From a4ef7395fea15e20e9a1905d858699aed5c74017 Mon Sep 17 00:00:00 2001
From: Pat Alt <55311242+pat-alt@users.noreply.github.com>
Date: Wed, 19 Apr 2023 17:15:27 +0200
Subject: [PATCH] batchnorm sorted
---
Manifest.toml | 2 +-
docs/Manifest.toml | 7 ++++
docs/Project.toml | 1 +
notebooks/Manifest.toml | 2 +-
notebooks/mnist.qmd | 85 ++++++++++++++++++++++++++--------------
src/model.jl | 1 +
src/penalties.jl | 32 +++++++--------
www/cce_mnist.png | Bin 7586 -> 15731 bytes
8 files changed, 82 insertions(+), 48 deletions(-)
create mode 100644 docs/Manifest.toml
create mode 100644 docs/Project.toml
diff --git a/Manifest.toml b/Manifest.toml
index d16a0962..4531e744 100644
--- a/Manifest.toml
+++ b/Manifest.toml
@@ -304,7 +304,7 @@ uuid = "d38c429a-6771-53c6-b99e-75d170b6e991"
version = "0.6.2"
[[deps.CounterfactualExplanations]]
-deps = ["CSV", "CUDA", "CategoricalArrays", "DataFrames", "Flux", "LaplaceRedux", "LazyArtifacts", "LinearAlgebra", "MLDatasets", "MLJBase", "MLJModels", "MLUtils", "MultivariateStats", "NearestNeighborModels", "Parameters", "Plots", "ProgressMeter", "Random", "Serialization", "SliceMap", "SnoopPrecompile", "Statistics", "StatsBase", "Tables", "UMAP"]
+deps = ["CSV", "CUDA", "CategoricalArrays", "ChainRulesCore", "DataFrames", "Flux", "LaplaceRedux", "LazyArtifacts", "LinearAlgebra", "MLDatasets", "MLJBase", "MLJModels", "MLUtils", "MultivariateStats", "NearestNeighborModels", "Parameters", "Plots", "ProgressMeter", "Random", "Serialization", "SliceMap", "SnoopPrecompile", "Statistics", "StatsBase", "Tables", "UMAP"]
path = "../CounterfactualExplanations.jl"
uuid = "2f13d31b-18db-44c1-bc43-ebaf2cff0be0"
version = "0.1.9"
diff --git a/docs/Manifest.toml b/docs/Manifest.toml
new file mode 100644
index 00000000..bc62edc1
--- /dev/null
+++ b/docs/Manifest.toml
@@ -0,0 +1,7 @@
+# This file is machine-generated - editing it directly is not advised
+
+julia_version = "1.8.5"
+manifest_format = "2.0"
+project_hash = "da39a3ee5e6b4b0d3255bfef95601890afd80709"
+
+[deps]
diff --git a/docs/Project.toml b/docs/Project.toml
new file mode 100644
index 00000000..81648c0b
--- /dev/null
+++ b/docs/Project.toml
@@ -0,0 +1 @@
+[deps]
diff --git a/notebooks/Manifest.toml b/notebooks/Manifest.toml
index 6dd7ba4c..a1c318aa 100644
--- a/notebooks/Manifest.toml
+++ b/notebooks/Manifest.toml
@@ -383,7 +383,7 @@ uuid = "150eb455-5306-5404-9cee-2592286d6298"
version = "0.6.2"
[[deps.CounterfactualExplanations]]
-deps = ["CSV", "CUDA", "CategoricalArrays", "DataFrames", "Flux", "LaplaceRedux", "LazyArtifacts", "LinearAlgebra", "MLDatasets", "MLJBase", "MLJModels", "MLUtils", "MultivariateStats", "NearestNeighborModels", "Parameters", "Plots", "ProgressMeter", "Random", "Serialization", "SliceMap", "SnoopPrecompile", "Statistics", "StatsBase", "Tables", "UMAP"]
+deps = ["CSV", "CUDA", "CategoricalArrays", "ChainRulesCore", "DataFrames", "Flux", "LaplaceRedux", "LazyArtifacts", "LinearAlgebra", "MLDatasets", "MLJBase", "MLJModels", "MLUtils", "MultivariateStats", "NearestNeighborModels", "Parameters", "Plots", "ProgressMeter", "Random", "Serialization", "SliceMap", "SnoopPrecompile", "Statistics", "StatsBase", "Tables", "UMAP"]
path = "../../CounterfactualExplanations.jl"
uuid = "2f13d31b-18db-44c1-bc43-ebaf2cff0be0"
version = "0.1.9"
diff --git a/notebooks/mnist.qmd b/notebooks/mnist.qmd
index 0716c8bc..07a6fec5 100644
--- a/notebooks/mnist.qmd
+++ b/notebooks/mnist.qmd
@@ -22,12 +22,14 @@ First, let's create a couple of image classifier architectures:
```{julia}
# Model parameters:
epochs = 100
-batch_size = minimum([Int(round(n_obs/10)), 100])
-n_hidden = 32
+batch_size = minimum([Int(round(n_obs/10)), 128])
