",
data = data,
parameterGrouping = setup$grouping,
groupWeights = setup$groupWeights,
parameterWeights = setup$parameterWeights,
alpha = alpha,
d = d,
lambda.min = lambda.min,
algorithm.config = algorithm.config,
lambda.min.rel = lambda.min.rel
)
return(lambda)
}
#' C interface
#'
#' @keywords internal
#' @export
msgl_dense_sgl_lambda_R <- function(
data,
block_dim,
groupWeights,
parameterWeights,
alpha,
d,
lambda.min,
lambda.min.rel,
algorithm.config) {
.Call(msgl_dense_sgl_lambda, PACKAGE = "msgl",
data,
block_dim,
groupWeights,
parameterWeights,
alpha,
d,
lambda.min,
lambda.min.rel,
algorithm.config
)
}
#' C interface
#'
#' @keywords internal
#' @export
msgl_sparse_sgl_lambda_R <- function(
data,
block_dim,
groupWeights,
parameterWeights,
alpha,
d,
lambda.min,
lambda.min.rel,
algorithm.config) {
.Call(msgl_sparse_sgl_lambda, PACKAGE = "msgl",
data,
block_dim,
groupWeights,
parameterWeights,
alpha,
d,
lambda.min,
lambda.min.rel,
algorithm.config
)
}
#' Deprecated lambda function
#'
#' @keywords internal
#' @export
msgl.lambda.seq <- function(
x,
classes,
sampleWeights = NULL,
grouping = NULL,
groupWeights = NULL,
parameterWeights = NULL,
alpha = 0.5,
d = 100L,
standardize = TRUE,
lambda.min,
intercept = TRUE,
sparse.data = is(x, "sparseMatrix"),
lambda.min.rel = FALSE,
algorithm.config = msgl.standard.config) {
warning("msgl.lambda.seq is deprecated, use msgl::lambda")
msgl::lambda(
x,
classes,
sampleWeights,
grouping,
groupWeights,
parameterWeights,
alpha,
d,
standardize,
lambda.min,
intercept,
sparse.data,
lambda.min.rel,
algorithm.config
)
}
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