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‎.gitignore‎

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*.jl.cov
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*.jl.*.cov
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*.jl.mem
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deps/deps.jl

‎LICENSE‎

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MIT License
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Copyright (c) 2019 Martin Trapp
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎README.md‎

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# Bayesian Sum-Product Networks
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This Julia package implements Bayesian Sum-Product Networks and infinite mixtures of Bayesian Sum-Product Networks.
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Besides all necessary core routines, this package additionally implements approximate Bayesian inference using a combination of ancestral and Gibbs sampling as well as an implementation of the distributed slice sampler for infinite mixtures of SPNs.
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Note that this package requires Julia version >= 1.0.
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To install the package and run the provided code, make sure to first install Julia from [JuliaLang.org](https://julialang.org/downloads/).
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After starting Julia, you can install the package using Julia's internal package manager.
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To do so, press `]` within the Julia command line to switch to the package manager prompt. You can leaf the package manager by pressing `BACKSPACE`.
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To install the package, run the following command:
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```julia
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pkg> add https://github.com/trappmartin/BayesianSumProductNetworks.git
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```
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### Dataset and Predictions
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All dataset and predictions can be found under: [download](https://github.com/trappmartin/BayesianSumProductNetworks/releases/download/v1.0.0/data_predictions_scripts.tar)
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### Running Experiments
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To run the experiments, start the respective shell script located in the `hpc` folder. Those scripts are written such that they can be used as master scripts for slurm jobs.
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### API
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All types and functions listed here contain doc-strings. Therefore, if you are interest in more details please about the use of those functions/types please use Julia's internal documentation system. Therefore, press `?` within the Julia command line and then enter the name of the function/type you want to know more about.
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The package implements Bayesian Sum-Product Networks using the following types:
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```julia
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RegionGraphNode{<:Real} <: AbstractRegionGraphNode
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PartitionGraphNode <: AbstractRegionGraphNode
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FactorizedDistributionGraphNode <: AbstractRegionGraphNode
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FactorizedMixtureGraphNode <: AbstractRegionGraphNode
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InfiniteSumNode
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NormalInverseGamma <: Distribution
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DirichletSufficientStats <: AbstractSufficientStats
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NormalInvGammaSufficientStats <: AbstractSufficientStats
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GammaSufficientStats <: AbstractSufficientStats
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BetaSufficientStats <: AbstractSufficientStats
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```
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For Bayesian inference the following functions are implemented:
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```julia
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randomscopes!
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templateRegion
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templatePartition
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ancestralsampling!
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gibbssamplescopes!
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slicesample!
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logpdf
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logpdf!
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logmllh!
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```

‎REQUIRE‎

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julia 1.0
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Distributions 0.16.0
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AxisArrays
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Reexport
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SpecialFunctions
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StatsFuns
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StatsBase

‎hpc/run_abda_infty.sh‎

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#!/bin/bash
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# SETTINGS
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datasets=("abalone" "adult" "australian" "autism" "breast" "chess" "crx" "dermatology" "diabetes" "german" "student" "wine")
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ksums=(5 10)
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kleafs=(5 10)
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layers=(2 4)
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partitions=(2 4 8)
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chains=(1)
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threads=8
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iterations=10000
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numsamples=10000
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burnin=5000
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homedir="/home/"
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resultsdir="/PATH/TO/results_abda_infty"
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datadir="/PATH/TO/abda"
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codedir="/PATH/TO/BayesianSumProductNetworks"
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# Actual code
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# cd to code
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cd ${codedir}
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# ensure we use threads
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export JULIA_NUM_THREADS=${threads}
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# Loop over datasets and configurations...
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for chain in ${chains[@]}; do
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for ksum in ${ksums[@]}; do
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for kleaf in ${kleafs[@]}; do
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for layer in ${layers[@]}; do
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for partition in ${partitions[@]}; do
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for dataset in ${datasets[@]}; do
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echo "Trying (abda): Ksum ${ksum} L ${layer} Kleaf ${kleaf} J ${partition} chain ${chain} dataset ${dataset}"
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if [ "${kleaf}" -ge "${ksum}" ]; then
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OUTDIR="${resultsdir}/${dataset}/${ksum}_${kleaf}_${layer}_${partition}_${chain}"
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julia scripts/run_abda_infty.jl --Ksum ${ksum} --L ${layer} --Kleaf ${kleaf} --J ${partition} --chain ${chain} --numsamples ${numsamples} ${dataset} ${datadir} ${OUTDIR} ${burnin} ${iterations}
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else
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echo "skipping"
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fi
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done
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done
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done
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done
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done
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done

