PXR: Precision at a given recall level measures
In PerfMeas: Performance Measures for Ranking and Classification Tasks

PXR	R Documentation

Precision at a given recall level measures

Description

Set of functions to compute the precision at fixed recall levels.

Usage

precision.at.recall.level(scores, labels, rec.level = 0.2)
precision.at.recall.level.over.classes(target, predicted,  
                          g, rec.level = 0.2, root = "00")
precision.at.multiple.recall.level(scores, labels, 
           rec.levels = seq(from = 0.1, to = 1, by = 0.1))
precision.at.multiple.recall.level.over.classes(target, 
   predicted, rec.levels = seq(from = 0.1, to = 1, by = 0.1))
precision.at.all.recall.levels(scores, labels, resolution=1)

Arguments

`scores`	vector of the predicted scores in [0,1]
`labels`	0/1 vector of the true labels
`rec.level`	rec.level: the desired recall level (def: 0.2)
`target`	matrix with the target multilabels; rows correspond to examples, columns to classes
`predicted`	matrix with the predicted multilabels; rows correspond to examples, columns to classes
`g`	graph of the classes (object of class graphNEL, package graph). If missing, no per level results are computed.
`root`	the name of the root node (def. "00") of the graph g.
`rec.levels`	a vector with the desired recall levels (def. 0.1 to 1 by 0.1 step)
`resolution`	a number between 0 and 1 (def. 1). This represents the fraction of precision, recall and f-score values returned.

Details

precision.at.recall.level computes the precision at a given recall level for a single class.

precision.at.recall.level.over.classes computes precision at a given recall level for a set of classes.

precision.at.multiple.recall.level computes the precision at multiple levels of recall for a single class.

precision.at.multiple.recall.level.over.classes computes the precision at multiple levels of recall for multiple classes.

precision.at.all.recall.levels compute the precision at all recall levels for a single class. It returns a pair of precision and recall values by moving a threshold from the lowest to the highest score: a number of precision and recall values equal to the number n of available examples is returned if resolution=1, otherwise a number of values equal to n * resolution.

Value

precision.at.recall.level returns the precision at the requested recall

precision.at.recall.level.over.classes a list with three elements:

`- average`	the average precision at a given recall level across classes.
`- per.level`	a named vector with average precision at a given recall level for each level of the hierarchy; names correspond to levels
`- per.class`	a named vector with precision at a given recall level for each class. Names correspond to classes

precision.at.multiple.recall.level a list with 2 elements:

`- precisions`	a vector with the precision at different recall levels
`- f.score`	a vector with the f-score at different recall levels

precision.at.multiple.recall.level.over.classes

`- PXR`	a matrix with the precisions at different recall levels: rows are classes, columns precisions at different recall levels
`- avgPXR`	a vector with the the average precisions at different recall levels across classes

precision.at.all.recall.levels a list with 3 elements:

`- precision`	precision at different thresholds
`- recall`	recall at different thresholds
`- f.score`	f.score at different thresholds

Examples

# preparing pseudo-random predictions and target-labels for examples: 
# 100 examples and 10 classes
Scores <- matrix(runif(1000),nrow=100);
Targets <- matrix(integer(1000),nrow=100);
Targets[Scores>0.5] <- 1;
# adding noise to scores
Scores <- Scores + matrix(rnorm(1000, sd=0.3),nrow=100);
colnames(Scores) <-colnames(Targets) <- LETTERS[1:10];
# getting scores and labels of class "A"
scores <- Scores[,"A"];
labels <- Targets[,"A"];
# precsion at 0.4 recall level for class A
precision.at.recall.level(scores, labels, rec.level=0.4);
# precision at 0.4 recall level for all the 10 classes
precision.at.recall.level.over.classes(Targets, Scores, rec.level=0.4);
# precision at multiple recall levels for class A
levels <- seq(from=0.1, to=1, by=0.1);
precision.at.multiple.recall.level(scores, labels, rec.levels=levels);
# precision at multiple recall levels for all the 10 classes
precision.at.multiple.recall.level.over.classes(Targets, Scores);
# precision, recall and f-score for a single class obtained 
# by moving the threshold across the examples
precision.at.all.recall.levels(scores, labels);

PerfMeas documentation built on Sept. 14, 2022, 9:06 a.m.