ModalAssociationRules.jl

Association rules in Julia!

Usage

Creation of a Miner object.

using ModalAssociationRules
using SoleData

# load a sample dataset (NATOPS)
# and transform it into a scalar logiset.
X_df, y = load_NATOPS()
X = scalarlogiset(X_df[1:30, :])

# focus on just the first three variables;
# define a collection of Items, that is, 
# an alphabet of propositional letters (propositions)
# and modal literals. 
p = Atom(ScalarCondition(VariableMin(1), >, -0.5))
q = Atom(ScalarCondition(VariableMin(2), <=, -2.2))
r = Atom(ScalarCondition(VariableMin(3), >, -3.6))

lp = box(IA_L)(p)
lq = diamond(IA_L)(q)
lr = box(IA_L)(r)

items = Vector{Item}([p, q, r, lp, lq, lr])

# define which measures to use to establish the interestingness
# of both itemsets (groups of items) and association rules;
# also define a minimum threshold that must be surpassed both 
# locally, inside an instance, and globally across all instances.

# 0.1 is the local minsup, while 0.2 is the global minsup.
itemsetmeasures = [(gsupport, 0.1, 0.2)]
# 0.3 is the local minconfidence, while 0.5 is the global one.
arulemeasures = [(gconfidence, 0.3, 0.5)]

# choose an association rule mining algorithm, like fpgrowth;
# we can finally define a Miner machine.
miner = Miner(X, fpgrowth, items, itemsetmeasures, arulemeasures)

Miner execution and results retrieval.

mine!(miner)
mined_itemsets = freqitems(miner)
mined_arules = arules(miner)

We can create more complex Miner objects by specifying three kinds of policies, which define the properties that must be satisfied by X's worlds, the Itemsets collected during the mining, and the final ARules.

miner = Miner(
    X
    fpgrowth,
    items,
    itemsetmeasures,
    arulemeasures,

    # we specify a condition that the worlds of the logiset X must honor
    worldfilter=SoleLogics.FunctionalWorldFilter(
        x -> length(x) <= 10, Interval{Int}
    ),

    # an itemset is considered meaningful if it also honors specific condiitons
    itemset_policies=[islimited_length_itemset(; maxlength=5)],

    # similarly, for the association rules extracted
    arule_policies=[
        islimited_length_arule(; antecedent_maxlength=5),
        isanchored_arule(; npropositions=1),
        isheterogeneous_arule(; antecedent_nrepetitions=1, consequent_nrepetitions=0),
    ],
)

Compilation (development dependencies)

This package heavily depends on the Sole ecosystem.

To compile this package while referencing the dev branches of the other necessary Sole packages, clone and instantiate them in a common folder, following the steps below.

git clone https://github.com/aclai-lab/SoleBase.jl.git
git clone https://github.com/aclai-lab/MultiData.jl.git
git clone https://github.com/aclai-lab/SoleLogics.jl.git
git clone https://github.com/aclai-lab/SoleData.jl.git
git clone https://github.com/aclai-lab/SoleModels.jl.git
git clone https://github.com/aclai-lab/ModalAssociationRules.jl.git

For each folder, checkout on dev branch and open the Julia REPL to install the dependencies associated:

SoleBase              -> ]instantiate
MultiData             -> ]dev SoleBase
SoleLogics            -> ]dev SoleBase
SoleData              -> ]dev SoleBase SoleLogics MultiData
SoleModels            -> ]dev SoleBase MultiData SoleLogics SoleData
ModalAssociationRules -> ]dev SoleBase MultiData SoleLogics SoleData SoleModels

When not specified (every time but in SoleBase), also execute ]instantiate.