From 6c3a78fd2d03b2b4a9c52826c95988132a891dd0 Mon Sep 17 00:00:00 2001
From: Yuan-Ru Lin <yuanruleonlin@gmail.com>
Date: Sun, 24 Nov 2024 13:58:09 -0800
Subject: [PATCH 01/13] Initial commit for implementation of Affinity
 Propagation

---
 src/MLJClusteringInterface.jl | 79 +++++++++++++++++++++++++++++++++--
 1 file changed, 75 insertions(+), 4 deletions(-)

diff --git a/src/MLJClusteringInterface.jl b/src/MLJClusteringInterface.jl
index 4e9b17e..407358b 100644
--- a/src/MLJClusteringInterface.jl
+++ b/src/MLJClusteringInterface.jl
@@ -16,7 +16,7 @@ using Distances
 
 # ===================================================================
 ## EXPORTS
-export KMeans, KMedoids, DBSCAN, HierarchicalClustering
+export KMeans, KMedoids, AffinityPropagation, DBSCAN, HierarchicalClustering
 
 # ===================================================================
 ## CONSTANTS
@@ -95,10 +95,69 @@ function MMI.transform(model::KMedoids, fitresult, X)
     return MMI.table(X̃, prototype=X)
 end
 
+# # AFFINITY_PROPAGATION
 
-# # PREDICT FOR K_MEANS AND K_MEDOIDS
+@mlj_model mutable struct AffinityPropagation <: MMI.Unsupervised
+    damp::Float64 = 0.5::(0.0 ≤ _ < 1.0)
+    maxiter::Int = 200::(_ > 0)
+    tol::Float64 = 1e-6::(_ > 0)
+    preference::Union{Nothing,Float64} = nothing
+    metric::SemiMetric = SqEuclidean()
+end
+
+function MMI.fit(model::AffinityPropagation, verbosity::Int, X)
+    Xarray = MMI.matrix(X)'
+
+    # Compute similarity matrix using negative pairwise distances
+    S = -pairwise(model.metric, Xarray, dims=2)
+
+    # Set preferences on diagonal if specified
+    if !isnothing(model.preference)
+        fill!(view(S, diagind(S)), model.preference)
+    end
+
+    result = Cl.affinityprop(
+        S,
+        maxiter=model.maxiter,
+        tol=model.tol,
+        damp=model.damp
+    )
 
-function MMI.predict(model::Union{KMeans,KMedoids}, fitresult, Xnew)
+    # Get number of clusters and labels
+    exemplars = result.exemplars
+    k = length(exemplars)
+    cluster_labels = MMI.categorical(1:k)
+
+    # Store exemplar points as centers (similar to KMeans/KMedoids)
+    centers = view(Xarray, :, exemplars)
+
+    fitresult = (centers, cluster_labels)
+    cache = nothing
+    report = (
+        assignments=result.assignments,
+        cluster_labels=cluster_labels,
+        iterations=result.iterations,
+        converged=result.converged
+    )
+
+    return fitresult, cache, report
+end
+
+MMI.fitted_params(::AffinityPropagation, fitresult) = (exemplars=fitresult[1],)
+
+function MMI.transform(model::AffinityPropagation, fitresult, X)
+    # negative pairwise distance from samples to exemplars
+    X̃ = -pairwise(
+        model.metric,
+        MMI.matrix(X)',
+        fitresult[1], dims=2
+    )
+    return MMI.table(X̃, prototype=X)
+end
+
+# # PREDICT FOR K_MEANS, K_MEDOIDS and AFFINITY_PROPAGATION
+
+function MMI.predict(model::Union{KMeans,KMedoids,AffinityPropagation}, fitresult, Xnew)
     locations, cluster_labels = fitresult
     Xarray = MMI.matrix(Xnew)
     (n, p), k = size(Xarray), model.k
@@ -211,7 +270,7 @@ MMI.reporting_operations(::Type{<:HierarchicalClustering}) = (:predict,)
 # # METADATA
 
 metadata_pkg.(
-    (KMeans, KMedoids, DBSCAN, HierarchicalClustering),
+    (KMeans, KMedoids, DBSCAN, HierarchicalClustering, AffinityPropagation),
     name="Clustering",
     uuid="aaaa29a8-35af-508c-8bc3-b662a17a0fe5",
     url="https://github.com/JuliaStats/Clustering.jl",
@@ -251,6 +310,18 @@ metadata_model(
     path = "$(PKG).HierarchicalClustering"
 )
 
