Utilities: Utility functions

Description Usage Arguments Details Value Examples

Description

Mixed utility functions to compute accuracy, norms, labels from scores and to perform stratified cross-validation.

Usage

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compute.acc(pred, labels)
compute.F(pred, labels)
norm1(x)
Unit.sphere.norm(K)
do.stratified.cv.data(examples, positives, k = 5, seed = NULL)
labelsfromscores(scores, thresh)
Multiple.labels.from.scores(S, thresh.vect)
selection.test(pos.scores, av.scores, ind.positives, alpha = 0.05, thresh.pos = 0)

Arguments

pred

vector of the predicted labels

labels

vector of the true labels. Note that 0 stands for negative and 1 for positive. In general the first level is negative and the second positive

x

numeric vector

K

a kernel matrix

examples

indices of the examples (a vector of integer)

positives

vector of integer. Indices of the positive examples. The indices refer to the indices of examples

k

number of folds (def = 5)

seed

seed of the random generator (def=NULL). If is set to NULL no initiazitation is performed

scores

numeric. Vector of scores: each element correspond to the score of an example

thresh

real value. Threshold for the classification

S

numeric matrix. Matrix of scores: rows represent examples, columns classes

thresh.vect

numeric vector. Vector of the thresholds for multiple classes (one threshold for each class)

pos.scores

vector with scores of positive examples. It is returned from multiple.ker.score.cv.

av.scores

a vector with the average scores computed by multiple.ker.score.cv. It may be a named vector. If not, the names attributes corresponding to the indices of the vector are added.

ind.positives

indices of the positive examples. They are the indices of av.scores corresponding to positive examples.

alpha

quantile level (def. 0.05)

thresh.pos

only values larger than thresh.pos are retained in pos.scores (def.: 0)

Details

compute.acc computes the accuracy for a single class

compute.F computes the F-score for a single class

norm1 computes the L1-norm of a numeric vector

Unit.sphere.norm normalize a kernel according to the unit sphere

do.stratified.cv.data generates data for the stratified cross-validation. In particular subdividas the indices that refer to the rows of the data matrix in different folds (separated for positive and negative examples)

labelsfromscores computes the labels of a single class from the corresponding scores

Multiple.labels.from.scores computes the labels of multiple classes from the corresponding scores

selection.test is a non parametric test to select the most significant unlabeled examples

Value

compute.acc returns the accuracy

compute.F returns the F-score

norm1 returns the L1-norm value

Unit.sphere.norm returns the kernel normalized according to the unit sphere

do.stratified.cv.data returns a list with 2 two components:

fold.non.positives

a list with k components. Each component is a vector with the indices of the non positive elements of the fold

fold.positives

a list with k components. Each component is a vector with the indices of the positive elements of the fold

Indices refer to row numbers of the data matrix

labelsfromscores returns a numeric vector res with 0 or 1 values. The label res[i]=1 if scores[i]>thresh, otherwise res[i]=0

Multiple.labels.from.scores returns a binary matrix with the labels of the predictions. Rows represent examples, columns classes. Element L[i,j] is the label of example i w.r.t. class j. L[i,j]=1 if i belongs to j, 0 otherwise.

selection.test returns a list with 5 components:

selected

a named vector with the components of av.scores selected by the test

selected.labeled

a named vector with the labeled components of av.scores selected by the test

selected.unlabeled

a named vector with the unlabeled components of av.scores selected by the test

thresh

the score threshold selected by the test

alpha

significance level (the same value of the input)

Examples

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# L1-norm of a vector
norm1(rnorm(10));
# generation of 5 stratified folds;
do.stratified.cv.data(1:100, 1:10, k = 5, seed = NULL);
# generation of labels form scores.
labelsfromscores(runif(20), thresh=0.3);


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