Package evaluation of LaplaceRedux on Julia 1.12.0-DEV.1805 (a080deafdd*) started at 2025-03-24T23:44:18.609


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# Set-up
#

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 9.17s


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# Installation
#

Installing LaplaceRedux...
   Resolving package versions...
    Updating `~/.julia/environments/v1.12/Project.toml`
  [c52c1a26] + LaplaceRedux v1.2.0
    Updating `~/.julia/environments/v1.12/Manifest.toml`
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  [82899510] + IteratorInterfaceExtensions v1.0.0
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  [929cbde3] + LLVM v9.2.0
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  [c52c1a26] + LaplaceRedux v1.2.0
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        Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m`

Installation completed after 5.77s


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# Precompilation
#

Precompiling PkgEval dependencies...

Precompiling package dependencies...

Precompilation completed after 42.27s


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# Testing
#

     Testing LaplaceRedux
      Status `/tmp/jl_mtqDOo/Project.toml`
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⌅ [587475ba] Flux v0.14.25
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        Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading.
     Testing Running tests...
Training networks.
Running workflows.
[ Info: Running workflow for: (regression, 0, GGN, all)
[ Info: Running workflow for: (regression, 0, EmpiricalFisher, all)
[ Info: Running workflow for: (regression, 32, GGN, all)
[ Info: Running workflow for: (regression, 32, EmpiricalFisher, all)
[ Info: Running workflow for: (regression, 0, GGN, last_layer)
[ Info: Running workflow for: (regression, 0, EmpiricalFisher, last_layer)
[ Info: Running workflow for: (regression, 32, GGN, last_layer)
[ Info: Running workflow for: (regression, 32, EmpiricalFisher, last_layer)
[ Info: Running workflow for: (regression, 0, GGN, subnetwork)
[ Info: Running workflow for: (regression, 0, EmpiricalFisher, subnetwork)
[ Info: Running workflow for: (regression, 32, GGN, subnetwork)
[ Info: Running workflow for: (regression, 32, EmpiricalFisher, subnetwork)
[ Info: Running workflow for: (classification_multi, 0, GGN, all)
[ Info: Running workflow for: (classification_multi, 0, EmpiricalFisher, all)
[ Info: Running workflow for: (classification_multi, 32, GGN, all)
[ Info: Running workflow for: (classification_multi, 32, EmpiricalFisher, all)
[ Info: Running workflow for: (classification_multi, 0, GGN, last_layer)
[ Info: Running workflow for: (classification_multi, 0, EmpiricalFisher, last_layer)
[ Info: Running workflow for: (classification_multi, 32, GGN, last_layer)
[ Info: Running workflow for: (classification_multi, 32, EmpiricalFisher, last_layer)
[ Info: Running workflow for: (classification_multi, 0, GGN, subnetwork)
[ Info: Running workflow for: (classification_multi, 0, EmpiricalFisher, subnetwork)
[ Info: Running workflow for: (classification_multi, 32, GGN, subnetwork)
[ Info: Running workflow for: (classification_multi, 32, EmpiricalFisher, subnetwork)
[ Info: Running workflow for: (classification_binary, 0, GGN, all)
[ Info: Running workflow for: (classification_binary, 0, EmpiricalFisher, all)
[ Info: Running workflow for: (classification_binary, 32, GGN, all)
[ Info: Running workflow for: (classification_binary, 32, EmpiricalFisher, all)
[ Info: Running workflow for: (classification_binary, 0, GGN, last_layer)
[ Info: Running workflow for: (classification_binary, 0, EmpiricalFisher, last_layer)
[ Info: Running workflow for: (classification_binary, 32, GGN, last_layer)
[ Info: Running workflow for: (classification_binary, 32, EmpiricalFisher, last_layer)