+metadata_model(
+    AffinityPropagation,
+    human_name = "Affinity Propagation clusterer",
+    input_scitype = Tuple{MMI.Table(Continuous)},  # Not sure about this part
+    path = "$(PKG).AffinityPropagation"
+)
+
+"""
+$(MMI.doc_header(AffinityPropagation))
+To be added later!
+"""
+
 """
 $(MMI.doc_header(KMeans))
 

From 5ff901300778ca45d69ff4a48b1bd1770b0b4c27 Mon Sep 17 00:00:00 2001
From: Yuan-Ru Lin <yuanruleonlin@gmail.com>
Date: Sun, 24 Nov 2024 14:15:09 -0800
Subject: [PATCH 02/13] Fix the input_scitype

---
 src/MLJClusteringInterface.jl | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/MLJClusteringInterface.jl b/src/MLJClusteringInterface.jl
index 407358b..fb89248 100644
--- a/src/MLJClusteringInterface.jl
+++ b/src/MLJClusteringInterface.jl
@@ -313,7 +313,7 @@ metadata_model(
 metadata_model(
     AffinityPropagation,
     human_name = "Affinity Propagation clusterer",
-    input_scitype = Tuple{MMI.Table(Continuous)},  # Not sure about this part
+    input_scitype = MMI.Table(Continuous),
     path = "$(PKG).AffinityPropagation"
 )
 

From 222e9197451c1423ba02db3ca8a7c3dd77175711 Mon Sep 17 00:00:00 2001
From: Yuan-Ru Lin <yuanruleonlin@gmail.com>
Date: Mon, 2 Dec 2024 15:28:24 -0800
Subject: [PATCH 03/13] Make AffinityPropagation a Static model

---
 src/MLJClusteringInterface.jl | 113 +++++++++++++++-------------------
 1 file changed, 51 insertions(+), 62 deletions(-)

diff --git a/src/MLJClusteringInterface.jl b/src/MLJClusteringInterface.jl
index fb89248..7ec7bb5 100644
--- a/src/MLJClusteringInterface.jl
+++ b/src/MLJClusteringInterface.jl
@@ -95,69 +95,9 @@ function MMI.transform(model::KMedoids, fitresult, X)
     return MMI.table(X̃, prototype=X)
 end
 
-# # AFFINITY_PROPAGATION
-
-@mlj_model mutable struct AffinityPropagation <: MMI.Unsupervised
-    damp::Float64 = 0.5::(0.0 ≤ _ < 1.0)
-    maxiter::Int = 200::(_ > 0)
-    tol::Float64 = 1e-6::(_ > 0)
-    preference::Union{Nothing,Float64} = nothing
-    metric::SemiMetric = SqEuclidean()
-end
-
-function MMI.fit(model::AffinityPropagation, verbosity::Int, X)
-    Xarray = MMI.matrix(X)'
-
-    # Compute similarity matrix using negative pairwise distances
-    S = -pairwise(model.metric, Xarray, dims=2)
-
-    # Set preferences on diagonal if specified
-    if !isnothing(model.preference)
-        fill!(view(S, diagind(S)), model.preference)
-    end
-
-    result = Cl.affinityprop(
-        S,
-        maxiter=model.maxiter,
-        tol=model.tol,
-        damp=model.damp
-    )
-
-    # Get number of clusters and labels
-    exemplars = result.exemplars
-    k = length(exemplars)
-    cluster_labels = MMI.categorical(1:k)
-
-    # Store exemplar points as centers (similar to KMeans/KMedoids)
-    centers = view(Xarray, :, exemplars)
-
-    fitresult = (centers, cluster_labels)
-    cache = nothing
-    report = (
-        assignments=result.assignments,
-        cluster_labels=cluster_labels,
-        iterations=result.iterations,
-        converged=result.converged
-    )
-
-    return fitresult, cache, report
-end
-
-MMI.fitted_params(::AffinityPropagation, fitresult) = (exemplars=fitresult[1],)
+# # PREDICT FOR K_MEANS AND K_MEDOIDS
 
-function MMI.transform(model::AffinityPropagation, fitresult, X)
-    # negative pairwise distance from samples to exemplars
-    X̃ = -pairwise(
-        model.metric,
-        MMI.matrix(X)',
-        fitresult[1], dims=2
-    )
-    return MMI.table(X̃, prototype=X)
-end
-
-# # PREDICT FOR K_MEANS, K_MEDOIDS and AFFINITY_PROPAGATION
-
-function MMI.predict(model::Union{KMeans,KMedoids,AffinityPropagation}, fitresult, Xnew)
+function MMI.predict(model::Union{KMeans,KMedoids}, fitresult, Xnew)
     locations, cluster_labels = fitresult
     Xarray = MMI.matrix(Xnew)
     (n, p), k = size(Xarray), model.k
@@ -267,6 +207,55 @@ end
 
