f.criterion
from
tpr.dag.cv
, tpr.dag.holdout
,
find.best.f
and compute.fmax
: type of
F-measure used to select the best F-measure is always the harmonic mean
between the average precision and recall (f.criterion="F"
)
and never the F-measure computed as average across examples
(f.criterion="avF"
);tpr.dag.holdout
;tpr.dag.cv
and
tpr.dag.holdout
;build.scores.matrix.from.list
;build.scores.matrix.from.tupla
;Do.HTD
–> htd.vanilla
;Do.HTD.holdout
–> htd.holdout
;heuristic.max
–> obozinski.max
;heuristic.and
–> obozinski.and
;heuristic.or
–> obozinski.or
;Do.heuristic.methods
–>
obozinski.methods
;Do.heuristic.methods.holdout
–>
obozinski.holdout
;GPAV
–> gpav
;GPAV.over.examples
–>
gpav.over.examples
;GPAV.parallel
–> gpav.parallel
;Do.GPAV
–> gpav.vanilla
;Do.GPAV.holdout
–> gpav.holdout
;TPR.DAG
–> tpr.dag
;Do.TPR.DAG
–> tpr.dag.cv
;Do.TPR.DAG.holdout
–>
tpr.dag.holdout
;get.parents
–> build.parents
;get.parents.top.down
–>
build.parents.top.down
;get.parents.bottom.up
–>
build.parents.bottom.up
;get.parents.topological.sorting
–>
build.parents.topological.sorting
;get.children.top.down
–>
build.children.top.down
;get.children.bottom.up
–>
build.children.bottom.up
;check.DAG.integrity
–>
check.dag.integrity
;do.subgraph
–> build.subgraph
;do.submatrix
–> build.submatrix
;do.stratified.cv.data.single.class
–>
stratified.cv.data.single.class
;do.stratified.cv.data.over.classes
–>
stratified.cv.data.over.classes
;do.unstratified.cv.data
–>
unstratified.cv.data
;do.edges.from.HPO.obo
–>
build.edges.from.hpo.obo
;AUPRC.single.class
–>
auprc.single.class
;AUPRC.single.over.classes
–>
auprc.single.over.classes
;AUROC.single.class
–>
auroc.single.class
;AUROC.single.over.classes
–>
auroc.single.over.classes
;compute.Fmeasure.multilabel
–>
compute.fmax
;Do.flat.scores.normalization
;Do.full.annotation.matrix
;stringsAsFactors
issue – link;obogaf::parser
;build.consistent.graph
;Do.GPAV.holdout
;precision.at.all.recall.levels.single.class
(labels are all
negatives/positives);precision.at.given.recall.levels.over.classes
(labels in a
fold are all negatives/positives);do.stratified.cv.data.single.class
(sampling of the labels
with just one positive/negative);compute.performance
to the following
high level functions:
Do.TPR.DAG
and Do.TPR.DAG.holdout
;Do.HTD
and Do.HTD.holdout
;Do.GPAV
and Do.GPAV.holdout
;Do.heuristic.methods
and
Do.heuristic.methods.holdout
;lexicographical.topological.sort
;precrec
package:
precision.at.all.recall.levels.single.class
;PXR.at.multiple.recall.levels.over.classes
–>
precision.at.given.recall.levels.over.classes
;.txt
) or compressed
(.gz
);CRAN
Package Check Results: remove unneeded header
and define from GPAV C++
source codeGPAV
algorithm (Burdakov et al., Journal of
Computational Mathematics, 2006 – link);GPAV
algorithm in the top-down step of the
functions TPR.DAG
, Do.TPR.DAG
and
Do.TPR.DAG.holdout
;help("HEMDAG-defunct")
;C++
code of GPAV
algorithm;compute.Fmeasure.multilabel
;PXR.at.multiple.recall.levels.over.classes
;AUPRC
, AUROC
,
FMM
, PXR
) can be computed either
one-shot or averaged across
folds;metric
: maximization by
FMAX
or PRC
(see manual for further
details);do.stratified.cv.data.single.class
;add TPR-DAG
: function gathering several hierarchical
ensemble variants;
add Do.TPR.DAG
: high-level function to run
TPR-DAG
cross-validated
experiments;
add Do.TPR.DAG.holdout
: high-level functions to run
TPR-DAG
holdout experiments;
The following TPR-DAG
and DESCENS
high-level functions were remove:
Do.tpr.threshold.free
;Do.tpr.threshold.cv
;Do.tpr.weighted.threshold.free.cv
;Do.tpr.weighted.threshold.cv
;Do.descens.threshold.free
;Do.descens.threshold.cv
;Do.descens.weighted.threshold.free.cv
;Do.descens.tau.cv
;Do.descens.weighted.threshold.cv
;Do.tpr.threshold.free.holdout
;Do.tpr.threshold.holdout
;Do.tpr.weighted.threshold.free.holdout
;Do.tpr.weighted.threshold.holdout
;Do.descens.threshold.free.holdout
;Do.descens.threshold.holdout
;Do.descens.weighted.threshold.free.holdout
;Do.descens.tau.holdout
;Do.descens.weighted.threshold.holdout
;NOTE: all the removed functions can be run opportunely by setting the input parameters of the new high-level function
Do.TPR.DAG
(for cross-validated experiments) andDo.TPR.DAG.holdout
(for hold-out experiments);
DESCENS
algorithm;Max
, And
,
Or
(Obozinski et al., Genome Biology, 2008 – link);tupla.matrix
function;HPOparser
(note: from
version 2.6.0
HPOparser
was changed in
obogaf::parser
);CITATION
file;