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Patrick Altmeyer
ECCCo-jl
Commits
136467dc
Commit
136467dc
authored
2 years ago
by
pat-alt
Browse files
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some work on configurable loss function
parent
8981107c
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3 changed files
Manifest.toml
+123
-82
123 additions, 82 deletions
Manifest.toml
notebooks/conformal.qmd
+45
-0
45 additions, 0 deletions
notebooks/conformal.qmd
src/ConformalGenerator.jl
+36
-8
36 additions, 8 deletions
src/ConformalGenerator.jl
with
204 additions
and
90 deletions
Manifest.toml
+
123
−
82
View file @
136467dc
...
...
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...
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@@ -609,10 +638,10 @@ uuid = "a09fc81d-aa75-5fe9-8630-4744c3626534"
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...
...
@@ -733,9 +762,9 @@ version = "4.16.0"
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...
...
@@ -851,9 +880,9 @@ uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
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@@ -890,15 +919,15 @@ version = "0.7.9"
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@@ -960,6 +989,12 @@ git-tree-sha1 = "c13304c81eec1ed3af7fc20e75fb6b26092a1102"
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@@ -1011,15 +1046,15 @@ version = "7.8.3"
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@@ -1120,9 +1155,9 @@ version = "2.0.2"
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...
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@@ -1142,9 +1177,9 @@ version = "10.40.0+0"
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...
...
@@ -1206,9 +1241,9 @@ version = "1.3.4"
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...
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@@ -1356,9 +1391,9 @@ version = "1.1.1"
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...
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@@ -1422,9 +1457,9 @@ uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
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@@ -1440,9 +1475,9 @@ version = "0.1.1"
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@@ -1473,9 +1508,9 @@ version = "0.33.21"
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...
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notebooks/conformal.qmd
+
45
−
0
View file @
136467dc
...
...
@@ -3,6 +3,7 @@ using CCE
using ConformalPrediction
using CounterfactualExplanations
using CounterfactualExplanations.Data
using CounterfactualExplanations.Objectives
using Flux
using MLJBase
using MLJFlux
...
...
@@ -107,18 +108,62 @@ p2 = contourf(mach.model, mach.fitresult, X, y; plot_classification_loss=true, t
plot(p1, p2, size=(800,320))
```
### Penalizing Size
```{julia}
#| output: true
#| echo: false
#| label: fig-ce
#| fig-cap: "Comparison of counterfactuals produced using different generators."
opt = Descent(0.01)
ordered_names = [
"Generic (γ=0.5)",
"Generic (γ=0.9)",
"Conformal (λ₂=1)",
"Conformal (λ₂=10)"
]
loss_fun = Objectives.logitbinarycrossentropy
# Generators:
generators = Dict(
ordered_names[1] => GenericGenerator(opt = opt, decision_threshold=0.5),
ordered_names[2] => GenericGenerator(opt = opt, decision_threshold=0.9),
ordered_names[3] => CCE.ConformalGenerator(loss=loss_fun, opt=opt, λ=[0.1,1]),
ordered_names[4] => CCE.ConformalGenerator(loss=loss_fun, opt=opt, λ=[0.1,10]),
)
counterfactuals = Dict([name => generate_counterfactual(x, target, counterfactual_data, M, gen; initialization=:identity) for (name, gen) in generators])
# Plots:
plts = []
for name ∈ ordered_names
ce = counterfactuals[name]
plt = plot(ce; title=name, colorbar=false, ticks = false, legend=false, zoom=0)
plts = vcat(plts..., plt)
end
_n = length(generators)
img_size = 300
plot(plts..., size=(_n * img_size,1.05*img_size), layout=(1,_n))
```
### Configurable Classification Loss
```{julia}
#| output: true
#| echo: false
#| label: fig-ce
#| fig-cap: "Comparison of counterfactuals produced using different generators."
opt = Descent(0.01)
ordered_names = [
"Generic (γ=0.5)",
"Generic (γ=0.9)",
"Conformal (λ₂=1)",
"Conformal (λ₂=10)"
]
loss_fun = Objectives.logitbinarycrossentropy
# Generators:
generators = Dict(
...
...
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src/ConformalGenerator.jl
+
36
−
8
View file @
136467dc
using
CategoricalArrays
using
CounterfactualExplanations
using
CounterfactualExplanations
.
Generators
using
CounterfactualExplanations
.
Models
:
binary_to_onehot
using
CounterfactualExplanations
.
Objectives
using
Flux
using
LinearAlgebra
using
Parameters
...
...
@@ -7,7 +10,7 @@ using SliceMap
using
Statistics
mutable struct
ConformalGenerator
<:
AbstractGradientBasedGenerator
loss
::
Union
{
Nothing
,
Symbol
}
# loss function
loss
::
Union
{
Nothing
,
Function
}
# loss function
complexity
::
Function
# complexity function
λ
::
Union
{
AbstractFloat
,
AbstractVector
}
# strength of penalty
decision_threshold
::
Union
{
Nothing
,
AbstractFloat
}
...
...
@@ -26,13 +29,12 @@ end
end
"""
ConformalGenerator(
;
loss::Symbol=:logitbinarycrossentropy,
ConformalGenerator(;
loss::Union{Nothing,Function}=conformal_training_loss,
complexity::Function=norm,
λ::AbstractFloat
=0.1
,
opt::Flux.Optimise.AbstractOptimiser=Flux.Optimise.Descent()
,
τ::AbstractFloat=1e-5
λ::
Union{
AbstractFloat
,AbstractVector}=[0.1, 1.0]
,
decision_threshold=nothing
,
kwargs...
)
An outer constructor method that instantiates a generic generator.
...
...
@@ -43,7 +45,7 @@ generator = ConformalGenerator()
```
"""
function
ConformalGenerator
(;
loss
::
Union
{
Nothing
,
Symbol
}
=
nothing
,
loss
::
Union
{
Nothing
,
Function
}
=
conformal_training_loss
,
complexity
::
Function
=
norm
,
λ
::
Union
{
AbstractFloat
,
AbstractVector
}
=
[
0.1
,
1.0
],
decision_threshold
=
nothing
,
...
...
@@ -53,6 +55,32 @@ function ConformalGenerator(;
ConformalGenerator
(
loss
,
complexity
,
λ
,
decision_threshold
,
params
.
opt
,
params
.
τ
,
params
.
κ
,
params
.
temp
)
end
@doc
raw
"""
conformal_training_loss(counterfactual_explanation::AbstractCounterfactualExplanation; kwargs...)
A configurable classification loss function for Conformal Predictors.
"""
function
conformal_training_loss
(
counterfactual_explanation
::
AbstractCounterfactualExplanation
;
kwargs
...
)
conf_model
=
counterfactual_explanation
.
M
.
model
fitresult
=
counterfactual_explanation
.
M
.
fitresult
X
=
CounterfactualExplanations
.
decode_state
(
counterfactual_explanation
)
y
=
counterfactual_explanation
.
target_encoded
[
:
,
:
,
1
]
if
counterfactual_explanation
.
M
.
likelihood
==
:
classification_binary
y
=
binary_to_onehot
(
y
)
end
y
=
permutedims
(
y
)
generator
=
counterfactual_explanation
.
generator
loss
=
SliceMap
.
slicemap
(
X
,
dims
=
(
1
,
2
))
do
x
x
=
Matrix
(
x
)
ConformalPrediction
.
classification_loss
(
conf_model
,
fitresult
,
x
,
y
;
temp
=
generator
.
temp
)
end
loss
=
mean
(
loss
)
return
loss
end
"""
set_size_penalty(
generator::ConformalGenerator,
...
...
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