 MMI.reporting_operations(::Type{<:HierarchicalClustering}) = (:predict,)
 
+# # AFFINITY_PROPAGATION
+
+@mlj_model mutable struct AffinityPropagation <: MMI.Static
+    damp::Float64 = 0.5::(0.0 ≤ _ < 1.0)
+    maxiter::Int = 200::(_ > 0)
+    tol::Float64 = 1e-6::(_ > 0)
+    preference::Union{Nothing,Float64} = nothing
+    metric::SemiMetric = SqEuclidean()
+end
+
+function MMI.predict(model::AffinityPropagation, ::Nothing, X)
+    Xarray = MMI.matrix(X)'
+
+    # Compute similarity matrix using negative pairwise distances
+    S = -pairwise(model.metric, Xarray, dims=2)
+
+    # Set preferences on diagonal if specified
+    if !isnothing(model.preference)
+        fill!(view(S, diagind(S)), model.preference)
+    end
+
+    result = Cl.affinityprop(
+        S,
+        maxiter=model.maxiter,
+        tol=model.tol,
+        damp=model.damp
+    )
+
+    # Get number of clusters and labels
+    exemplars = result.exemplars
+    k = length(exemplars)
+    cluster_labels = MMI.categorical(1:k)
+
+    # Store exemplar points as centers (similar to KMeans/KMedoids)
+    centers = view(Xarray, :, exemplars)
+
+    report = (
+        exemplars=exemplars,
+        centers=centers,
+        cluster_labels=cluster_labels,
+        iterations=result.iterations,
+        converged=result.converged
+    )
+
+    return MMI.caterogical(result.assignments), report
+end
+
+MMI.reporting_operations(::Type{<:AffinityPropagation}) = (:predict,)
+
 # # METADATA
 
 metadata_pkg.(

From 2f187355d5db77f088f677fc669810112e978acd Mon Sep 17 00:00:00 2001
From: Yuan-Ru Lin <yuanruleonlin@gmail.com>
Date: Mon, 2 Dec 2024 16:44:56 -0800
Subject: [PATCH 04/13] Fix missing dep

---
 Project.toml                  | 2 ++
 src/MLJClusteringInterface.jl | 3 ++-
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/Project.toml b/Project.toml
index 56b46d6..0845e0a 100644
--- a/Project.toml
+++ b/Project.toml
@@ -6,10 +6,12 @@ version = "0.1.11"
 [deps]
 Clustering = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5"
 Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea"
 
 [compat]
 Clustering = "0.15"
 Distances = "0.9, 0.10"
+LinearAlgebra = "1.11.0"
 MLJModelInterface = "1.4"
 julia = "1.6"
diff --git a/src/MLJClusteringInterface.jl b/src/MLJClusteringInterface.jl
index 7ec7bb5..828c218 100644
--- a/src/MLJClusteringInterface.jl
+++ b/src/MLJClusteringInterface.jl
@@ -13,6 +13,7 @@ import MLJModelInterface: Continuous, Count, Finite, Multiclass, Table, OrderedF
     @mlj_model, metadata_model, metadata_pkg
 
 using Distances
+using LinearAlgebra
 
 # ===================================================================
 ## EXPORTS
@@ -251,7 +252,7 @@ function MMI.predict(model::AffinityPropagation, ::Nothing, X)
         converged=result.converged
     )
 
-    return MMI.caterogical(result.assignments), report
+    return MMI.categorical(result.assignments), report
 end
 
 MMI.reporting_operations(::Type{<:AffinityPropagation}) = (:predict,)

From d1b95e93d5c4c6dcd1ce72e5e704ac456afb77d6 Mon Sep 17 00:00:00 2001
From: Yuan-Ru Lin <yuanruleonlin@gmail.com>
Date: Mon, 2 Dec 2024 16:46:21 -0800
Subject: [PATCH 05/13] Add doc

---
 src/MLJClusteringInterface.jl | 74 ++++++++++++++++++++++++++++++++---
 1 file changed, 69 insertions(+), 5 deletions(-)

diff --git a/src/MLJClusteringInterface.jl b/src/MLJClusteringInterface.jl
index 828c218..dbd86d2 100644
--- a/src/MLJClusteringInterface.jl
+++ b/src/MLJClusteringInterface.jl
@@ -307,11 +307,6 @@ metadata_model(
     path = "$(PKG).AffinityPropagation"
 )
 
