find.optimal.thresh.cv: Function to find the optimal RANKS score thereshold

In RANKS: Ranking of Nodes with Kernelized Score Functions

Function to find the optimal RANKS score thereshold

Description

Function to find the optimal quantile alpha and corresponding threshold by cross-validation with a kernel-based score method.

Usage

find.optimal.thresh.cv(K, ind.pos, ind.non.pos, m = 5, 
alpha = seq(from = 0.05, to = 0.6, by = 0.05), init.seed = NULL, 
opt.fun = compute.F, fun = KNN.score, ...)

Arguments

K	matrix. Kernel matrix or any valid symmetric matrix
ind.pos	indices of the positive examples. They are the indices the row of RW corresponding to positive examples of the training set.
ind.non.pos	indices of the non positive examples. They are the indices the row of RW corresponding to non positive examples of the training set.
m	number of folds (default: 5)
alpha	vector of the quantiles to be tested
init.seed	initial seed for the random generator. If NULL (def) no initialization is performed
opt.fun	Function implementing the metric to select the optimal threshold. The F-score (compute.F) is the default. Available functions: - compute.F: F-score (default) - compute.acc:accuracy. Any function having two arguments representing the vector of predicted and true labels can be in principle used.
fun	function. It must be a kernel-based score method (default KNN.score)
...	optional arguments for the function fun

Details

Function to find the optimal quantile alpha and corresponding threshold by cross-validation with a kernel-based score method. The optimality is computed with respect to a specific metric (def: F-score). This function is used by multiple.ker.score.thresh.cv, ker.score.classifier.holdout, ker.score.classifier.cv.

Value

A list with 3 elements:

alpha	quantile corresponding to the best F-score
thresh	threshold corresponding to the best F-score
pos.scores	scores of the positive elements computed through CV

See Also

multiple.ker.score.thresh.cv, Kernel functions, ker.score.classifier.holdout

Examples

# Finding the optimal threshold in the Tanimoto chemical structure similarity network 
# between 1253 DrugBank drugs for the prediction of the DrugBank category Penicillins using
# the KNN-score with the random walk kernel 
library(bionetdata);
data(DD.chem.data);
data(DrugBank.Cat);
K <- rw.kernel(DD.chem.data);
labels <- DrugBank.Cat[,"Penicillins"];
ind.pos <- which(labels==1);
ind.non.pos <- which(labels==0);
res <- find.optimal.thresh.cv(K, ind.pos, ind.non.pos);
res

RANKS documentation built on Sept. 21, 2022, 9:06 a.m.