[Breaking] Rename confusing terms such as `Split`

[rikhuijzer]

This PR renames Split to SubClause and TreePath to Clause. In turn, this renaming makes things much more intuitive since it sticks to the end-result terms instead of internal representation (decision tree) terms. Also, the Split datastructure is finally fixed to directly contain the split value and feature instead of having those in a nested data structure called SplitPoint.

[rikhuijzer]

Will take the new API logic out and save it for a next PR.

src/extract.jl:

"Estimate the importance of a rule."
function _rule_importance(weight::Number, rule::Rule)
    importance = 0.0
    thens = rule.then::Vector{Float64}
    otherwises = rule.otherwise::Vector{Float64}
    for (then, otherwise) in zip(thens, otherwises)
        importance += weight * abs(then - otherwise)
    end
    return importance
end

"""
    feature_importance(
        model::StableRules,
        feature_name::AbstractString
    )

Estimate the importance of the given `feature_name`.
The aim of this function is to satisfy the following property:

> Given two features X and Y, if X has more effect on the outcome, then
> feature_importance(model, X) > feature_importance(model, Y).

This function provides only an estimation of the importance because
the effect on the outcome depends on the data.
"""
function feature_importance(
        model::StableRules,
        feature_name::String
    )
    importance = 0.0
    for (i, rule) in enumerate(model.rules)
        for subclause::SubClause in _subclauses(rule)
            if _feature_name(subclause)::String == feature_name
                weight = model.weights[i]
                importance += _rule_importance(weight, rule)
            end
        end
    end
    return importance
end

function feature_importance(model::StableRules, feature_name::AbstractString)
    return feature_importance(model, string(feature_name)::String)
end

and test/extract.jl:

function _haberman_data()
    df = haberman()
    X = MLJBase.table(MLJBase.matrix(df[:, Not(:survival)]))
    y = categorical(df.survival)
    (X, y)
end

X, y = _haberman_data()

classifier = StableRulesClassifier(; max_depth=2, max_rules=8, n_trees=1000, rng=_rng())
mach = machine(classifier, X, y)
fit!(mach)

model = mach.fitresult::StableRules

importance = SIRUS.feature_importance(model, "x1")