‎hpc/run_abda_rg.sh‎

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#!/bin/bash
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# SETTINGS
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datasets=("abalone" "adult" "australian" "autism" "breast" "chess" "crx" "dermatology" "diabetes" "german" "student" "wine")
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ksums=(5 10)
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kleafs=(5 10)
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layers=(2 4)
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partitions=(2 4 8)
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chains=(1)
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threads=8
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iterations=10000
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numsamples=10000
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burnin=5000
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homedir="/home/"
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resultsdir="/PATH/TO/results_abda_rg"
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datadir="/PATH/TO/abda"
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codedir="/PATH/TO/BayesianSumProductNetworks"
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# Actual code
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# cd to code
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cd ${codedir}
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# ensure we use threads
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export JULIA_NUM_THREADS=${threads}
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# Loop over datasets and configurations...
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for chain in ${chains[@]}; do
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for ksum in ${ksums[@]}; do
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for kleaf in ${kleafs[@]}; do
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for layer in ${layers[@]}; do
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for partition in ${partitions[@]}; do
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for dataset in ${datasets[@]}; do
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echo "Trying (abda): Ksum ${ksum} L ${layer} Kleaf ${kleaf} J ${partition} chain ${chain} dataset ${dataset}"
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if [ "${kleaf}" -ge "${ksum}" ]; then
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OUTDIR="${resultsdir}/${dataset}/${ksum}_${kleaf}_${layer}_${partition}_${chain}"
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julia scripts/run_abda_rg.jl --Ksum ${ksum} --L ${layer} --Kleaf ${kleaf} --J ${partition} --chain ${chain} --numsamples ${numsamples} ${dataset} ${datadir} ${OUTDIR} ${burnin} ${iterations}
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else
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echo "skipping"
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fi
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done
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done
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done
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done
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done
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done

‎hpc/run_discrete_infty.sh‎

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#!/bin/bash
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# SETTINGS
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datasets=("accidents" "ad" "baudio" "bbc" "bnetflix" "book" "c20ng" "cr52" "cwebkb" "dna" "jester" "kdd" "kosarek" "msnbc" "msweb" "nltcs" "plants" "pumsb_star" "tmovie" "tretail")
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ksums=(5 10)
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kleafs=(5 10)
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layers=(2 4)
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partitions=(2 4 8)
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chains=(1)
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threads=10
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iterations=10000
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numsamples=10000
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burnin=5000
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homedir="/home/"
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resultsdir="/PATH/TO/results_infty"
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datadir="/PATH/TO/discrete"
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codedir="/PATH/TO/BayesianSumProductNetworks"
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# Actual code
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# cd to code
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cd ${codedir}
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# ensure we use threads
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export JULIA_NUM_THREADS=${threads}
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# Loop over datasets and configurations...
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for chain in ${chains[@]}; do
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for ksum in ${ksums[@]}; do
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for kleaf in ${kleafs[@]}; do
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for layer in ${layers[@]}; do
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for partition in ${partitions[@]}; do
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for dataset in ${datasets[@]}; do
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echo "Trying (infty): Ksum ${ksum} L ${layer} Kleaf ${kleaf} J ${partition} chain ${chain} dataset ${dataset}"
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if [ "${kleaf}" -ge "${ksum}" ]; then
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OUTDIR="${resultsdir}/${dataset}/${ksum}_${kleaf}_${layer}_${partition}_${chain}"
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julia scripts/run_discrete_rg_infty.jl --Ksum ${ksum} --L ${layer} --Kleaf ${kleaf} --J ${partition} --chain ${chain} --numsamples ${numsamples} ${dataset} ${datadir} ${OUTDIR} ${burnin} ${iterations}
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else
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echo "skipping"
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fi
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done
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done
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done
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done
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done
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done

