diff --git a/artifacts/results/images/mnist_eccco.png b/artifacts/results/images/mnist_eccco.png
index 217094c975213d0afc36922c9b9d0d1653d1b7d4..4d91b401d4b88aaeaae8660904fe687f128f74b3 100644
Binary files a/artifacts/results/images/mnist_eccco.png and b/artifacts/results/images/mnist_eccco.png differ
diff --git a/artifacts/results/images/mnist_eccco_benchmark.png b/artifacts/results/images/mnist_eccco_benchmark.png
index 9bf388b70297582d15f3accc2b6da83d46e512f1..49b590bc15223b0bf69a600c0648a91cc578c048 100644
Binary files a/artifacts/results/images/mnist_eccco_benchmark.png and b/artifacts/results/images/mnist_eccco_benchmark.png differ
diff --git a/artifacts/results/mnist_vae.jls b/artifacts/results/mnist_vae.jls
index e571c3f2ac2db78575bfa61439e4da7b817bb3f7..b0e32960a27364c9cf284cd5d8409ec363dd5299 100644
Binary files a/artifacts/results/mnist_vae.jls and b/artifacts/results/mnist_vae.jls differ
diff --git a/artifacts/results/mnist_vae_weak.jls b/artifacts/results/mnist_vae_weak.jls
index 5717d5eeb1a7e905354727a7553c701f328a93be..d3db5f46b841781aeaa993c20653bae898dc0aad 100644
Binary files a/artifacts/results/mnist_vae_weak.jls and b/artifacts/results/mnist_vae_weak.jls differ
diff --git a/notebooks/mnist.qmd b/notebooks/mnist.qmd
index e8b52e2313bb4eed9641ade3ceef39b8925664fb..82d559fddefc8f283e66c3366ec4ff6fb9e9f48b 100644
--- a/notebooks/mnist.qmd
+++ b/notebooks/mnist.qmd
@@ -137,7 +137,6 @@ savefig(plt, joinpath(output_images_path, "surrogate_gone_wrong.png"))
```{julia}
function pre_process(x; noise::Float32=0.03f0)
ϵ = Float32.(randn(size(x)) * noise)
- x = @.(2 * x - 1)
x += ϵ
return x
end
@@ -149,7 +148,7 @@ _retrain = false
_regen = false
# Data:
-n_obs = 10000
+n_obs = 1000
counterfactual_data = load_mnist(n_obs)
counterfactual_data.X = pre_process.(counterfactual_data.X)
X, y = CounterfactualExplanations.DataPreprocessing.unpack_data(counterfactual_data)
@@ -187,7 +186,7 @@ sampler = ConditionalSampler(
input_size=(input_dim,),
batch_size=10,
)
-α = [1.0,1.0,1e-2] # penalty strengths
+α = [1.0,1.0,1e-1] # penalty strengths
```
```{julia}
@@ -307,7 +306,6 @@ model_performance = DataFrame()
for (mod_name, mod) in model_dict
# Test performance:
test_data = load_mnist_test()
- test_data.X = pre_process.(test_data.X, noise=0.0f0)
_perf = CounterfactualExplanations.Models.model_evaluation(mod, test_data, measure=collect(values(measure)))
_perf = DataFrame([[p] for p in _perf], collect(keys(measure)))
_perf.mod_name .= mod_name
@@ -321,58 +319,132 @@ model_performance
### Different Models
```{julia}
-plt_order = ["MLP", "MLP Ensemble", "JEM", "JEM Ensemble"]
-
-# ECCCo:
-λ=[0.5,0.1,0.5]
-temp=0.5
-η=0.01
-
-# Generate counterfactuals using ECCCo generator:
-eccco_generator = ECCCoGenerator(
- λ=λ,
- temp=temp,
- opt=Flux.Optimise.Adam(η),
+function _plot_eccco_mnist(
+ x::Union{AbstractArray, Int}=x_factual, target::Int=target;
+ λ=[0.1,0.1,0.1],
+ temp=0.1,η=0.01,
+ plt_order = ["MLP", "MLP Ensemble", "JEM", "JEM Ensemble"],
+ opt = Flux.Optimise.Adam(η),
+ rng::Union{Int,AbstractRNG}=Random.GLOBAL_RNG,
)
-ces = Dict()
-for (mod_name, mod) in model_dict
- ce = generate_counterfactual(
- x_factual, target, counterfactual_data, mod, eccco_generator;