+n_hidden = 200
activation = Flux.relu
builder = MLJFlux.@builder Flux.Chain(
Dense(n_in, n_hidden),
BatchNorm(n_hidden, activation),
+ Dense(n_hidden, n_hidden),
+ BatchNorm(n_hidden, activation),
Dense(n_hidden, n_out),
)
# builder = MLJFlux.Short(n_hidden=n_hidden, dropout=0.2, σ=activation)
@@ -51,7 +53,11 @@ mlp = NeuralNetworkClassifier(
# Joint Energy Model:
ð’Ÿx = Uniform(0,1)
ð’Ÿy = Categorical(ones(output_dim) ./ output_dim)
-sampler = ConditionalSampler(ð’Ÿx, ð’Ÿy, input_size=(input_dim,), batch_size=batch_size)
+sampler = ConditionalSampler(
+ ð’Ÿx, ð’Ÿy,
+ input_size=(input_dim,),
+ batch_size=10
+)
jem = JointEnergyClassifier(
sampler;
builder=builder,
@@ -73,31 +79,33 @@ mlp_ens = EnsembleModel(model=mlp, n=5)
```{julia}
cov = .90
-conf_model = conformal_model(jem; method=:simple_inductive, coverage=cov)
+conf_model = conformal_model(mlp; method=:adaptive_inductive, coverage=cov)
mach = machine(conf_model, X, labels)
fit!(mach)
M = CCE.ConformalModel(mach.model, mach.fitresult)
```
```{julia}
-jem = mach.model.model.jem
-n_iter = 100
-_w = 1500
-plts = []
-neach = 10
-for i in 1:10
- x = jem.sampler(jem.chain, jem.sampling_rule; niter=n_iter, n_samples=neach, y=i)
- plts_i = []
- for j in 1:size(x, 2)
- xj = x[:,j]
- xj = reshape(xj, (n_digits, n_digits))
- plts_i = [plts_i..., Plots.heatmap(rotl90(xj), axis=nothing, cb=false)]
+if mach.model.model isa JointEnergyModels.JointEnergyClassifier
+ jem = mach.model.model.jem
+ n_iter = 100
+ _w = 1500
+ plts = []
+ neach = 10
+ for i in 1:10
+ x = jem.sampler(jem.chain, jem.sampling_rule; niter=n_iter, n_samples=neach, y=i)
+ plts_i = []
+ for j in 1:size(x, 2)
+ xj = x[:,j]
+ xj = reshape(xj, (n_digits, n_digits))
+ plts_i = [plts_i..., Plots.heatmap(rotl90(xj), axis=nothing, cb=false)]
+ end
+ plt = Plots.plot(plts_i..., size=(_w,0.10*_w), layout=(1,10))
+ plts = [plts..., plt]
end
- plt = Plots.plot(plts_i..., size=(_w,0.10*_w), layout=(1,10))
- plts = [plts..., plt]
+ plt = Plots.plot(plts..., size=(_w,_w), layout=(10,1))
+ display(plt)
end
-plt = Plots.plot(plts..., size=(_w,_w), layout=(10,1))
-display(plt)
```
```{julia}
@@ -110,12 +118,12 @@ println("F1 score (test): $(round(f1,digits=3))")
Random.seed!(1234)
# Set up search:
-factual_label = 9
+factual_label = 4
x = reshape(counterfactual_data.X[:,rand(findall(predict_label(M, counterfactual_data).==factual_label))],input_dim,1)
-target = 4
+target = 9
factual = predict_label(M, counterfactual_data, x)[1]
γ = 0.5
-T = 1
+T = 100
# Generate counterfactual using generic generator:
generator = GenericGenerator()
@@ -125,11 +133,14 @@ ce_wachter = generate_counterfactual(
initialization=:identity,
)
+# CCE:
+λ=[0.0,1.0]
+temp=0.5
+
# Generate counterfactual using CCE generator:
generator = CCEGenerator(
- λ=[0.0,1.0],
- temp=0.5,
- # opt=CounterfactualExplanations.Generators.JSMADescent(η=1.0),
+ λ=λ,
+ temp=temp,
)
ce_conformal = generate_counterfactual(
x, target, counterfactual_data, M, generator;
@@ -138,6 +149,19 @@ ce_conformal = generate_counterfactual(
converge_when=:generator_conditions,
)
+# Generate counterfactual using CCE generator:
+generator = CCEGenerator(
+ λ=λ,
+ temp=temp,
+ opt=CounterfactualExplanations.Generators.JSMADescent(η=1.0),
+)
+ce_conformal_jsma = generate_counterfactual(
+ x, target, counterfactual_data, M, generator;
+ decision_threshold=γ, max_iter=T,
+ initialization=:identity,
+ converge_when=:generator_conditions,
+)
+
# Plot:
p1 = Plots.plot(
convert2image(MNIST, reshape(x,28,28)),
@@ -147,12 +171,13 @@ p1 = Plots.plot(
)
plts = [p1]
-ces = zip([ce_wachter,ce_conformal])
+ces = zip([ce_wachter,ce_conformal,ce_conformal_jsma])
