Merge pull request #504 from JuliaTrustworthyAI/update-deps

Updating Flux and EnergySamplers
JuliaTrustworthyAI · Dec 31, 2024 · 3961fd4 · 3961fd4 · pat-alt · Dec 31, 2024
2 parents ee300c4 + 997b0b3
commit 3961fd4
Show file tree

Hide file tree

Showing 18 changed files with 1,366 additions and 704 deletions.
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -6,7 +6,17 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 
 *Note*: We try to adhere to these practices as of version [v1.1.1].
 
-## Version [1.4.0]
+## Version [1.4.1] - 2024-12-19
+
+### Changed
+
+- Updated dependencies. [#504]
+
+### Removed
+
+- Removed everything related to GrowingSpheres. [#504]
+
+## Version [1.4.0] - 2024-12-19
 
 ### Added
 

diff --git a/Project.toml b/Project.toml
@@ -1,7 +1,7 @@
 name = "CounterfactualExplanations"
 uuid = "2f13d31b-18db-44c1-bc43-ebaf2cff0be0"
 authors = ["Patrick Altmeyer <[email protected]> and contributors"]
-version = "1.4.0"
+version = "1.4.1"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
@@ -44,13 +44,13 @@ NeuroTreeExt = "NeuroTreeModels"
 [compat]
 Aqua = "0.8"
 CategoricalArrays = "0.10"
-CausalInference = "0.17.0"
+CausalInference = "0.17, 0.18"
 ChainRulesCore = "1.15"
 DataFrames = "1"
 DecisionTree = "0.12.3, 0.12.4"
 Distributions = "0.25.97"
 EnergySamplers = "1.0"
-Flux = "0.12, 0.13, 0.14"
+Flux = "0.12, 0.13, 0.14, 0.15, 0.16"
 Graphs = "1.11.1"
 JointEnergyModels = "0.1.7"
 LaplaceRedux = "0.1.4, 0.2, 1.0"

diff --git a/README.md b/README.md
@@ -97,7 +97,9 @@ ce = generate_counterfactual(
 plot(ce)
 ```
 
-![](README_files/figure-commonmark/cell-3-output-1.svg)
+    [ Info: No target label supplied, using first.
+
+![](README_files/figure-commonmark/cell-3-output-2.svg)
 
 ### Example: Give Me Some Credit
 
@@ -148,7 +150,7 @@ To this end, we specify a counterfactual generator of our choice:
 generator = DiCEGenerator(λ=[0.1,0.3])
 ```
 
-Here, we have chosen to use the `GradientBasedGenerator` to move the individual from its factual label 1 to the target label 2.
+Here, we have chosen to use the `CounterfactualExplanations.Generators.GradientBasedGenerator` to move the individual from its factual label 1 to the target label 2.
 
 With all of our ingredients specified, we finally generate counterfactuals using a simple API call:
 
@@ -162,7 +164,9 @@ ce = generate_counterfactual(
 
 The plot below shows the resulting counterfactual path:
 
-![](README_files/figure-commonmark/cell-16-output-1.svg)
+    [ Info: No target label supplied, using first.
+
+![](README_files/figure-commonmark/cell-16-output-2.svg)
 
 ## ☑️ Implemented Counterfactual Generators
 
@@ -176,7 +180,6 @@ Currently, the following counterfactual generators are implemented:
 - Generic
 - GravitationalGenerator (Altmeyer et al. 2023)
 - Greedy (Schut et al. 2021)
-- GrowingSpheres (Laugel et al. 2017)
 - MINT (Karimi et al. 2020) (**causal CE**)
 - PROBE (Pawelczyk et al. 2023)
 - REVISE (Joshi et al. 2019)
@@ -251,8 +254,6 @@ Kaggle. 2011. “Give Me Some Credit, Improve on the State of the Art in Credit
 
 Karimi, Amir-Hossein, Julius Von Kügelgen, Bernhard Schölkopf, and Isabel Valera. 2020. “Algorithmic Recourse Under Imperfect Causal Knowledge: A Probabilistic Approach.” <https://arxiv.org/abs/2006.06831>.
 
