Warning: This repository is deprecated. All functionalities have been moved to (a package extension of) SoleModels.jl. Please refer to that repository for continued support and updates.

SoleDecisionTreeInterface.jl

Ever wondered what to do with a trained decision tree? Start by inspecting its knowledge, and end up evaluating it in a dedicated framework! This package allows you to convert learned DecisionTree models to Sole decision tree models. With a Sole model in your hand, you can then treat the extracted knowledge in symbolic form, that is, as a set of logical formulas, which allows you to: - Evaluate them in terms of + accuracy (e.g., confidence, lift), + relevance (e.g., support), + interpretability (e.g., syntax height, number of atoms); - Modify them; - Merge them.

Usage

Converting to a Sole model

```julia using MLJ using MLJDecisionTreeInterface using DataFrames

X, y = @load_iris X = DataFrame(X)

train, test = partition(eachindex(y), 0.8, shuffle=true); Xtrain, ytrain = X[train, :], y[train]; Xtest, ytest = X[test, :], y[test];

Train a model

learneddttree = begin Tree = MLJ.@load DecisionTreeClassifier pkg=DecisionTree model = Tree(maxdepth=-1, ) mach = machine(model, Xtrain, ytrain) fit!(mach) fittedparams(mach).tree end

using SoleDecisionTreeInterface

Convert to Sole model

soledt = solemodel(learneddt_tree) ```

Model inspection & rule study

```julia-repl julia> using Sole;

julia> # Make test instances flow into the model, so that test metrics can, then, be computed. apply!(soledt, Xtest, y_test);

julia> # Print Sole model printmodel(soledt; showmetrics = true); ▣ V4 < 0.8 ├✔ setosa : (ninstances = 7, ncovered = 7, confidence = 1.0, lift = 1.0) └✘ V3 < 4.95 ├✔ V4 < 1.65 │├✔ versicolor : (ninstances = 10, ncovered = 10, confidence = 1.0, lift = 1.0) │└✘ V2 < 3.1 │ ├✔ virginica : (ninstances = 2, ncovered = 2, confidence = 1.0, lift = 1.0) │ └✘ versicolor : (ninstances = 0, ncovered = 0, confidence = NaN, lift = NaN) └✘ V3 < 5.05 ├✔ V1 < 6.5 │├✔ virginica : (ninstances = 0, ncovered = 0, confidence = NaN, lift = NaN) │└✘ versicolor : (ninstances = 0, ncovered = 0, confidence = NaN, lift = NaN) └✘ virginica : (ninstances = 11, ncovered = 11, confidence = 0.91, lift = 1.0)

julia> # Extract rules that are at least as good as a random baseline model interestingrules = listrules(soledt, minlift = 1.0, minninstances = 0);

julia> printmodel.(interestingrules; showmetrics = true); ▣ (V4 < 0.8) ∧ (⊤) ↣ setosa : (ninstances = 30, ncovered = 7, coverage = 0.23, confidence = 1.0, natoms = 1, lift = 4.29) ▣ (¬(V4 < 0.8)) ∧ (V3 < 4.95) ∧ (V4 < 1.65) ∧ (⊤) ↣ versicolor : (ninstances = 30, ncovered = 10, coverage = 0.33, confidence = 1.0, natoms = 3, lift = 2.73) ▣ (¬(V4 < 0.8)) ∧ (V3 < 4.95) ∧ (¬(V4 < 1.65)) ∧ (V2 < 3.1) ∧ (⊤) ↣ virginica : (ninstances = 30, ncovered = 2, coverage = 0.07, confidence = 1.0, natoms = 4, lift = 2.5) ▣ (¬(V4 < 0.8)) ∧ (¬(V3 < 4.95)) ∧ (¬(V3 < 5.05)) ∧ (⊤) ↣ virginica : (ninstances = 30, ncovered = 11, coverage = 0.37, confidence = 0.91, natoms = 3, lift = 2.27)

julia> # Simplify rules while extracting and prettify result interestingrules = listrules(soledt, minlift = 1.0, minninstances = 0, normalize = true);

