Quickstart¶
This short HowTo guides you from downloading HEMDAG library, load it into your R environment and make first computations.
Installation¶
Please go to the Installation section and install HEMDAG by using one of the ways shown.
Load HEMDAG Library¶
Start R in your console using
$ R
then load the library by using
> library(HEMDAG)
First Classification – for the Impatient¶
HEMDAG encompasses in total 23 hierarchical ensemble methods. Below we show the simple call to all the hierarchical ensemble algorithms included in HEMDAG, bu using the pre-built datasets available in the HEMDAG for making predictions. For more details about datasets and methods have a look to section Tutorial.
- Loading the pre-built dataset of HEMDAG
# load the DAG g
> data(graph);
# load the scores matrix S
> data(scores);
# load the annotation matrix L
> data(labels);
# compute the root node
> root <- root.node(g);
- HTD-DAG: Hierarchical Top-Down for DAG
> S.htd <- htd(S, g, root);
- GPAV: Generalized Pool-Adjacent-Violators
> S.gpav <- gpav.over.examples(S, g, W=NULL);
- TPR-DAG (True Path Rule for DAG) and all its 18 ensemble variants
> S.tprTF <- tpr.dag(S, g, root, positive="children", bottomup="threshold.free", topdown="htd");
> S.tprT <- tpr.dag(S, g, root, positive="children", bottomup="threshold", topdown="htd", t=0.5);
> S.tprW <- tpr.dag(S, g, root, positive="children", bottomup="weighted.threshold.free", topdown="htd", w=0.5);
> S.tprWT <- tpr.dag(S, g, root, positive="children", bottomup="weighted.threshold", topdown="htd", t=0.5, w=0.5);
> S.descensTF <- tpr.dag(S, g, root, positive="descendants", bottomup="threshold.free", topdown="htd");
> S.descensT <- tpr.dag(S, g, root, positive="descendants", bottomup="threshold", topdown="htd", t=0.5);
> S.descensW <- tpr.dag(S, g, root, positive="descendants", bottomup="weighted.threshold.free", topdown="htd", w=0.5);
> S.descensWT <- tpr.dag(S, g, root, positive="descendants", bottomup="weighted.threshold", topdown="htd", t=0.5, w=05);
> S.descensTAU <- tpr.dag(S, g, root, positive="descendants", bottomup="tau", topdown="htd", t=0.5);
> S.isotprTF <- tpr.dag(S, g, root, positive="children", bottomup="threshold.free", topdown="gpav");
> S.isotprT <- tpr.dag(S, g, root, positive="children", bottomup="threshold", topdown="gpav", t=0.5);
> S.isotprW <- tpr.dag(S, g, root, positive="children", bottomup="weighted.threshold.free", topdown="gpav", w=0.5);
> S.isotprWT <- tpr.dag(S, g, root, positive="children", bottomup="weighted.threshold", topdown="gpav", t=0.5, w=0.5);
> S.isodescensTF <- tpr.dag(S, g, root, positive="descendants", bottomup="threshold.free", topdown="gpav");
> S.isodescensT <- tpr.dag(S, g, root, positive="descendants", bottomup="threshold", topdown="gpav", t=0.5);
> S.isodescensW <- tpr.dag(S, g, root, positive="descendants", bottomup="weighted.threshold.free", topdown="gpav", w=0.5);
> S.isodescensWT <- tpr.dag(S, g, root, positive="descendants", bottomup="weighted.threshold", topdown="gpav", t=0.5, w=0.5);
> S.isodescensTAU <- tpr.dag(S, g, root, positive="descendants", bottomup="tau", topdown="gpav", t=0.5);
- Obozisnki heuristic methods
> S.max <- obozinski.max(S,g,root);
> S.and <- obozinski.and(S,g,root);
> S.or <- obozinski.or(S,g,root);