‎hpc/run_discrete_rg.sh‎

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#!/bin/bash
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# SETTINGS
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datasets=("accidents" "ad" "baudio" "bbc" "bnetflix" "book" "c20ng" "cr52" "cwebkb" "dna" "jester" "kdd" "kosarek" "msnbc" "msweb" "nltcs" "plants" "pumsb_star" "tmovie" "tretail")
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ksums=(5 10)
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kleafs=(5 10)
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layers=(2 4)
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partitions=(2 4 8)
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chains=(1)
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threads=10
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iterations=10000
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numsamples=10000
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burnin=5000
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homedir="/home/"
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resultsdir="/PATH/TO/results_rg"
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datadir="/PATH/TO/discrete"
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codedir="/PATH/TO/BayesianSumProductNetworks"
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# Actual code
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# cd to code
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cd ${codedir}
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# ensure we use threads
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export JULIA_NUM_THREADS=${threads}
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# Loop over datasets and configurations...
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for chain in ${chains[@]}; do
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for ksum in ${ksums[@]}; do
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for kleaf in ${kleafs[@]}; do
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for layer in ${layers[@]}; do
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for partition in ${partitions[@]}; do
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for dataset in ${datasets[@]}; do
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echo "Trying: Ksum ${ksum} L ${layer} Kleaf ${kleaf} J ${partition} chain ${chain} dataset ${dataset}"
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if [ "${kleaf}" -ge "${ksum}" ]; then
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OUTDIR="${resultsdir}/${dataset}/${ksum}_${kleaf}_${layer}_${partition}_${chain}"
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julia scripts/run_discrete_rg.jl --Ksum ${ksum} --L ${layer} --Kleaf ${kleaf} --J ${partition} --chain ${chain} --numsamples ${numsamples} ${dataset} ${datadir} ${OUTDIR} ${burnin} ${iterations}
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else
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echo "skipping"
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fi
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done
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done
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done
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done
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done
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done

‎hpc/run_missing.sh‎

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#!/bin/bash
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# SETTINGS
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datasets=("bbc" "cwebkb" "tmovie")
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ksums=(5)
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kleafs=(5)
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layers=(2)
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partitions=(2)
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chains=(1)
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setups=("20_missing" "40_missing" "60_missing" "80_missing")
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threads=10
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iterations=5000
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numsamples=2500
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burnin=1000
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homedir="/home/"
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resultsdir="/PATH/TO/results_missing/"
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datadir="/PATH/TO/discrete_missing/"
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codedir="/PATH/TO/BayesianSumProductNetworks"
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# Actual code
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# cd to code
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cd ${codedir}
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# ensure we use threads
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export JULIA_NUM_THREADS=${threads}
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# Loop over datasets and configurations...
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for chain in ${chains[@]}; do
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for setup in ${setups[@]}; do
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for ksum in ${ksums[@]}; do
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for kleaf in ${kleafs[@]}; do
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for layer in ${layers[@]}; do
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for partition in ${partitions[@]}; do
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for dataset in ${datasets[@]}; do
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echo "Trying: Ksum ${ksum} L ${layer} Kleaf ${kleaf} J ${partition} chain ${chain} dataset ${dataset}"
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OUTDIR="${resultsdir}/${setup}/${dataset}/${ksum}_${kleaf}_${layer}_${partition}_${chain}"
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DATA="${datadir}/${setup}"
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julia scripts/run_discrete_missing_rg.jl --Ksum ${ksum} --L ${layer} --Kleaf ${kleaf} --J ${partition} --chain ${chain} --numsamples ${numsamples} ${dataset} ${DATA} ${OUTDIR} ${burnin} ${iterations}
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done
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done
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done
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done
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done
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done
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done

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