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Merged
pat-alt merged 4 commits into from
Dec 19, 2025
Merged

Update categoricaldistributions #142

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026f317
updating categorical distributions
pasq-cat Dec 5, 2025
f349168
removed temporarily taijadata, using categoricaldistributions in lapl…
pasq-cat Dec 5, 2025
b145902
added the categoricaldistribution package in the test env and solve…
pasq-cat Dec 8, 2025
3333383
fixed changelog and added using CategoricalDistributions in multi.md
pasq-cat Dec 8, 2025
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5 changes: 5 additions & 0 deletions CHANGELOG.md
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Expand Up @@ -5,6 +5,11 @@ All notable changes to this project will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).

*Note*: We try to adhere to these practices as of version [v0.2.1].
## Version [1.3.0] - 2025-12-08
- temporarily removed TaijaData due to issues with CategoricalDistributions 0.2 [#142]
- Docs env now has compatibility issues with TajaPlotting and RData(needs to be fixed). Cannot add CategoricalDistributions 0.2 without conflicts
- updated the package CategoricalDistributions to 0.2 in LaplaceRedux
- Explicitly used LaplaceRedux.Laplace in the pytorch_comparison.jl to avoid name conflicts


## Version [1.2.0] - 2024-12-03
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4 changes: 2 additions & 2 deletions Project.toml
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Expand Up @@ -22,13 +22,13 @@ Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"

[compat]
Aqua = "0.8"
CategoricalDistributions = "0.1.15"
CategoricalDistributions = "0.2.1"
ChainRulesCore = "1.23.0"
Compat = "4.7.0"
Distributions = "0.25.109"
Flux = "0.12, 0.13, 0.14"
LinearAlgebra = "1.7, 1.10"
MLJBase = "1"
MLJBase = "1.11"
MLJModelInterface = "1.8.0"
MLUtils = "0.4"
Optimisers = "0.2, 0.3"
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10 changes: 5 additions & 5 deletions docs/src/tutorials/multi.md
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Expand Up @@ -6,7 +6,7 @@
``` julia
using Pkg; Pkg.activate("docs")
# Import libraries
using Flux, Plots, TaijaPlotting, Random, Statistics, LaplaceRedux
using Flux, Plots, TaijaPlotting, Random, Statistics, LaplaceRedux, CategoricalDistributions
theme(:lime)
```

Expand All @@ -17,7 +17,7 @@ using LaplaceRedux.Data
seed = 1234
x, y = Data.toy_data_multi(seed=seed)
X = hcat(x...)
y_onehot = Flux.onehotbatch(y, unique(y))
y_onehot = Flux.onehotbatch(y, unwrap.(unique(y)))
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Missing import: unwrap is used here but CategoricalDistributions is not imported in this file. Add using CategoricalDistributions to the imports section to make unwrap available.

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y_onehot = Flux.unstack(y_onehot',1)
```

Expand Down Expand Up @@ -59,7 +59,7 @@ We set up a model
``` julia
n_hidden = 3
D = size(X,1)
out_dim = length(unique(y))
out_dim = length(unwrap.(unique(y)))
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Missing import: unwrap is used here but CategoricalDistributions is not imported in this file. Add using CategoricalDistributions to the imports section to make unwrap available.

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nn = Chain(
Dense(D, n_hidden, σ),
Dense(n_hidden, out_dim)
Expand Down Expand Up @@ -103,7 +103,7 @@ optimize_prior!(la; verbosity=1, n_steps=100)
with either the probit approximation:

``` julia
_labels = sort(unique(y))
_labels = sort(unwrap.(unique(y)))
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Missing import: unwrap is used here but CategoricalDistributions is not imported in this file. Add using CategoricalDistributions to the imports section to make unwrap available.

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plt_list = []
for target in _labels
plt = plot(la, X_test, y_test; target=target, clim=(0,1))
Expand All @@ -117,7 +117,7 @@ plot(plt_list...)
or the plugin approximation:

``` julia
_labels = sort(unique(y))
_labels = sort(unwrap.(unique(y)))
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Missing import: unwrap is used here but CategoricalDistributions is not imported in this file. Add using CategoricalDistributions to the imports section to make unwrap available.