- decision_threshold=γ, max_iter=T,
- initialization=:identity,
- converge_when=:generator_conditions,
+ # Setup:
+ Random.seed!(rng)
+ if x isa Int
+ x = reshape(counterfactual_data.X[:,rand(findall(labels.==x))],input_dim,1)
+ end
+
+ # Generate counterfactuals using ECCCo generator:
+ eccco_generator = ECCCoGenerator(
+ λ=λ,
+ temp=temp,
+ opt=opt,
+ )
+
+ ces = Dict()
+ for (mod_name, mod) in model_dict
+ ce = generate_counterfactual(
+ x, target, counterfactual_data, mod, eccco_generator;
+ decision_threshold=γ, max_iter=T,
+ initialization=:identity,
+ converge_when=:generator_conditions,
+ )
+ ces[mod_name] = ce
+ end
+ _plt_order = map(x -> findall(collect(keys(model_dict)) .== x)[1], plt_order)
+
+ # Plot:
+ p1 = Plots.plot(
+ convert2image(MNIST, reshape(x,28,28)),
+ axis=nothing,
+ size=(img_height, img_height),
+ title="Factual"
)
- ces[mod_name] = ce
+
+ plts = []
+ for (_name,ce) in ces
+ _x = CounterfactualExplanations.counterfactual(ce)
+ _phat = target_probs(ce)
+ _title = "$_name (p̂=$(round(_phat[1]; digits=3)))"
+ plt = Plots.plot(
+ convert2image(MNIST, reshape(_x,28,28)),
+ axis=nothing,
+ size=(img_height, img_height),
+ title=_title
+ )
+ plts = [plts..., plt]
+ end
+ plts = plts[_plt_order]
+ plts = [p1, plts...]
+ plt = Plots.plot(plts...; size=(img_height*length(plts),img_height), layout=(1,length(plts)))
+
+ return plt, eccco_generator
end
-_plt_order = map(x -> findall(collect(keys(model_dict)) .== x)[1], plt_order)
+```
-# Plot:
-p1 = Plots.plot(
- convert2image(MNIST, reshape(x_factual,28,28)),
- axis=nothing,
- size=(img_height, img_height),
- title="Factual"
+```{julia}
+plt, eccco_generator = _plot_eccco_mnist()
+display(plt)
+savefig(plt, joinpath(output_images_path, "mnist_eccco.png"))
+```
+
+### All digits
+
+```{julia}
+function plot_mnist(
+ factual::Int,target::Int;
+ generator::AbstractGenerator,
+ model::AbstractFittedModel=model_dict["JEM Ensemble"],
+ data::CounterfactualData=counterfactual_data,
+ rng::Union{Int,AbstractRNG}=Random.GLOBAL_RNG,
+ _plot_title::Bool=true,
+ kwargs...,
)
+ decision_threshold = !isdefined(kwargs, :decision_threshold) ? 0.9 : decision_threshold
+ max_iter = !isdefined(kwargs, :max_iter) ? 100 : max_iter
+ initialization = !isdefined(kwargs, :initialization) ? :identity : initialization
+ converge_when = !isdefined(kwargs, :converge_when) ? :generator_conditions : converge_when
+
+ x = reshape(data.X[:,rand(findall(predict_label(model, data).==factual))],input_dim,1)
+ ce = generate_counterfactual(
+ x, target, data, model, generator;
+ decision_threshold=decision_threshold, max_iter=max_iter,
+ initialization=initialization,
+ converge_when=converge_when,
+ kwargs...
+ )
+
+ _title = _plot_title ? "$(factual) -> $(target)" : ""
-plts = []
-for (_name,ce) in ces
_x = CounterfactualExplanations.counterfactual(ce)
- _phat = target_probs(ce)
- _title = "$_name (p̂=$(round(_phat[1]; digits=3)))"
plt = Plots.plot(
convert2image(MNIST, reshape(_x,28,28)),
axis=nothing,
size=(img_height, img_height),
title=_title
)
- plts = [plts..., plt]
+ return plt
+end
+```
+
+```{julia}
+if _regen
+ function plot_all_digits(rng=1;verbose=true,kwargs...)
