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+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/gensvm.grid.R
+\name{gensvm.grid}
+\alias{gensvm.grid}
+\title{Cross-validated grid search for GenSVM}
+\usage{
+gensvm.grid(X, y, param.grid = "tiny", refit = TRUE, scoring = NULL,
+  cv = 3, verbose = 0, return.train.score = TRUE)
+}
+\arguments{
+\item{X}{training data matrix. We denote the size of this matrix by 
+n_samples x n_features.}
+
+\item{y}{training vector of class labes of length n_samples. The number of 
+unique labels in this vector is denoted by n_classes.}
+
+\item{param.grid}{String (\code{'tiny'}, \code{'small'}, or \code{'full'}) 
+or data frame with parameter configurations to evaluate.  Typically this is 
+the output of \code{expand.grid}. For more details, see "Using a Parameter 
+Grid" below.}
+
+\item{refit}{boolean variable. If true, the best model from cross validation 
+is fitted again on the entire dataset.}
+
+\item{scoring}{metric to use to evaluate the classifier performance during 
+cross validation. The metric should be an R function that takes two 
+arguments: y_true and y_pred and that returns a float such that higher 
+values are better. If it is NULL, the accuracy score will be used.}
+
+\item{cv}{the number of cross-validation folds to use or a vector with the 
+same length as \code{y} where each unique value denotes a test split.}
+
+\item{verbose}{integer to indicate the level of verbosity (higher is more 
+verbose)}
+
+\item{return.train.score}{whether or not to return the scores on the 
+training splits}
+}
+\value{
+A "gensvm.grid" S3 object with the following items:
+\item{call}{Call that produced this object}
+\item{param.grid}{Sorted version of the parameter grid used in training}
+\item{cv.results}{A data frame with the cross validation results}
+\item{best.estimator}{If refit=TRUE, this is the GenSVM model fitted with 
+the best hyperparameter configuration, otherwise it is NULL}
+\item{best.score}{Mean cross-validated test score for the model with the 
+best hyperparameter configuration}
+\item{best.params}{Parameter configuration that provided the highest mean 
+cross-validated test score}
+\item{best.index}{Row index of the cv.results data frame that corresponds to 
+the best hyperparameter configuration}
+\item{n.splits}{The number of cross-validation splits}
+\item{n.objects}{The number of instances in the data}
+\item{n.features}{The number of features of the data}
+\item{n.classes}{The number of classes in the data}
+\item{classes}{Array with the unique classes in the data}
+\item{total.time}{Training time for the grid search}
+\item{cv.idx}{Array with cross validation indices used to split the data}
+}
+\description{
+This function performs a cross-validated grid search of the 
+model parameters to find the best hyperparameter configuration for a given 
+dataset. This function takes advantage of GenSVM's ability to use warm 
+starts to speed up computation. The function uses the GenSVM C library for 
+speed.
+}
+\note{
+This function returns partial results when the computation is interrupted by 
+the user.
+}
+\section{Using a Parameter Grid}{
+
+To evaluate certain paramater configurations, a data frame can be supplied 
+to the \code{param.grid} argument of the function. Such a data frame can 
+easily be generated using the R function \code{expand.grid}, or could be 
+created through other ways to test specific parameter configurations.
+
+Three parameter grids are predefined:
+\describe{
+\item{\code{'tiny'}}{This parameter grid is generated by the function 
+\code{\link{gensvm.load.tiny.grid}} and is the default parameter grid. It 
+consists of parameter configurations that are likely to perform well on 
+various datasets.}
+\item{\code{'small'}}{This grid is generated by 
+\code{\link{gensvm.load.small.grid}} and generates a data frame with 90 
+configurations. It is typically fast to train but contains some 
+configurations that are unlikely to perform well. It is included for 
+educational purposes.}
+\item{\code{'full'}}{This grid loads the parameter grid as used in the 
+GenSVM paper. It consists of 342 configurations and is generated by the 
+\code{\link{gensvm.load.full.grid}} function. Note that in the GenSVM paper 
+cross validation was done with this parameter grid, but the final training 
+step used \code{epsilon=1e-8}. The \code{\link{gensvm.refit}} function is 
+useful in this scenario.}
+}
+
+When you provide your own parameter grid, beware that only certain column 
+names are allowed in the data frame corresponding to parameters for the 
+GenSVM model. These names are:
+
+\describe{
+\item{p}{Parameter for the lp norm. Must be in [1.0, 2.0].}
+\item{kappa}{Parameter for the Huber hinge function. Must be larger than 
+-1.}
+\item{lambda}{Parameter for the regularization term. Must be larger than 0.}
+\item{weight}{Instance weight specification. Allowed values are "unit" for 
+unit weights and "group" for group-size correction weights}
+\item{epsilon}{Stopping parameter for the algorithm. Must be larger than 0.}
+\item{max.iter}{Maximum number of iterations of the algorithm. Must be 
+larger than 0.}
+\item{kernel}{The kernel to used, allowed values are "linear", "poly", 
+"rbf", and "sigmoid". The default is "linear"}
+\item{coef}{Parameter for the "poly" and "sigmoid" kernels. See the section 
+"Kernels in GenSVM" in the code{ink{gensvm-package}} page for more info.}
+\item{degree}{Parameter for the "poly" kernel. See the section "Kernels in 
+GenSVM" in the code{ink{gensvm-package}} page for more info.}
+\item{gamma}{Parameter for the "poly", "rbf", and "sigmoid" kernels. See the 
+section "Kernels in GenSVM" in the code{ink{gensvm-package}} page for more 
+info.}
+}
+
+For variables that are not present in the \code{param.grid} data frame the 
+default parameter values in the \code{\link{gensvm}} function will be used.
+
+Note that this function reorders the parameter grid to make the warm starts 
+as efficient as possible, which is why the param.grid in the result will not 
+be the same as the param.grid in the input.
+}
+\examples{
+x <- iris[, -5]
+y <- iris[, 5]
+
+# use the default parameter grid
+grid <- gensvm.grid(x, y)
+
+# use a smaller parameter grid
+pg <- expand.grid(p=c(1.0, 1.5, 2.0), kappa=c(-0.9, 1.0), epsilon=c(1e-3))
+grid <- gensvm.grid(x, y, param.grid=pg)
+
+# print the result
+print(grid)
+
+# Using a custom scoring function (accuracy as percentage)
+acc.pct <- function(yt, yp) { return (100 * sum(yt == yp) / length(yt)) }
+grid <- gensvm.grid(x, y, scoring=acc.pct)
+
+}
+\author{
+Gerrit J.J. van den Burg, Patrick J.F. Groenen \cr
+Maintainer: Gerrit J.J. van den Burg <gertjanvandenburg@gmail.com>
+}
+\references{
+Van den Burg, G.J.J. and Groenen, P.J.F. (2016). \emph{GenSVM: A Generalized 
+Multiclass Support Vector Machine}, Journal of Machine Learning Research, 
+17(225):1--42. URL \url{http://jmlr.org/papers/v17/14-526.html}.
+}
+\seealso{
+\code{\link{predict.gensvm.grid}}, \code{\link{print.gensvm.grid}}, and 
+\code{\link{gensvm}}.
+}
+