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.
using MLJ
using MLJDecisionTreeInterface
using DataFrames
X, y = @load_iris
X = DataFrame(X)
train, test = partition(eachindex(y), 0.8, shuffle=true);
X_train, y_train = X[train, :], y[train];
X_test, y_test = X[test, :], y[test];
# Train a model
learned_dt_tree = begin
Tree = MLJ.@load DecisionTreeClassifier pkg=DecisionTree
model = Tree(max_depth=-1, )
mach = machine(model, X_train, y_train)
fit!(mach)
fitted_params(mach).tree
end
using SoleDecisionTreeInterface
# Convert to Sole model
sole_dt = solemodel(learned_dt_tree)julia> using Sole;
julia> # Make test instances flow into the model, so that test metrics can, then, be computed.
apply!(sole_dt, X_test, y_test);
julia> # Print Sole model
printmodel(sole_dt; show_metrics = 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
interesting_rules = listrules(sole_dt, min_lift = 1.0, min_ninstances = 0);
julia> printmodel.(interesting_rules; show_metrics = 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
interesting_rules = listrules(sole_dt, min_lift = 1.0, min_ninstances = 0, normalize = true);
julia> printmodel.(interesting_rules; show_metrics = true, syntaxstring_kwargs = (; threshold_digits = 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(sole_dt; min_lift=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(interesting_rules, by = readmetrics); show_metrics = (; round_digits = nothing, additional_metrics = (; 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(interesting_rules; metrics_kwargs = (; round_digits = nothing, additional_metrics = (; 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 │
└────────────────────────────────────────┴────────────┴────────────┴──────────┴───────────┴────────────┴────────┴─────────┘