-"""
-$(MMI.doc_header(AffinityPropagation))
-To be added later!
-"""
-
 """
 $(MMI.doc_header(KMeans))
 
@@ -675,4 +670,73 @@ report(mach).cutter(h = 2.5)
 """
 HierarchicalClustering
 
+"""
+$(MMI.doc_header(AffinityPropagation))
+
+[Affinity Propagation](https://en.wikipedia.org/wiki/Affinity_propagation) is a clustering algorithm based on the concept of "message passing" between data points. More information is available at the [Clustering.jl documentation](https://juliastats.org/Clustering.jl/stable/index.html). Use `predict` to get cluster assignments. Indices of the exemplars, their values, etc, are accessed from the machine report (see below).
+
+This is a static implementation, i.e., it does not generalize to new data instances, and
+there is no training data. For clusterers that do generalize, see [`KMeans`](@ref) or
+[`KMedoids`](@ref).
+
+In MLJ or MLJBase, create a machine with
+
+    mach = machine(model)
+
+# Hyper-parameters
+
+- `damp = 0.5`: damping factor
+
+- `maxiter = 200`: maximum number of iteration
+
+- `tol = 1e-6`: tolerance for converenge
+
+- `preference = nothing`: the value of the diagonal elements of the similarity matrix
+
+- `metric = SqEuclidean`: metric (see `Distances.jl` for available metrics)
+
+# Operations
+
+- `predict(mach, X)`: return cluster label assignments, as an unordered
+  `CategoricalVector`. Here `X` is any table of input features (eg, a `DataFrame`) whose
+  columns are of scitype `Continuous`; check column scitypes with `schema(X)`.
+
+# Report
+
+After calling `predict(mach)`, the fields of `report(mach)`  are:
+
+- exemplars: indices of the data picked as exemplars in `X`
+
+- centers: positions of the exemplars in the feature space
+
+- cluster_labels: labels of clusters given to each datum in `X`
+
+- iterations: the number of iteration run by the algorithm
+
+- converged: whether or not the algorithm converges by the maximum iteration
+
+# Examples
+
+```
+using MLJ, MLJClusteringInterface
+
+X, labels = make_moons(400, noise=0.9, rng=1)
+
+AffinityPropagation = @load AffinityPropagation pkg=Clustering
+model = AffinityPropagation(preferences=-10.0)
+mach = machine(model)
+
+# compute and output cluster assignments for observations in `X`:
+yhat = predict(mach, X)
+
+# Get the positions of the exemplars
+report(mach).centers
+
+# Plot clustering result
+using GLMakie
+scatter(MLJ.matrix(X)', color=yhat.refs)
+```
+"""
+AffinityPropagation
+
 end # module

From c2f5d18c5a39eaf3a1a95569cd2616818394a786 Mon Sep 17 00:00:00 2001
From: Yuan-Ru Lin <yuanruleonlin@gmail.com>
Date: Mon, 2 Dec 2024 17:00:49 -0800
Subject: [PATCH 06/13] Fix typo of a var in doc

---
 src/MLJClusteringInterface.jl | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/MLJClusteringInterface.jl b/src/MLJClusteringInterface.jl
index dbd86d2..c4745d0 100644
--- a/src/MLJClusteringInterface.jl
+++ b/src/MLJClusteringInterface.jl
@@ -723,7 +723,7 @@ using MLJ, MLJClusteringInterface
 X, labels = make_moons(400, noise=0.9, rng=1)
 
 AffinityPropagation = @load AffinityPropagation pkg=Clustering
-model = AffinityPropagation(preferences=-10.0)
+model = AffinityPropagation(preference=-10.0)
 mach = machine(model)
 
 # compute and output cluster assignments for observations in `X`:

From 7d0453c583e169c192bad603d1fe15dc1ae2eb24 Mon Sep 17 00:00:00 2001
From: Yuan-Ru Lin <yuanruleonlin@gmail.com>
Date: Mon, 2 Dec 2024 18:04:15 -0800
Subject: [PATCH 07/13] Set default preference to be median similarity of all
 pairs

