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;