penalties.jl 2.31 KiB
using ChainRules: ignore_derivatives
using LinearAlgebra: norm
using Statistics: mean
"""
set_size_penalty(counterfactual_explanation::AbstractCounterfactualExplanation)
Penalty for smooth conformal set size.
"""
function set_size_penalty(
counterfactual_explanation::AbstractCounterfactualExplanation;
κ::Real=0.0, temp::Real=0.05, agg=mean
)
conf_model = counterfactual_explanation.M.model
fitresult = counterfactual_explanation.M.fitresult
X = CounterfactualExplanations.decode_state(counterfactual_explanation)
loss = map(eachslice(X, dims=3)) do x
x = Matrix(x)
if target_probs(counterfactual_explanation, x)[1] >= 0.5
l = ConformalPrediction.smooth_size_loss(
conf_model, fitresult, x';
κ=κ,
temp=temp
)[1]
else
l = 0.0
end
return l
end
loss = agg(loss)
return loss
end
function distance_from_energy(
counterfactual_explanation::AbstractCounterfactualExplanation;
n::Int=100, from_buffer=true, agg=mean, kwargs...
)
conditional_samples = []
ignore_derivatives() do
_dict = counterfactual_explanation.params
if !(:energy_sampler ∈ collect(keys(_dict)))
_dict[:energy_sampler] = CCE.EnergySampler(counterfactual_explanation; kwargs...)
end
sampler = _dict[:energy_sampler]
push!(conditional_samples, rand(sampler, n; from_buffer=from_buffer))
end
x′ = CounterfactualExplanations.counterfactual(counterfactual_explanation)
loss = map(eachslice(x′, dims=3)) do x
x = Matrix(x)
Δ = map(eachcol(conditional_samples[1])) do xsample
norm(x - xsample)
end
return mean(Δ)
end
loss = agg(loss)[1]
return loss
end
function distance_from_targets(
counterfactual_explanation::AbstractCounterfactualExplanation;
n::Int=100, agg=mean
)
target_samples = counterfactual_explanation.data.X |>
X -> X[:,rand(1:end,n)]
x′ = CounterfactualExplanations.counterfactual(counterfactual_explanation) loss = map(eachslice(x′, dims=3)) do x
x = Matrix(x)
Δ = map(eachcol(target_samples)) do xsample
norm(x - xsample)
end
return mean(Δ)
end
loss = agg(loss)[1]
return loss
end