Copilot uses AI. Check for mistakes.
plt_list = []
for target in _labels
plt = plot(la, X_test, y_test; target=target, clim=(0,1), link_approx=:plugin)
Expand Down
8 changes: 4 additions & 4 deletions docs/src/tutorials/multi.qmd
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Expand Up @@ -15,7 +15,7 @@ using LaplaceRedux.Data
seed = 1234
x, y = Data.toy_data_multi(seed=seed)
X = hcat(x...)
y_onehot = Flux.onehotbatch(y, unique(y))
y_onehot = Flux.onehotbatch(y, unwrap.(unique(y)))
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Missing import: unwrap is used here but CategoricalDistributions is not imported in this file. Add using CategoricalDistributions to the imports section to make unwrap available.

Copilot uses AI. Check for mistakes.
y_onehot = Flux.unstack(y_onehot',1)
```

Expand Down Expand Up @@ -59,7 +59,7 @@ We set up a model
```{julia}
n_hidden = 3
D = size(X,1)
out_dim = length(unique(y))
out_dim = length(unwrap.(unique(y)))
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Missing import: unwrap is used here but CategoricalDistributions is not imported in this file. Add using CategoricalDistributions to the imports section to make unwrap available.

Copilot uses AI. Check for mistakes.
nn = Chain(
Dense(D, n_hidden, σ),
Dense(n_hidden, out_dim)
Expand Down Expand Up @@ -105,7 +105,7 @@ with either the probit approximation:
```{julia}
#| output: true

_labels = sort(unique(y))
_labels = sort(unwrap.(unique(y)))
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Missing import: unwrap is used here but CategoricalDistributions is not imported in this file. Add using CategoricalDistributions to the imports section to make unwrap available.

Copilot uses AI. Check for mistakes.
plt_list = []
for target in _labels
plt = plot(la, X_test, y_test; target=target, clim=(0,1))
Expand All @@ -119,7 +119,7 @@ plot(plt_list...)
```{julia}
#| output: true

_labels = sort(unique(y))
_labels = sort(unwrap.(unique(y)))
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Missing import: unwrap is used here but CategoricalDistributions is not imported in this file. Add using CategoricalDistributions to the imports section to make unwrap available.

Copilot uses AI. Check for mistakes.
plt_list = []
for target in _labels
plt = plot(la, X_test, y_test; target=target, clim=(0,1), link_approx=:plugin)
Expand Down
2 changes: 1 addition & 1 deletion test/Project.toml
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@@ -1,6 +1,7 @@
[deps]
Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
CategoricalDistributions = "af321ab8-2d2e-40a6-b165-3d674595d28e"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab"
Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
Expand All @@ -17,7 +18,6 @@ Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
StatisticalMeasures = "a19d573c-0a75-4610-95b3-7071388c7541"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
TaijaData = "9d524318-b4e6-4a65-86d2-b2b72d07866c"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
Trapz = "592b5752-818d-11e9-1e9a-2b8ca4a44cd1"
Tullio = "bc48ee85-29a4-5162-ae0b-a64e1601d4bc"
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8 changes: 4 additions & 4 deletions test/laplace.jl
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Expand Up @@ -9,7 +9,7 @@ using MLUtils
using LinearAlgebra
using Distributions: Normal, Bernoulli, Categorical
using Random

using CategoricalDistributions
@testset "Construction" begin

# One layer:
Expand Down Expand Up @@ -132,12 +132,12 @@ end
#Random.seed!(123) # For reproducibility
x, y = Data.toy_data_multi(50)
X = hcat(x...)
y_train = Flux.onehotbatch(y, unique(y))
y_train = Flux.onehotbatch(y, unwrap.(unique(y)))
y_train = Flux.unstack(y_train'; dims=1)
data = zip(x, y_train)
n_hidden = 3
D = size(X, 1)
out_dim = length(unique(y))
out_dim = length(unwrap.(unique(y)))
# Case: softmax activation function
nn = Chain(Dense(D, n_hidden, σ), Dense(n_hidden, out_dim), softmax)

