Bounded Tree-width Bayesian Networks Learner

btbn is a tool for learning bounded tree-width Bayesian networks

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Installation and usage

Get, install and test:

	go get -u github.com/britojr/btbn...
	go install github.com/britojr/btbn...
	go test github.com/britojr/btbn... -cover

Usage:

	btbn --help
	Usage: btbn <command> [options]

	Commands:

		struct		run bounded tree-width structure learning algorithm
		mutinf		compute pairwise mutual information

	Usage of struct command:

		-a			structure learning algorithm {sample|selected|guided|iterative}
		-b			network output file (ex: example.bnet)
		-i			max number of iterations (0 means unbounded) (default 1)
		-p			parameters file (ex: example-parms.yaml)
		-s			precomputed parent set scores file (ex: example.pss)
		-t			available time to search for solution (0 means unbounded)
		-v=true		prints detailed steps (default true)

	Usage of mutinf command:

		-d			dataset file (ex: example.csv)
		-o			mutual information output file (ex: example.mi)
		-v=true		prints detailed steps (default true)

Examples:

Learn bounded tree-width bn using iterative greedy search for 2 secs. Save resulting networnk in 'example.bnet':

cd examples/
btbn struct -i 0 -t 2 -s example.pss -p example-parms.yaml -a iterative -b example.bnet

Compute mutual information:

btbn mutinf -d example.csv -o example.mi

Parameters file:

YAML file containing specific parameters for the structure learning algorithms

Parameters file fields:

treewidth:		structure's tree-width upper bound 
num_trees:		number of ktrees to sample and sort (used on selected strategy)
mutual_info:		precomputed mutual information file (used on selected strategy)
search_variation:	iterative search type (greedy or astar)

Parameters file example:

./examples/example-parms.yaml
treewidth: 4
num_trees: 100
mutual_info: example.mi
search_variation: greedy