Jireh Huang (jirehhuang@ucla.edu)
This package implements the Bayesian network structure learning algorithms developed in Huang and Zhou (2022), interfacing with the R package bnlearn. In particular, this package features the partitioned PC (pPC), p-value adjacency thresholding (PATH), and hybrid greedy initialization (HGI) algorithms, which culminate in the partitioned hybrid greedy search (pHGS) algorithm.
Run the following code to install the package from GitHub.
devtools::install_github("jirehhuang/phsl")The pPC, PATH, and HGI algorithms are integrated into the bnsl()
function, which is a mix-and-match Bayesian network structure learning
function modeled after the rsmax2() function from the
bnlearn package. The pPC algorithm is
available as a constraint-based algorithm in the restrict argument,
PATH may be specified by the path argument when restrict = "ppc",
and HGI may be activated by the hgi argument for constraint-based and
hybrid approaches. See help(bnsl) for more details and examples.
Wrapper functions ppc() and phgs() contain presets to implement pPC
with PATH and pHGS, respectively.
Up to 1200 copies of the CANCER network were tiled to obtain the 12 networks with numbers of nodes and edges shown in the following table.
| p | |E| |
|---|---|
| 5 | 4 |
| 25 | 27 |
| 50 | 56 |
| 125 | 140 |
| 250 | 273 |
| 500 | 558 |
| 1000 | 1088 |
| 2000 | 2249 |
| 3000 | 3342 |
| 4000 | 4467 |
| 5000 | 5638 |
| 6000 | 6653 |
Five datasets were generated for each network configuration with
n = 1000 samples, and the following algorithms were executed on these
datasets with alpha = 0.05 and max.sx = 3. bnlearn::pc.stable(...)
is the bnlearn implementation of PC(-stable),
phsl::ppc(..., max_groups = 1) is the phsl version by executing pPC
with κ = 1, and phsl::ppc(...) is the pPC algorithm. The timing
results are shown in the following figure.
While pPC and PC in phsl executed on the datasets with p = 6000 variables in approximately 80 minutes and 13 hours, respectively, the bnlearn implementation of PC struggled to execute on even 2000 variables, requiring over 50 hours, over 850 times slower than pPC.
Please cite the following paper when using any part of this package, modified or as is.
This package relies heavily on and borrows functionalities from the bnlearn package.