Expand Down Expand Up @@ -374,7 +374,7 @@ end

# Classification multi:
xs, y = LaplaceRedux.Data.toy_data_multi(n)
ytrain = Flux.onehotbatch(y, unique(y))
ytrain = Flux.onehotbatch(y, unwrap.(unique(y)))
ytrain = Flux.unstack(ytrain'; dims=1)
X = reduce(hcat, xs)
Y = reduce(hcat, ytrain)
Expand Down
24 changes: 12 additions & 12 deletions test/pytorch_comparison.jl
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Expand Up @@ -19,7 +19,7 @@ include("testutils.jl")
y = df[:, 3]

X = hcat(x...)
y_train = Flux.onehotbatch(y, unique(y))
y_train = Flux.onehotbatch(y, unwrap.(unique(y)))
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Missing import: unwrap is used here but CategoricalDistributions is not imported in this file. Add using CategoricalDistributions to the imports at the top of the file to make unwrap available.

Copilot uses AI. Check for mistakes.
y_train = Flux.unstack(y_train'; dims=1)

data = zip(x, y_train)
Expand All @@ -28,7 +28,7 @@ include("testutils.jl")
nn = deserialize(joinpath(@__DIR__, "datafiles", "nn-binary_multi.jlb"))

@testset "LA - full weights - full hessian - ggn" begin
la = Laplace(
la = LaplaceRedux.Laplace(
nn;
likelihood=:classification,
hessian_structure=:full,
Expand All @@ -43,7 +43,7 @@ include("testutils.jl")
end

@testset "LA - full weights - full hessian - empfisher" begin
la = Laplace(
la = LaplaceRedux.Laplace(
nn;
likelihood=:classification,
hessian_structure=:full,
Expand All @@ -58,7 +58,7 @@ include("testutils.jl")
end

@testset "LA - last layer - full hessian - empfisher" begin
la = Laplace(
la = LaplaceRedux.Laplace(
nn;
likelihood=:classification,
hessian_structure=:full,
Expand All @@ -75,7 +75,7 @@ include("testutils.jl")
end

@testset "LA - last layer - full hessian - ggn" begin
la = Laplace(
la = LaplaceRedux.Laplace(
nn;
likelihood=:classification,
hessian_structure=:full,
Expand All @@ -92,7 +92,7 @@ include("testutils.jl")
end

@testset "LA - subnetwork - full hessian - ggn" begin
la = Laplace(
la = LaplaceRedux.Laplace(
nn;
likelihood=:classification,
hessian_structure=:full,
Expand All @@ -115,7 +115,7 @@ include("testutils.jl")
end

@testset "LA - subnetwork - full hessian - empfisher" begin
la = Laplace(
la = LaplaceRedux.Laplace(
nn;
likelihood=:classification,
hessian_structure=:full,
Expand All @@ -138,7 +138,7 @@ include("testutils.jl")
end

@testset "LA - full weights - kron - ggn" begin
la = Laplace(
la = LaplaceRedux.Laplace(
nn;
likelihood=:classification,
hessian_structure=:kron,
Expand All @@ -151,7 +151,7 @@ include("testutils.jl")
end

@testset "LA - last layer - kron - ggn" begin
la = Laplace(
la = LaplaceRedux.Laplace(
nn;
likelihood=:classification,
hessian_structure=:kron,
Expand Down Expand Up @@ -179,7 +179,7 @@ include("testutils.jl")
nn = deserialize(joinpath(@__DIR__, "datafiles", "nn-binary_regression.jlb"))

@testset "LA - full weights - full hessian - ggn" begin
la = Laplace(
la = LaplaceRedux.Laplace(
nn;
likelihood=:regression,
hessian_structure=:full,
Expand All @@ -192,7 +192,7 @@ include("testutils.jl")
end

@testset "LA - last layer - full hessian - ggn" begin
la = Laplace(
la = LaplaceRedux.Laplace(
nn;
likelihood=:regression,
hessian_structure=:full,
Expand All @@ -205,7 +205,7 @@ include("testutils.jl")
end

@testset "LA - subnetwork - full hessian - ggn" begin
la = Laplace(
la = LaplaceRedux.Laplace(
nn;
likelihood=:regression,
hessian_structure=:full,
Expand Down
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