-Laugel, Thibault, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, and Marcin Detyniecki. 2017. “Inverse Classification for Comparison-Based Interpretability in Machine Learning.” <https://arxiv.org/abs/1712.08443>.
-
 Mothilal, Ramaravind K, Amit Sharma, and Chenhao Tan. 2020. “Explaining Machine Learning Classifiers Through Diverse Counterfactual Explanations.” In *Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency*, 607–17. <https://doi.org/10.1145/3351095.3372850>.
 
 Pawelczyk, Martin, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci, and Himabindu Lakkaraju. 2023. “Probabilistically Robust Recourse: Navigating the Trade-Offs Between Costs and Robustness in Algorithmic Recourse.” <https://arxiv.org/abs/2203.06768>.

diff --git a/README.qmd b/README.qmd
@@ -9,7 +9,9 @@ crossref:
   tbl-prefix: Table
 bibliography: https://raw.githubusercontent.com/pat-alt/bib/main/bib.bib
 output: asis
-jupyter: julia-1.10
+engine: julia
+julia: 
+  exeflags: ["--project=docs/"]
 execute: 
   freeze: auto
   eval: true

diff --git a/README_files/figure-commonmark/cell-11-output-1.svg b/README_files/figure-commonmark/cell-11-output-1.svg
diff --git a/README_files/figure-commonmark/cell-16-output-2.svg b/README_files/figure-commonmark/cell-16-output-2.svg
diff --git a/README_files/figure-commonmark/cell-3-output-2.svg b/README_files/figure-commonmark/cell-3-output-2.svg
diff --git a/README_files/figure-commonmark/cell-6-output-1.svg b/README_files/figure-commonmark/cell-6-output-1.svg
diff --git a/docs/setup_docs.jl b/docs/setup_docs.jl
@@ -34,7 +34,7 @@ setup_docs = quote
 
     # Setup:
     theme(:wong)
-    Random.seed!(2022)
+    Random.seed!(2025)
     synthetic = TaijaData.load_synthetic_data()
     ENV["DATADEPS_ALWAYS_ACCEPT"] = "true"
 

diff --git a/docs/src/_intro.qmd b/docs/src/_intro.qmd
@@ -115,6 +115,8 @@ plot(ce)
 ```{julia}
 #| echo: false
 
+Random.seed!(2024)
+
 # Data and Model:
 data = TaijaData.load_gmsc(10000)
 counterfactual_data = CounterfactualExplanations.DataPreprocessing.CounterfactualData(data...)
@@ -377,7 +379,6 @@ Currently, the following counterfactual generators are implemented:
 -   Generic
 -   GravitationalGenerator [@altmeyer2023endogenous]
 -   Greedy [@schut2021generating]
--   GrowingSpheres [@laugel2017inverse]
 -   MINT [@karimi2020algorithmic] (**causal CE**)
 -   PROBE [@pawelczyk2022probabilistically]
 -   REVISE [@joshi2019realistic]

diff --git a/docs/src/index.qmd b/docs/src/index.qmd
@@ -1,3 +1,9 @@
+---
+engine: julia
+julia: 
+  exeflags: ["--project=docs/"]
+---
+
 ```@meta
 CurrentModule = CounterfactualExplanations
 ```

diff --git a/docs/src/www/mnist_factual.png b/docs/src/www/mnist_factual.png
diff --git a/src/counterfactuals/growing_spheres.jl b/src/counterfactuals/growing_spheres.jl
diff --git a/src/generative_models/encoders.jl b/src/generative_models/encoders.jl
@@ -11,7 +11,7 @@ struct Encoder
     μ::Any
     logσ::Any
 end
-Flux.@functor Encoder
+Flux.@layer Encoder
 
 function Encoder(input_dim::Int, latent_dim::Int, hidden_dim::Int; activation=sigmoid)
     return Encoder(

diff --git a/src/generative_models/vae.jl b/src/generative_models/vae.jl
@@ -58,7 +58,7 @@ function VAE(input_dim; kws...)
     return VAE(encoder, decoder, args, false)
 end
 
-Flux.@functor VAE
+Flux.@layer VAE
 
 function Flux.trainable(generative_model::VAE)
     return (encoder=generative_model.encoder, decoder=generative_model.decoder)