COSNet (version 1.6.0)

cosnet.cross.validation: Cross validation procedure for the COSNet algorithm

Description

This function applies the function COSNet to the input data with a cross validation procedure.

Usage

cosnet.cross.validation(labels, W, nfolds, cost)

Arguments

labels

named matrix of node labels. The (i-j)-th component contains the label (1 for positive examples, -1 for negative examples) of node i for j-th functional class to be predicted

W

square symmetric named matrix. The (i,j)-th component is a similarity index between node i and node j. The components of the diagonal of W are zero.

nfolds

integer corresponding to the number of desired folds

cost

real value that corresponds to the cost parameter of COSNet

Value

List with three elements:

labels

1/-1 named input label matrix, in which rows correspond to nodes and columns to classes

predictions

named 1/-1 prediction matrix, in which rows correspond to nodes and columns to classes. The position i-j-th is 1 if the node i has been predicted as positive for the class j, -1 otherwise

scores

named real score matrix, in which rows correspond to nodes and columns to classes. The position i-j-th is a real number corresponding to the internal energy at equlibrium for node i when predicting class j. This score is a "degree" of membership of node i to the class j

Details

cosnet.cross.validation runs the function COSNet on the input data through a cross validation procedure. For each class to be predicted (column of "labels"), both "W" and "labels" are partitioned into "nfolds" subsets and at each iteration the labels of a fold are hidden and predicted with function COSNet. When possible, input data are partitioned by ensuring the same proportion of positive and negative instances in each fold.

See Also

COSNet

Examples

library(bionetdata);
data(Yeast.STRING.data)
data(Yeast.STRING.FunCat) # "Yeast.STRING.FunCat"
## excluding the dummy "00" root
Yeast.STRING.FunCat <- 
        Yeast.STRING.FunCat[, -which(colnames(Yeast.STRING.FunCat) == "00")];
nfolds <- 5;
res <- cosnet.cross.validation(Yeast.STRING.FunCat[, 1:50], Yeast.STRING.data,
        nfolds, 0.0001);
## computing performances
library(PerfMeas);
perf <- F.measure.single.over.classes(res$labels, res$predictions);
cat(perf$average);