HEMDAG (version 2.7.4)

pxr: Precision-Recall curves

Description

Compute the Precision-Recall (PxR) values through precrec package.

Usage

precision.at.all.recall.levels.single.class(labels, scores)
precision.at.given.recall.levels.over.classes(
  target,
  predicted,
  folds = NULL,
  seed = NULL,
  recall.levels = seq(from = 0.1, to = 1, by = 0.1)
)

Arguments

labels

vector of the true labels (0 negative, 1 positive examples).

scores

a numeric vector of the values of the predicted labels (scores).

target

matrix with the target multilabel: rows correspond to examples and columns to classes. \(target[i,j]=1\) if example \(i\) belongs to class \(j\), \(target[i,j]=0\) otherwise.

predicted

a numeric matrix with predicted values (scores): rows correspond to examples and columns to classes.

folds

number of folds on which computing the PXR. If folds=NULL (def.), the PXR is computed one-shot, otherwise the PXR is computed averaged across folds.

seed

initialization seed for the random generator to create folds. Set seed only if folds\(\neq\)NULL. If seed=NULL and folds\(\neq\)NULL, the PXR averaged across folds is computed without seed initialization.

recall.levels

a vector with the desired recall levels (def: from:0.1, to:0.9, by:0.1).

Value

precision.at.all.recall.levels.single.class returns a two-columns matrix, representing a pair of precision and recall values. The first column is the precision, the second the recall; precision.at.given.recall.levels.over.classes returns a list with two elements:

1. average: a vector with the average precision at different recall levels across classes;

2. fixed.recall: a matrix with the precision at different recall levels: rows are classes, columns precision at different recall levels;

Details

precision.at.all.recall.levels.single.class computes the precision at all recall levels just for a single class.

precision.at.given.recall.levels.over.classes computes the precision at fixed recall levels over classes.

Examples

# NOT RUN {
data(labels);
data(scores);
data(graph);
root <- root.node(g);
L <- L[,-which(colnames(L)==root)];
S <- S[,-which(colnames(S)==root)];
labels <- L[,1];
scores <- S[,1];
rec.levels <- seq(from=0.25, to=1, by=0.25);
pxr.single <- precision.at.all.recall.levels.single.class(labels, scores);
pxr <- precision.at.given.recall.levels.over.classes(L, S, folds=5, seed=23,
         recall.levels=rec.levels);
# }