PRROC (version 1.3.1)

pr.curve: PR curve

Description

Computes the area under the precision-recall (PR) curve for weighted and unweighted data. In contrast to other implementations, the interpolation between points of the PR curve is done by a non-linear piecewise function. In addition to the area under the curve, the curve itself can be obtained by setting argument curve to TRUE.

Usage

pr.curve( scores.class0, scores.class1=scores.class0, weights.class0=NULL, 
    weights.class1 = {if(is.null(weights.class0)){NULL}else{1-weights.class0}}, 
    sorted = FALSE, curve = FALSE, 
    minStepSize=min(1,ifelse(is.null(weights.class0),1,sum(weights.class0)/100)),
    max.compute=F, min.compute=F, rand.compute=F,dg.compute=T)

Arguments

scores.class0

the classification scores of i) all data points or ii) only the data points belonging to the positive class.

In the first case, scores.class1 should not be assigned an explicit value, but left at the default (scores.class1=scores.class0). In addition, weights.class0 needs to contain the class labels of the data points (1 for positive class, 0 for negative class) or the soft-labels for the positive class, i.e., the probability for each data point to belong to the positive class. Accordingly, weights.class1 should be left at the default value (1-weights.class0).

In the second case, the scores for the negative data points need to be provided in scores.class1. In this case, weights.class0 and weights.class1 need to be provided only for soft-labelling and should be of the same length as scores.class0 and scores.class1, respectively.

scores.class1

the scores of the negative class if provided separately (see scores.class0)

weights.class0

the weights for the data points of the positive class in same ordering as scores.class0 (optional)

weights.class1

the weights for the data points of the negative class in same ordering as scores.class1 (optional)

sorted

TRUE if the scores are already sorted

curve

TRUE if the curve should also be returned, FALSE otherwise

minStepSize

the minimum step size between intermediate points of the curve, does not affect the computation of AUC-PR

max.compute

TRUE if the maximum PR curve given the supplied weights should be computed

min.compute

TRUE if the minimum PR curve given the supplied weights should be computed

rand.compute

TRUE if the PR curve of a random classifier given the supplied weights should be computed

dg.compute

TRUE if the area under the curve according to the interpolation of Davis and Goadrich should be computed. Reduces runtime if switched off.

Value

type

always "PR"

auc.integral

area under the curve computed by integration of the piecewise function

auc.davis.goadrich

area under the curve computed using the interpolation of Davis & Goadrich (2006). Is NA if weights are provided and different from 1.

curve

the PR curve as a matrix, where the first column contains recall, the second contains precision, and the third contains the corresponding threshold on the scores.

max

the maximum PR curve (if max.compute=TRUE)

min

the minimum PR curve (if min.compute=TRUE)

rand

the PR curve of a random classifier (if rand.compute=TRUE)

Details

This function computes the area under a precision-recall curve and, optionally, the curve itself and returns it as a PRROC object (see below). It can be used under different scenarios:

1. Standard, hard-labeled classification problems:

Each data point is uniquely assigned to one out of two possible classes. In this case, the classification scores may be either provided separately for the data points of each of the classes, i.e., as scores.class0 for the data points from the positive/foreground class and as scores.class1 for the data points of the negative/background class; or the classification scores for all data points are provided as scores.class0 and the labels are provided as numerical values (1 for the positive class, 0 for the negative class) as weights.class0.

2. Weighted, hard-labeled classification problems:

Each data point is uniquely assigned to one out of two possible classes, where each data points additionally has a weight assigned, for instance multiplicities in the original data set. In this case, the classification scores need to be provided separately for the data points of each of the classes, i.e., as scores.class0 for the data points from the positive/foreground class and as scores.class1 for the data points of the negative/background class. In addition, the weights for the data points must be provided as weights.class0 and weights.class1, respectively.

3. Soft-labeled classification problems:

Each data point belongs to both of the two classes with a certain probability, where for each data point, these two probabilities add up to 1. In this case, the classification scores for all data points need to be provided only once as scores.class0 and only the positive/foreground weights for each data point need to be provided in weights.class0, while the converse probability for the negative class is automatically set to weights.class1=1.0-weights.class0.

References

J. Davis and M. Goadrich. The relationship between precision-recall and ROC curves. In Proceedings of the 23rd International Conference on Machine Learning, pages 233--240, New York, NY, USA, 2006. ACM.

J. Keilwagen, I. Grosse, and J. Grau. Area under precision-recall curves for weighted and unweighted data, PLOS ONE (9) 3, 2014.

J. Grau, I. Grosse, and J. Keilwagen. PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R. Bioinformatics, 31(15):2595-2597, 2015.

See Also

roc.curve

plot.PRROC

Examples

# NOT RUN {
# create artificial scores as random numbers
x <- rnorm( 1000 );
y <- rnorm( 1000, -1 );
# compute area under PR curve for the hard-labeled case
pr <- pr.curve( x, y );
print( pr );

# compute PR curve and area under curve
pr <- pr.curve( x, y, curve = TRUE );
# plot curve
plot(pr);

# create artificial weights
x.weights <- runif( 1000 );
y.weights <- runif( 1000 );

# compute PR curve and area under curve for weighted, hard-labeled data
pr <- pr.curve( x, y, x.weights, y.weights, curve = TRUE );
# and plot the curve
plot(pr);


# compute PR curve and area under curve,
# and maximum, minimum, and random PR curve for weighted, hard-labeled data
pr <- pr.curve(x, y, x.weights, y.weights, curve = TRUE, max.compute = TRUE, 
  min.compute = TRUE, rand.compute = TRUE);
# plot all three curves
plot(pr, max.plot = TRUE, min.plot = TRUE, rand.plot = TRUE, fill.area = TRUE)


# concatenate the drawn scores
scores<-c(x,y);
# and create artificial soft-labels
weights<-c(runif(1000, min = 0.5, max = 1), runif(1000, min = 0, max = 0.5))

# compute PR curve and area under curve,
# and maximum, minimum, and random PR curve for soft-labeled data
pr<-pr.curve(scores.class0 = scores, weights.class0 = weights, curve = TRUE, 
  max.compute = TRUE, min.compute = TRUE, rand.compute = TRUE);
# plot all three curves
plot(pr, max.plot = TRUE, min.plot = TRUE, rand.plot = TRUE, fill.area = TRUE)

# print the areas under the curves
print(pr);


# generate classification scores of a second classifier
scores.2<-c(rnorm( 1000 ),rnorm( 1000, -2 ));
# and compute the PR curve
pr.2<-pr.curve(scores.class0 = scores.2, weights.class0 = weights, curve = TRUE)
# plot all three curves for the first classifier in red
plot(pr, max.plot = TRUE, min.plot = TRUE, rand.plot = TRUE, fill.area = TRUE, 
  color="red", auc.main=FALSE)
# and add the curve for the second classifier
plot(pr.2, add=TRUE, color="green")
# }