% Generated by roxygen2: do not edit by hand

% Please edit documentation in R/CrossValidation.R

\name{CrossValidationSSL}

\alias{CrossValidationSSL}

\alias{CrossValidationSSL.list}

\alias{CrossValidationSSL.matrix}

\title{Cross-validation in semi-supervised setting}

\usage{

CrossValidationSSL(X, y, ...)

\method{CrossValidationSSL}{list}(X, y, ..., verbose = FALSE, mc.cores = 1)

\method{CrossValidationSSL}{matrix}(X, y, classifiers, measures = list(Error

= measure_error), k = 10, repeats = 1, verbose = FALSE,

leaveout = "test", n_labeled = 10, prop_unlabeled = 0.5, time = TRUE,

pre_scale = FALSE, pre_pca = FALSE, n_min = 1, low_level_cores = 1,

...)

}

\arguments{

\item{X}{design matrix of the labeled objects}

\item{y}{vector with labels}

\item{...}{arguments passed to underlying functions}

\item{verbose}{logical; Controls the verbosity of the output}

\item{mc.cores}{integer; Number of cores to be used}

\item{classifiers}{list; Classifiers to crossvalidate}

\item{measures}{named list of functions giving the measures to be used}

\item{k}{integer; Number of folds in the cross-validation}

\item{repeats}{integer; Number of repeated assignments to folds}

\item{leaveout}{either "labeled" or "test", see details}

\item{n_labeled}{Number of labeled examples, used in both leaveout modes}

\item{prop_unlabeled}{numeric; proportion of unlabeled objects}

\item{time}{logical; Whether execution time should be saved.}

\item{pre_scale}{logical; Whether the features should be scaled before the dataset is used}

\item{pre_pca}{logical; Whether the features should be preprocessed using a PCA step}

\item{n_min}{integer; Minimum number of labeled objects per class}

\item{low_level_cores}{integer; Number of cores to use compute repeats of the learning curve}

}

\description{

Cross-validation for semi-supervised learning, in which the dataset is split in three parts: labeled training object, unlabeled training object and validation objects. This can be used to evaluate different approaches to semi-supervised classification under the assumption the labels are missing at random. Different cross-validation schemes are implemented. See below for details.

}

\details{

The input to this function can be either: a dataset in the form of a feature matrix and factor containing the labels, a dataset in the form of a formula and data.frame or a named list of these two options.

There are two main modes in which the cross-validation can be carried out, controlled by the \code{leaveout} parameter.

When leaveout is "labeled", the folds are formed by non-overlapping labeled training sets of a user specified size.

Each of these folds is used as a labeled set, while the rest of the objects are split into the an unlabeled and the test set, controlled by \code{prop_unlabeled} parameter. Note that objects can be used multiple times for testing, when training on a different fold, while other objects may never used for testing.

The "test" option of \code{leaveout}, on the other hand, uses the folds as the test sets. This means every object will be used as a test object exactly once. The remaining objects in each training iteration are split randomly into a labeled and an unlabeled part, where the number of the labeled objects is controlled by the user through the n_labeled parameter.

}

\examples{

X <- model.matrix(Species~.-1,data=iris)

y <- iris$Species

classifiers <- list("LS"=function(X,y,X_u,y_u) {

LeastSquaresClassifier(X,y,lambda=0)},

"EM"=function(X,y,X_u,y_u) {

SelfLearning(X,y,X_u,

method=LeastSquaresClassifier)}

)

measures <- list("Accuracy" = measure_accuracy,

"Loss" = measure_losstest,

"Loss labeled" = measure_losslab,

"Loss Lab+Unlab" = measure_losstrain

)

cvresults1 <- CrossValidationSSL(X,y,

classifiers=classifiers,

measures=measures,

leaveout="test", k=10,

repeats = 2,n_labeled = 10)

print(cvresults1)

plot(cvresults1)

cvresults2 <- CrossValidationSSL(X,y,

classifiers=classifiers,

measures=measures,

leaveout="labeled", k=10,

repeats = 2,n_labeled = 10,

prop_unlabeled=0.5)

print(cvresults2)

plot(cvresults2)

}