HEMDAG (version 2.1.3)

Do.heuristic.methods: Do Heuristic Methods

Description

High level function to compute the hierarchical heuristic methods MAX, AND, OR (Heuristic Methods MAX, AND, OR (Obozinski et al., Genome Biology, 2008)

Usage

Do.heuristic.methods(heuristic.fun = "AND", norm = TRUE, norm.type = NULL,
  folds = 5, seed = 23, n.round = 3, f.criterion = "F",
  rec.levels = seq(from = 0.1, to = 1, by = 0.1), flat.file = flat.file,
  ann.file = ann.file, dag.file = dag.file, flat.dir = flat.dir,
  ann.dir = ann.dir, dag.dir = dag.dir, hierScore.dir = hierScore.dir,
  perf.dir = perf.dir)

Arguments

heuristic.fun

can be one of the following three values:

1. "MAX": run the heuristic method MAX;

2. "AND": run the heuristic method AND;

3. "OR": run the heuristic method OR;

norm

boolean value:

• TRUE (def.): the flat scores matrix has been already normalized in according to a normalization method;

• FALSE: the flat scores matrix has not been normalized yet. See the parameter norm.type for which normalization can be applied.

norm.type

can be one of the following three values:

1. NULL (def.): set norm.type to NULL if and only if the parameter norm is set to TRUE;

2. MaxNorm: each score is divided for the maximum of each class;

3. Qnorm: quantile normalization. preprocessCore package is used.

folds

number of folds of the cross validation on which computing the performance metrics averaged across folds (def. 5). If folds=NULL, the performance metrics are computed one-shot, otherwise the performance metrics are averaged across folds.

seed

initialization seed for the random generator to create folds (def. 23). If NULL folds are generated without seed initialization.

n.round

number of rounding digits to be applied to the hierarchical scores matrix (def. 3). It is used for choosing the best threshold on the basis of the best F-measure

f.criterion

character. Type of F-measure to be used to select the best F-measure. 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

rec.levels

a vector with the desired recall levels (def: from:0.1, to:0.9, by:0.1) to compute the the Precision at fixed Recall level (PXR)

flat.file

name of the file containing the flat scores matrix to be normalized or already normalized (without rda extension)

ann.file

name of the file containing the the label matrix of the examples (without rda extension)

dag.file

name of the file containing the graph that represents the hierarchy of the classes (without rda extension)

flat.dir

relative path where flat scores matrix is stored

ann.dir

relative path where annotation matrix is stored

dag.dir

relative path where graph is stored

hierScore.dir

relative path where the hierarchical scores matrix must be stored

perf.dir

relative path where all the performance measures must be stored

Value

Two rda files stored in the respective output directories:

1. Hierarchical Scores Results: a matrix with examples on rows and classes on columns representing the computed hierarchical scores for each example and for each considered class. It is stored in the hierScore.dir directory.

2. Performance Measures: flat and hierarchical performace results:

   1. AUPRC results computed though AUPRC.single.over.classes (AUPRC);

   2. AUROC results computed through AUROC.single.over.classes (AUROC);

   3. PXR results computed though PXR.at.multiple.recall.levels.over.classes (PXR);

   4. FMM results computed though compute.Fmeasure.multilabel (FMM);

It is stored in the perf.dir directory.

Details

The function checks if the number of classes between the flat scores matrix and the annotations matrix mismatched. If so, the number of terms of the annotations matrix is shrunk to the number of terms of the flat scores matrix and the corresponding subgraph is computed as well. N.B.: it is supposed that all the nodes of the subgraph are accessible from the root.

See Also

Heuristic-Methods

Examples

# NOT RUN {
data(graph);
data(scores);
data(labels);
if (!dir.exists("data")){
	dir.create("data");
}
if (!dir.exists("results")){
	dir.create("results");
}
save(g,file="data/graph.rda");
save(L,file="data/labels.rda");
save(S,file="data/scores.rda");
dag.dir <- flat.dir <- ann.dir <- "data/";
hierScore.dir <- perf.dir <- "results/";
rec.levels <- seq(from=0.1, to=1, by=0.1);
dag.file <- "graph";
flat.file <- "scores";
ann.file <- "labels";
Do.heuristic.methods(heuristic.fun="AND", norm=FALSE, norm.type="MaxNorm",
folds=5, seed=23, n.round=3, f.criterion ="F", rec.levels=rec.levels, 
flat.file=flat.file, ann.file=ann.file, dag.file=dag.file, flat.dir=flat.dir, 
ann.dir=ann.dir, dag.dir=dag.dir, hierScore.dir=hierScore.dir, perf.dir=perf.dir);
# }