[ Info: Running workflow for: (classification_binary, 0, GGN, subnetwork)
[ Info: Running workflow for: (classification_binary, 0, EmpiricalFisher, subnetwork)
[ Info: Running workflow for: (classification_binary, 32, GGN, subnetwork)
[ Info: Running workflow for: (classification_binary, 32, EmpiricalFisher, subnetwork)
[ Info: Running workflow for: (regression, GGN, all)
[ Info: Running workflow for: (classification_multi, GGN, all)
[ Info: Running workflow for: (classification_binary, GGN, all)
[ Info: Running workflow for: (regression, EmpiricalFisher, all)
[ Info: Running workflow for: (classification_multi, EmpiricalFisher, all)
[ Info: Running workflow for: (classification_binary, EmpiricalFisher, all)
[ Info: Running workflow for: (regression, GGN, last_layer)
[ Info: Running workflow for: (classification_multi, GGN, last_layer)
[ Info: Running workflow for: (classification_binary, GGN, last_layer)
[ Info: Running workflow for: (regression, EmpiricalFisher, last_layer)
[ Info: Running workflow for: (classification_multi, EmpiricalFisher, last_layer)
[ Info: Running workflow for: (classification_binary, EmpiricalFisher, last_layer)
[ Info: Running workflow for: (regression, GGN, subnetwork)
[ Info: Running workflow for: (classification_multi, GGN, subnetwork)
[ Info: Running workflow for: (classification_binary, GGN, subnetwork)
[ Info: Running workflow for: (regression, EmpiricalFisher, subnetwork)
[ Info: Running workflow for: (classification_multi, EmpiricalFisher, subnetwork)
[ Info: Running workflow for: (classification_binary, EmpiricalFisher, subnetwork)
[ Info:  testing sharpness_regression with distributions
[ Info:  testing empirical_frequency_regression with distributions
[ Info:  testing sharpness_classification with distributions
[ Info:  testing empirical_frequency_classification with distributions
[ Info: testing sigma scaling technique
┌ Warning: Layer with Float32 parameters got Float64 input.
│   The input will be converted, but any earlier layers may be very slow.
│   layer = Dense(2 => 3, σ)    # 9 parameters
│   summary(x) = "2-element Vector{Float64}"
└ @ Flux ~/.julia/packages/Flux/vwk6M/src/layers/stateless.jl:59
[ Info:  testing  interface for LaplaceRegressor
The number of epochs inserted is lower than the number of epochs already been trained. No update is necessary
 updating only the laplace optimization part
[ Info: Training machine(LaplaceRegressor(model = nothing, …), …).
┌ Warning: Warning: no Flux model has been provided in the model. LaplaceRedux will use a standard MLP with 2 hidden layers with 20 neurons each and input and output layers compatible with the dataset.
└ @ LaplaceRedux ~/.julia/packages/LaplaceRedux/1bSKz/src/direct_mlj.jl:161
[ Info: Iteration 10: P₀=0.9507453084404416, σ=0.8636653304276862
loss(exp.(logP₀), exp.(logσ)) = 233.40690780558415
Log likelihood: -156.05340160252587
Log det ratio: 108.83697058451439
Scatter: 45.87004182160217
[ Info: Iteration 20: P₀=0.9721926561094985, σ=0.7790970828720825
loss(exp.(logP₀), exp.(logσ)) = 233.28016161005317
Log likelihood: -155.95247149864338
Log det ratio: 107.7505810105595
Scatter: 46.90479921226008
[ Info: Iteration 30: P₀=0.9922137321973001, σ=0.8273680251190529
loss(exp.(logP₀), exp.(logσ)) = 233.00325739338967
Log likelihood: -155.68566099498798
Log det ratio: 106.76444866816252
Scatter: 47.87074412864087
[ Info: Iteration 40: P₀=0.9993406246897789, σ=0.8268184239264026
loss(exp.(logP₀), exp.(logσ)) = 233.0006286543592
Log likelihood: -155.68348102290275
Log det ratio: 106.41970420304253
Scatter: 48.214591059870344
[ Info: Iteration 50: P₀=0.999644140149176, σ=0.8098835212052352
loss(exp.(logP₀), exp.(logσ)) = 232.9886633684639
Log likelihood: -155.67149895488043
Log det ratio: 106.40509423795967
Scatter: 48.22923458920724
[ Info: Iteration 60: P₀=0.9982600458283402, σ=0.8124845879347848
loss(exp.(logP₀), exp.(logσ)) = 232.9832879441371
Log likelihood: -155.66617636477213