---
 Project.toml                  |  2 ++
 src/MLJClusteringInterface.jl | 14 +++++++++++---
 2 files changed, 13 insertions(+), 3 deletions(-)

diff --git a/Project.toml b/Project.toml
index 0845e0a..73bea52 100644
--- a/Project.toml
+++ b/Project.toml
@@ -8,10 +8,12 @@ Clustering = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5"
 Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea"
+StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 
 [compat]
 Clustering = "0.15"
 Distances = "0.9, 0.10"
 LinearAlgebra = "1.11.0"
 MLJModelInterface = "1.4"
+StatsBase = "0.34.3"
 julia = "1.6"
diff --git a/src/MLJClusteringInterface.jl b/src/MLJClusteringInterface.jl
index c4745d0..45f9496 100644
--- a/src/MLJClusteringInterface.jl
+++ b/src/MLJClusteringInterface.jl
@@ -14,6 +14,7 @@ import MLJModelInterface: Continuous, Count, Finite, Multiclass, Table, OrderedF
 
 using Distances
 using LinearAlgebra
+using StatsBase
 
 # ===================================================================
 ## EXPORTS
@@ -224,11 +225,18 @@ function MMI.predict(model::AffinityPropagation, ::Nothing, X)
     # Compute similarity matrix using negative pairwise distances
     S = -pairwise(model.metric, Xarray, dims=2)
 
-    # Set preferences on diagonal if specified
-    if !isnothing(model.preference)
-        fill!(view(S, diagind(S)), model.preference)
+    diagonal_element = if !isnothing(model.preference)
+        model.preference
+    else
+        # Get the median out of all pairs of similarity, that is, values above
+        # the diagonal line.
+        # Such default choice is mentioned in the algorithm's wiki article
+        iuppertri = triu!(trues(size(S)),1)
+        median(S[iuppertri])
     end
 
+    fill!(view(S, diagind(S)), diagonal_element)
+
     result = Cl.affinityprop(
         S,
         maxiter=model.maxiter,

From 20b3b8dd6dc908598c2de3943eebcfc1336f23fd Mon Sep 17 00:00:00 2001
From: "Leon (Yuan-Ru) Lin" <yuanruleonlin@gmail.com>
Date: Thu, 5 Dec 2024 16:03:32 -0800
Subject: [PATCH 08/13] Remove redundant import of MLJClusteringInterface