+_names = ["Wachter", "CCE", "CCE-JSMA"]
counterfactuals = reduce((x,y)->cat(x,y,dims=3),map(ce -> CounterfactualExplanations.counterfactual(ce[1]), ces))
phat = reduce((x,y) -> cat(x,y,dims=3), map(ce -> target_probs(ce[1]), ces))
-for x in zip(eachslice(counterfactuals; dims=3), eachslice(phat; dims=3))
- ce, _phat = (x[1],x[2])
- _title = "p(y=$(target)|x′)=$(round(_phat[1]; digits=3))"
+for x in zip(eachslice(counterfactuals; dims=3), _names, eachslice(phat; dims=3))
+ ce, _name, _phat = (x[1],x[2],x[3])
+ _title = "$_name (p̂=$(round(_phat[1]; digits=3)))"
plt = Plots.plot(
convert2image(MNIST, reshape(ce,28,28)),
axis=nothing,
diff --git a/src/model.jl b/src/model.jl
index c724867b..cc7cde3c 100644
--- a/src/model.jl
+++ b/src/model.jl
@@ -52,6 +52,7 @@ function ConformalModel(model, fitresult=nothing; likelihood::Union{Nothing,Symb
end
# Construct model:
+ testmode!(fitresult[1])
M = ConformalModel(model, fitresult, likelihood)
return M
end
diff --git a/src/penalties.jl b/src/penalties.jl
index 09d2c2a2..30ae653c 100644
--- a/src/penalties.jl
+++ b/src/penalties.jl
@@ -3,21 +3,21 @@ using LinearAlgebra: norm
using Statistics: mean
"""
- set_size_penalty(counterfactual_explanation::AbstractCounterfactualExplanation)
+ set_size_penalty(ce::AbstractCounterfactualExplanation)
Penalty for smooth conformal set size.
"""
function set_size_penalty(
- counterfactual_explanation::AbstractCounterfactualExplanation;
+ ce::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
+ conf_model = ce.M.model
+ fitresult = ce.M.fitresult
+ X = CounterfactualExplanations.decode_state(ce)
+ loss = map(eachslice(X, dims=ndims(X))) do x
+ x = ndims(x) == 1 ? x[:,:] : x
+ if target_probs(ce, x)[1] >= 0.5
l = ConformalPrediction.smooth_size_loss(
conf_model, fitresult, x';
κ=κ,
@@ -35,19 +35,19 @@ function set_size_penalty(
end
function distance_from_energy(
- counterfactual_explanation::AbstractCounterfactualExplanation;
+ ce::AbstractCounterfactualExplanation;
n::Int=10000, from_buffer=true, agg=mean, kwargs...
)
conditional_samples = []
ignore_derivatives() do
- _dict = counterfactual_explanation.params
+ _dict = ce.params
if !(:energy_sampler ∈ collect(keys(_dict)))
- _dict[:energy_sampler] = CCE.EnergySampler(counterfactual_explanation; kwargs...)
+ _dict[:energy_sampler] = CCE.EnergySampler(ce; kwargs...)
end
sampler = _dict[:energy_sampler]
push!(conditional_samples, rand(sampler, n; from_buffer=from_buffer))
end
- x′ = CounterfactualExplanations.counterfactual(counterfactual_explanation)
+ x′ = CounterfactualExplanations.counterfactual(ce)
loss = map(eachslice(x′, dims=3)) do x
x = Matrix(x)
Δ = map(eachcol(conditional_samples[1])) do xsample
@@ -62,13 +62,13 @@ function distance_from_energy(
end
function distance_from_targets(
- counterfactual_explanation::AbstractCounterfactualExplanation;
+ ce::AbstractCounterfactualExplanation;
n::Int=10000, agg=mean
)
- target_idx = counterfactual_explanation.data.output_encoder.labels .== counterfactual_explanation.target
- target_samples = counterfactual_explanation.data.X[:,target_idx] |>
+ target_idx = ce.data.output_encoder.labels .== ce.target
+ target_samples = ce.data.X[:,target_idx] |>
X -> X[:,rand(1:end,n)]
- x′ = CounterfactualExplanations.counterfactual(counterfactual_explanation)
+ x′ = CounterfactualExplanations.counterfactual(ce)
loss = map(eachslice(x′, dims=3)) do x
x = Matrix(x)
Δ = map(eachcol(target_samples)) do xsample
diff --git a/www/cce_mnist.png b/www/cce_mnist.png
index ea956a80689729139b58966c11514539991a792a..ff97f8657cd5af3cd5f753e84cb38286614bf6ca 100644
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