HEMDAG (version 2.1.3)

FMM: Compute Multilabel F-measure

Description

Function to compute the best hierarchical F-score either one-shot or averaged across folds

Usage

compute.Fmeasure.multilabel(target, pred, n.round = 3, f.criterion = "F",
  verbose = TRUE, b.per.example = FALSE, folds = NULL, seed = NULL)

Arguments

target

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

pred

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

n.round

number of rounding digits to be applied to pred (default=3)

f.criterion

character. Type of F-measure to be used to select the best F-score. There are two possibilities:

1. F (def.) corresponds to the harmonic mean between the average precision and recall;

2. avF corresponds to the per-example F-score averaged across all the examples.

verbose

boolean. If TRUE (def.) the number of iterations are printed on stdout

b.per.example

boolean.

• TRUE: results are returned for each example;

• FALSE: only the average results are returned

folds

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

seed

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

Value

Two different outputs respect to the input parameter b.per.example:

• b.per.example==FALSE: a list with a single element average. A named vector with 7 elements relative to the best result in terms of the F.measure: Precision (P), Recall (R), Specificity (S), F.measure (F), av.F.measure (av.F), Accuracy (A) and the best selected Threshold (T). F is the F-measure computed as the harmonic mean between the average precision and recall; av.F is the F-measure computed as the average across examples and T is the best selected threshold;

• b.per.example==FALSE: a list with two elements:

  1. average: a named vector with with 7 elements relative to the best result in terms of the F.measure: Precision (P), Recall (R), Specificity (S), F.measure (F), av.F.measure (av.F), Accuracy (A) and the best selected Threshold (T).

  2. per.example: a named matrix with the Precision (P), Recall (R), Specificity (S), Accuracy (A), F-measure (F), av.F-measure (av.F) and the best selected Threshold (T) for each example. Row names correspond to examples, column names correspond respectively to Precision (P), Recall (R), Specificity (S), Accuracy (A), F-measure (F), av.F-measure (av.F) and the best selected Threshold (T).

Details

Names of rows and columns of target and pred matrix must be provided in the same order, otherwise a stop message is returned

Examples

# NOT RUN {
data(graph);
data(labels);
data(scores);
root <- root.node(g);
L <- L[,-which(colnames(L)==root)];
S <- S[,-which(colnames(S)==root)];
FMM <- compute.Fmeasure.multilabel(L, S, n.round=3, f.criterion="F", verbose=TRUE, 
b.per.example=TRUE, folds=5, seed=23);
# }