julia> printmodel.(interestingrules; showmetrics = true, syntaxstringkwargs = (; thresholddigits = 2)); ▣ V4 < 0.8 ↣ setosa : (ninstances = 30, ncovered = 7, coverage = 0.23, confidence = 1.0, natoms = 1, lift = 4.29) ▣ (V4 ∈ [0.8,1.65)) ∧ (V3 < 4.95) ↣ versicolor : (ninstances = 30, ncovered = 10, coverage = 0.33, confidence = 1.0, natoms = 2, lift = 2.73) ▣ (V4 ≥ 1.65) ∧ (V3 < 4.95) ∧ (V2 < 3.1) ↣ virginica : (ninstances = 30, ncovered = 2, coverage = 0.07, confidence = 1.0, natoms = 3, lift = 2.5) ▣ (V4 ≥ 0.8) ∧ (V3 ≥ 5.05) ↣ virginica : (ninstances = 30, ncovered = 11, coverage = 0.37, confidence = 0.91, natoms = 2, lift = 2.27)

julia> # Directly access rule metrics readmetrics.(listrules(soledt; minlift=1.0, min_ninstances = 0)) 4-element Vector{NamedTuple{(:ninstances, :ncovered, :coverage, :confidence, :natoms, :lift), Tuple{Int64, Int64, Float64, Float64, Int64, Float64}}}: (ninstances = 30, ncovered = 7, coverage = 0.23333333333333334, confidence = 1.0, natoms = 1, lift = 4.285714285714286) (ninstances = 30, ncovered = 10, coverage = 0.3333333333333333, confidence = 1.0, natoms = 3, lift = 2.7272727272727275) (ninstances = 30, ncovered = 2, coverage = 0.06666666666666667, confidence = 1.0, natoms = 4, lift = 2.5) (ninstances = 30, ncovered = 11, coverage = 0.36666666666666664, confidence = 0.9090909090909091, natoms = 3, lift = 2.2727272727272725)

julia> # Show rules with an additional metric (syntax height of the rule's antecedent) printmodel.(sort(interestingrules, by = readmetrics); showmetrics = (; rounddigits = nothing, additionalmetrics = (; height = r->SoleLogics.height(antecedent(r)))));

▣ (V4 ≥ 1.65) ∧ (V3 < 4.95) ∧ (V2 < 3.1) ↣ virginica : (ninstances = 30, ncovered = 2, coverage = 0.06666666666666667, confidence = 1.0, height = 2, lift = 2.5) ▣ V4 < 0.8 ↣ setosa : (ninstances = 30, ncovered = 7, coverage = 0.23333333333333334, confidence = 1.0, height = 0, lift = 4.285714285714286) ▣ (V4 ∈ [0.8,1.65)) ∧ (V3 < 4.95) ↣ versicolor : (ninstances = 30, ncovered = 10, coverage = 0.3333333333333333, confidence = 1.0, height = 1, lift = 2.7272727272727275) ▣ (V4 ≥ 0.8) ∧ (V3 ≥ 5.05) ↣ virginica : (ninstances = 30, ncovered = 11, coverage = 0.36666666666666664, confidence = 0.9090909090909091, height = 1, lift = 2.2727272727272725)

julia> # Pretty table of rules and their metrics metricstable(interestingrules; metricskwargs = (; rounddigits = nothing, additionalmetrics = (; height = r->SoleLogics.height(antecedent(r))))) ┌────────────────────────────────────────┬────────────┬────────────┬──────────┬───────────┬────────────┬────────┬─────────┐ │ Antecedent │ Consequent │ ninstances │ ncovered │ coverage │ confidence │ height │ lift │ ├────────────────────────────────────────┼────────────┼────────────┼──────────┼───────────┼────────────┼────────┼─────────┤ │ V4 < 0.8 │ setosa │ 30 │ 7 │ 0.233333 │ 1.0 │ 0 │ 4.28571 │ │ (V4 ∈ [0.8,1.65)) ∧ (V3 < 4.95) │ versicolor │ 30 │ 10 │ 0.333333 │ 1.0 │ 1 │ 2.72727 │ │ (V4 ≥ 1.65) ∧ (V3 < 4.95) ∧ (V2 < 3.1) │ virginica │ 30 │ 2 │ 0.0666667 │ 1.0 │ 2 │ 2.5 │ │ (V4 ≥ 0.8) ∧ (V3 ≥ 5.05) │ virginica │ 30 │ 11 │ 0.366667 │ 0.909091 │ 1 │ 2.27273 │ └────────────────────────────────────────┴────────────┴────────────┴──────────┴───────────┴────────────┴────────┴─────────┘ ```