Log det ratio: 106.47176614267312
Scatter: 48.16245701605683
[ Info: Iteration 70: P₀=0.9969939164551215, σ=0.8175487640580326
loss(exp.(logP₀), exp.(logσ)) = 232.98052866736967
Log likelihood: -155.66341216232033
Log det ratio: 106.53286218272508
Scatter: 48.101370827373586
[ Info: Iteration 80: P₀=0.9965056549647198, σ=0.8183007492401478
loss(exp.(logP₀), exp.(logσ)) = 232.9809752702102
Log likelihood: -155.6638432380804
Log det ratio: 106.55645009778846
Scatter: 48.07781396647112
[ Info: Iteration 90: P₀=0.9969409861237789, σ=0.8173360017662478
loss(exp.(logP₀), exp.(logσ)) = 232.9804471215338
Log likelihood: -155.6633293002002
Log det ratio: 106.5354185134195
Scatter: 48.09881712924775
[ Info: Iteration 100: P₀=0.9977454597544558, σ=0.8165890408476854
loss(exp.(logP₀), exp.(logσ)) = 232.98028287235263
Log likelihood: -155.66317543783478
Log det ratio: 106.49658478035929
Scatter: 48.13763008867645
[ Info: Updating machine(LaplaceRegressor(model = Chain(Dense(4 => 20, relu), Dense(20 => 20, relu), Dense(20 => 20, relu), Dense(20 => 1)), …), …).
Epoch 100: Loss: 62.0977430646542 
[ Info: Iteration 10: P₀=1.1069679211794654, σ=0.6347385111101538
loss(exp.(logP₀), exp.(logσ)) = 306.9223936474177
Log likelihood: -136.26169387044024
Log det ratio: 265.9548055599002
Scatter: 75.36659399405484
[ Info: Iteration 20: P₀=1.2164694100485116, σ=0.7405266408439823
loss(exp.(logP₀), exp.(logσ)) = 306.1974226512235
Log likelihood: -135.61240791100587
Log det ratio: 258.34815688666765
Scatter: 82.82187259376772
[ Info: Iteration 30: P₀=1.196296975030748, σ=0.6726830135383639
loss(exp.(logP₀), exp.(logσ)) = 305.84053380990144
Log likelihood: -135.27157689515107
Log det ratio: 259.6894574755057
Scatter: 81.44845635399507
[ Info: Iteration 40: P₀=1.1656040639376481, σ=0.7066466931015057
loss(exp.(logP₀), exp.(logσ)) = 305.7292783955371
Log likelihood: -135.15966836841218
Log det ratio: 261.78045403778924
Scatter: 79.35876601646066
[ Info: Iteration 50: P₀=1.1742203891370186, σ=0.6938364708856879
loss(exp.(logP₀), exp.(logσ)) = 305.69406139219325
Log likelihood: -135.12776724917612
Log det ratio: 261.18719001890304
Scatter: 79.9453982671313
[ Info: Iteration 60: P₀=1.1839472472751473, σ=0.6921753821982651
loss(exp.(logP₀), exp.(logσ)) = 305.69540813134165
Log likelihood: -135.12989769153904
Log det ratio: 260.5233810478286
Scatter: 80.60763983177668
[ Info: Iteration 70: P₀=1.1853396498858086, σ=0.6996909491941182
loss(exp.(logP₀), exp.(logσ)) = 305.6975720877396
Log likelihood: -135.13191999505273
Log det ratio: 260.428864278341
Scatter: 80.70243990703281
[ Info: Iteration 80: P₀=1.183810872968514, σ=0.6930200179518355
loss(exp.(logP₀), exp.(logσ)) = 305.6941286580219
Log likelihood: -135.12862869299246
Log det ratio: 260.53264498070985
Scatter: 80.5983549493491
[ Info: Iteration 90: P₀=1.1825268343275124, σ=0.6954531885812366
loss(exp.(logP₀), exp.(logσ)) = 305.6925435931137
Log likelihood: -135.12711247012157
Log det ratio: 260.61992953993376
Scatter: 80.51093270605053
[ Info: Iteration 100: P₀=1.1819153281202348, σ=0.6963019635669977
loss(exp.(logP₀), exp.(logσ)) = 305.6927407371653
Log likelihood: -135.12732344801364
Log det ratio: 260.6615355448887
Scatter: 80.46929903341471
┌ Warning: Warning: no Flux model has been provided in the model. LaplaceRedux will use a standard MLP with 2 hidden layers with 20 neurons each and input and output layers compatible with the dataset.
└ @ LaplaceRedux ~/.julia/packages/LaplaceRedux/1bSKz/src/direct_mlj.jl:161
[ Info:  testing  interface for LaplaceClassifier
The number of epochs inserted is lower than the number of epochs already been trained. No update is necessary
 updating only the laplace optimization part
Test Summary:   | Pass  Total      Time
LaplaceRedux.jl |  143    143  31m54.4s
     Testing LaplaceRedux tests passed 

Testing completed after 1947.07s

PkgEval succeeded after 2026.1s