Do.GPAV: GPAV - High Level Function
In gecko515/HEMDAG: Hierarchical Ensemble Methods for Directed Acyclic Graphs

Description Usage Arguments Details Value See Also Examples

High level function to correct the computed scores in a hierarchy according to the GPAV algorithm.

Do.GPAV(norm = TRUE, norm.type = NULL, W = NULL, parallel = FALSE,
  ncores = 1, folds = 5, seed = 23, n.round = 3,
  f.criterion = "F", recall.levels = seq(from = 0.1, to = 1, by = 0.1),
  compute.performance = FALSE, 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)

`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: `NULL` (`def.`): set `norm.type` to `NULL` if and only if the parameter `norm` is set to `TRUE`; `MaxNorm`: each score is divided for the maximum of each class; `Qnorm`: quantile normalization. preprocessCore package is used;
`W`	vector of weight relative to a single example. If the vector `W` is not specified (`def. W=NULL`), `W` is a unitary vector of the same length of the columns' number of the flat scores matrix (root node included).
`parallel`	boolean value: `TRUE`: execute the parallel implementation of `GPAV` (`GPAV.parallel`); `FALSE` (`def.`): execute the sequential implementation of `GPAV` (`GPAV.over.examples`);
`ncores`	number of cores to use for parallel execution (`def. 8`). Set the parameter `ncores` to `1` if the parameter `parallel` is set to `FALSE`, otherwise set the desired number of cores.
`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. If `compute.performance` is set to `FALSE`, `folds` is automatically set to `NULL`.
`seed`	initialization seed for the random generator to create folds (`def. 23`). If `NULL` folds are generated without seed initialization. The parameter `seed` controls both the parameter `kk` and the parameter `folds`. If `compute.performance` is set to `FALSE` and `bottomup` is set to `threshold.free`, then `seed` is automatically set to `NULL`.
`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. If `compute.performance` is set to `FALSE` and `bottomup` is set to `threshold.free`, then `n.round` is automatically set to `NULL`.
`f.criterion`	character. Type of F-measure to be used to select the best F-measure. Two possibilities: `F` (def.): corresponds to the harmonic mean between the average precision and recall; `avF`: corresponds to the per-example `F-score` averaged across all the examples; If `compute.performance` is set to `FALSE` and `bottomup` is set to `threshold.free`, then `f.criterion` is automatically set to `NULL`.
`recall.levels`	a vector with the desired recall levels (`def:` `from:0.1`, `to:0.9`, `by:0.1`) to compute the Precision at fixed Recall level (PXR). If `compute.performance=FALSE` the parameter `recall.levels` is automatically set to `NULL`.
`compute.performance`	boolean value: should the flat and hierarchical performance (`AUPRC`, `AUROC`, `PXR`, `multilabel F-score`) be returned? `FALSE` (`def.`): performance are not computed and just the hierarchical scores matrix is returned; `TRUE`: both performance and hierarchical scores matrix are returned;
`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 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 the performance measures must be stored. If `compute.performance=FALSE` the functions automatically sets `perf.dir` to `NULL`.

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.

Two rda files stored in the respective output directories:

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.
Performance Measures: flat and hierarchical performance 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 precision.at.given.recall.levels.over.classes (PXR);
4. FMM results computed though compute.Fmeasure.multilabel (FMM);

It is stored in the perf.dir directory.

GPAV

data(graph);
data(scores);
data(labels);
tmpdir <- paste0(tempdir(),"/");
save(g, file=paste0(tmpdir,"graph.rda"));
save(L, file=paste0(tmpdir,"labels.rda"));
save(S, file=paste0(tmpdir,"scores.rda"));
dag.dir <- flat.dir <- ann.dir <- tmpdir;
hierScore.dir <- perf.dir <- tmpdir;
recall.levels <- seq(from=0.25, to=1, by=0.25);
dag.file <- "graph";
flat.file <- "scores";
ann.file <- "labels";
Do.GPAV(norm=FALSE, norm.type= "MaxNorm", W=NULL, parallel=FALSE, ncores=1, folds=NULL, 
seed=23, n.round=3, f.criterion ="F", recall.levels=recall.levels, compute.performance=TRUE, 
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);