RSSL (version 0.9.7)

CrossValidationSSL: Cross-validation in semi-supervised setting

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.

Usage

CrossValidationSSL(X, y, ...)
# S3 method for list
CrossValidationSSL(X, y, ..., verbose = FALSE, mc.cores = 1)
# S3 method for matrix
CrossValidationSSL(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

X

design matrix of the labeled objects

y

vector with labels

...

arguments passed to underlying functions

verbose

logical; Controls the verbosity of the output

mc.cores

integer; Number of cores to be used

classifiers

list; Classifiers to crossvalidate

measures

named list of functions giving the measures to be used

k

integer; Number of folds in the cross-validation

repeats

integer; Number of repeated assignments to folds

leaveout

either "labeled" or "test", see details

n_labeled

Number of labeled examples, used in both leaveout modes

prop_unlabeled

numeric; proportion of unlabeled objects

time

logical; Whether execution time should be saved.

pre_scale

logical; Whether the features should be scaled before the dataset is used

pre_pca

logical; Whether the features should be preprocessed using a PCA step

n_min

integer; Minimum number of labeled objects per class

low_level_cores

integer; Number of cores to use compute repeats of the learning curve

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 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 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 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
)

# Cross-validation making sure test folds are non-overlapping
cvresults1 <- CrossValidationSSL(X,y, 
                                 classifiers=classifiers, 
                                 measures=measures,
                                 leaveout="test", k=10,
                                 repeats = 2,n_labeled = 10)
print(cvresults1)
plot(cvresults1)

# Cross-validation making sure labeled sets are non-overlapping
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)