HEMDAG (version 2.6.1)

FMM: Compute F-measure Multilabel

Description

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

Usage

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

Arguments

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.

n.round

number of rounding digits to be applied to predicted (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 AUROC is computed one-shot, 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=NULL and folds\(\neq\)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 predicted 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);
# }