fmax: Compute Fmax
In HEMDAG: Hierarchical Ensemble Methods for Directed Acyclic Graphs

Description Usage Arguments Details Value Examples

Compute the best hierarchical Fmax either one-shot or averaged across folds

compute.fmax(
  target,
  predicted,
  n.round = 3,
  verbose = TRUE,
  b.per.example = FALSE,
  folds = NULL,
  seed = NULL
)

`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`).
`verbose`	a boolean value. If `TRUE` (def.) the number of iterations are printed on stdout.
`b.per.example`	a boolean value. `TRUE`: results are returned for each example; `FALSE`: only the average results are returned;
`folds`	number of folds on which computing the Fmax If `folds=NULL` (`def.`), the Fmax is computed one-shot, otherwise the Fmax 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 Fmax averaged across folds is computed without seed initialization.

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

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);

data(graph);
data(labels);
data(scores);
root <- root.node(g);
L <- L[,-which(colnames(L)==root)];
S <- S[,-which(colnames(S)==root)];
fmax <- compute.fmax(L, S, n.round=3, verbose=TRUE, b.per.example=TRUE, folds=5, seed=23);