+ plts = []
+ for i in 0:9
+ for j in 0:9
+ @info "Generating counterfactual for $(i) -> $(j)"
+ plt = plot_mnist(i,j;kwargs...,rng=rng)
+ !verbose || display(plt)
+ plts = [plts..., plt]
+ end
+ end
+ plt = Plots.plot(plts...; size=(img_height*10,img_height*10), layout=(10,10))
+ return plt
+ end
+ plt = plot_all_digits(generator=eccco_generator)
+ savefig(plt, joinpath(output_images_path, "mnist_eccco_all_digits.png"))
end
-plts = plts[_plt_order]
-plts = [p1, plts...]
-plt = Plots.plot(plts...; size=(img_height*length(plts),img_height), layout=(1,length(plts)))
-display(plt)
-savefig(plt, joinpath(output_images_path, "mnist_eccco.png"))
```
### Different Generators
@@ -432,21 +504,12 @@ savefig(plt, joinpath(output_images_path, "mnist_eccco_benchmark.png"))
## Benchmark
```{julia}
-# Benchmark generators:
-generators = Dict(
- :wachter => GenericGenerator(opt=opt, λ=l2_λ),
- :revise => REVISEGenerator(opt=opt, λ=l2_λ),
- :greedy => GreedyGenerator(),
-)
-
-# Conformal Models:
-
-
# Measures:
measures = [
CounterfactualExplanations.distance,
ECCCo.distance_from_energy,
ECCCo.distance_from_targets,
CounterfactualExplanations.validity,
+ CounterfactualExplanations.redudancy,
]
```
\ No newline at end of file
diff --git a/paper/paper.pdf b/paper/paper.pdf
index 2859dae90c8dc3dd0fa0be53ee9a0557c3ac9354..b7ffb3b71de08967964800dacafa97d294ab1dfa 100644
Binary files a/paper/paper.pdf and b/paper/paper.pdf differ
diff --git a/src/generator.jl b/src/generator.jl
index 72952aa31de89bf7d1a8d297a8337abe97cf9a16..87b1dceff3e45e92075efdfb5a69f614eb15e2af 100644
--- a/src/generator.jl
+++ b/src/generator.jl
@@ -28,7 +28,7 @@ function ECCCoGenerator(;
function _set_size_penalty(ce::AbstractCounterfactualExplanation)
return ECCCo.set_size_penalty(ce; κ=κ, temp=temp)
end
- _penalties = [Objectives.distance_l2, _set_size_penalty, ECCCo.distance_from_energy]
+ _penalties = [Objectives.distance_l1, _set_size_penalty, ECCCo.distance_from_energy]
λ = λ isa AbstractFloat ? [0.0, λ, λ] : λ
return Generator(; penalty=_penalties, λ=λ, opt=opt, kwargs...)
end
diff --git a/src/penalties.jl b/src/penalties.jl
index 19f06ae48de244bc691df38268ec92322554b139..92b007d06e219bf2bb3b69952d286619a0c5f8a7 100644
--- a/src/penalties.jl
+++ b/src/penalties.jl
@@ -1,5 +1,6 @@
using ChainRules: ignore_derivatives
using Distances
+using Flux
using LinearAlgebra: norm
using Statistics: mean
@@ -37,7 +38,7 @@ end
function distance_from_energy(
ce::AbstractCounterfactualExplanation;
- n::Int=10, niter=250, from_buffer=true, agg=mean, kwargs...
+ n::Int=10, niter=100, from_buffer=true, agg=mean, kwargs...
)
conditional_samples = []
ignore_derivatives() do
@@ -46,7 +47,7 @@ function distance_from_energy(
_dict[:energy_sampler] = ECCCo.EnergySampler(ce; niter=niter, nsamples=n, kwargs...)
end
sampler = _dict[:energy_sampler]
- push!(conditional_samples, rand(sampler, n; from_buffer=from_buffer))
+ push!(conditional_samples, rand(sampler, 100; from_buffer=from_buffer))
end
x′ = CounterfactualExplanations.counterfactual(ce)
loss = map(eachslice(x′, dims=ndims(x′))) do x