Co-authored-by: Anthony Blaom, PhD <anthony.blaom@gmail.com>
---
 src/MLJClusteringInterface.jl | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/MLJClusteringInterface.jl b/src/MLJClusteringInterface.jl
index 45f9496..43f2cfe 100644
--- a/src/MLJClusteringInterface.jl
+++ b/src/MLJClusteringInterface.jl
@@ -726,7 +726,7 @@ After calling `predict(mach)`, the fields of `report(mach)`  are:
 # Examples
 
 ```
-using MLJ, MLJClusteringInterface
+using MLJ
 
 X, labels = make_moons(400, noise=0.9, rng=1)
 

From 06a2d885f549781ecc84499d67eb6803f92f8ba3 Mon Sep 17 00:00:00 2001
From: "Leon (Yuan-Ru) Lin" <yuanruleonlin@gmail.com>
Date: Thu, 5 Dec 2024 16:04:09 -0800
Subject: [PATCH 09/13] Specify where SqEuclidean() lives

Co-authored-by: Anthony Blaom, PhD <anthony.blaom@gmail.com>
---
 src/MLJClusteringInterface.jl | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/MLJClusteringInterface.jl b/src/MLJClusteringInterface.jl
index 43f2cfe..68747a9 100644
--- a/src/MLJClusteringInterface.jl
+++ b/src/MLJClusteringInterface.jl
@@ -701,7 +701,7 @@ In MLJ or MLJBase, create a machine with
 
 - `preference = nothing`: the value of the diagonal elements of the similarity matrix
 
-- `metric = SqEuclidean`: metric (see `Distances.jl` for available metrics)
+- `metric = Distances.SqEuclidean()`: metric (see `Distances.jl` for available metrics)
 
 # Operations
 

From 9b489262431fe1fc55e3a561aa21c5380e806429 Mon Sep 17 00:00:00 2001
From: "Leon (Yuan-Ru) Lin" <yuanruleonlin@gmail.com>
Date: Thu, 5 Dec 2024 16:05:03 -0800
Subject: [PATCH 10/13] Loose versions of newly added deps

Co-authored-by: Anthony Blaom, PhD <anthony.blaom@gmail.com>
---
 Project.toml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Project.toml b/Project.toml
index 73bea52..da904eb 100644
--- a/Project.toml
+++ b/Project.toml
@@ -13,7 +13,7 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 [compat]
 Clustering = "0.15"
 Distances = "0.9, 0.10"
-LinearAlgebra = "1.11.0"
+LinearAlgebra = "1"
 MLJModelInterface = "1.4"
-StatsBase = "0.34.3"
+StatsBase = "0.34"
 julia = "1.6"

From 45dd5839f4f23b2cb62d3eff30265da3361588f9 Mon Sep 17 00:00:00 2001
From: Yuan-Ru Lin <yuanruleonlin@gmail.com>
Date: Thu, 5 Dec 2024 16:17:27 -0800
Subject: [PATCH 11/13] Document how an unspecified preference is handled

---
 src/MLJClusteringInterface.jl | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/MLJClusteringInterface.jl b/src/MLJClusteringInterface.jl
index 68747a9..3dc83e0 100644
--- a/src/MLJClusteringInterface.jl
+++ b/src/MLJClusteringInterface.jl
@@ -699,7 +699,7 @@ In MLJ or MLJBase, create a machine with
 
 - `tol = 1e-6`: tolerance for converenge
 
-- `preference = nothing`: the value of the diagonal elements of the similarity matrix
+- `preference = nothing`: the (single float) value of the diagonal elements of the similarity matrix. If unspecified, choose median (negative) similarity of all pairs as mentioned [here](https://en.wikipedia.org/wiki/Affinity_propagation#Algorithm)
 
 - `metric = Distances.SqEuclidean()`: metric (see `Distances.jl` for available metrics)
 

From fc87b92781ff1d254e2026282569faa64678d997 Mon Sep 17 00:00:00 2001
From: Yuan-Ru Lin <yuanruleonlin@gmail.com>
Date: Thu, 5 Dec 2024 16:19:42 -0800
Subject: [PATCH 12/13] Bump supported Julia version to 1.10

---
 .github/workflows/ci.yml | 2 +-
 Project.toml             | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 3713c1b..361c6c9 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -15,7 +15,7 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.6'
+          - '1.10'
           - '1'
         os:
           - ubuntu-latest
diff --git a/Project.toml b/Project.toml
index da904eb..9f813f0 100644
--- a/Project.toml
+++ b/Project.toml
@@ -16,4 +16,4 @@ Distances = "0.9, 0.10"
 LinearAlgebra = "1"
 MLJModelInterface = "1.4"
 StatsBase = "0.34"
-julia = "1.6"
+julia = "1.10"

From da6e69cdde8f7e0082cb61d09d64d14576c16bd3 Mon Sep 17 00:00:00 2001
From: Yuan-Ru Lin <yuanruleonlin@gmail.com>
Date: Thu, 5 Dec 2024 16:54:52 -0800
Subject: [PATCH 13/13] Add test for Affinity Propagation

---
 test/runtests.jl | 34 +++++++++++++++++++++++++++++++++-
 1 file changed, 33 insertions(+), 1 deletion(-)

diff --git a/test/runtests.jl b/test/runtests.jl
index b0bc903..b42088e 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -150,8 +150,40 @@ end
     @test report(mach).dendrogram.heights == dendro.heights
 end
 
+# # AffinityPropagation
+
+@testset "AffinityPropagation" begin
+    X = table(stack(Iterators.partition(0.5:0.5:20, 5))')
+
+    # Test case 1: preference == median (negative) similarity (i.e. unspecified)
+    mach = machine(AffinityPropagation())
+
+    yhat = predict(mach, X)
+    @test yhat == [1, 1, 1, 1, 2, 2, 2, 2]
+
+    _report = report(mach)
+    @test _report.exemplars == [2, 7]
+    @test _report.centers == [3.0 15.5; 3.5 16.0; 4.0 16.5; 4.5 17.0; 5.0 17.5]
+    @test _report.cluster_labels == [1, 2]
+    @test _report.iterations == 50
+    @test _report.converged == true
+
+    # Test case 2: |preference| too large
+    mach2 = machine(AffinityPropagation(preference=-20.0))
+
+    yhat = predict(mach2, X)
+    @test yhat == [1, 2, 3, 4, 5, 6, 7, 8]
+
+    _report = report(mach2)
+    @test _report.exemplars == [1, 2, 3, 4, 5, 6, 7, 8]
+    @test _report.centers == matrix(X)'
+    @test _report.cluster_labels == [1, 2, 3, 4, 5, 6, 7, 8]
+    @test _report.iterations == 32
+    @test _report.converged == true
+end
+
 @testset "MLJ interface" begin
-    models = [KMeans, KMedoids, DBSCAN, HierarchicalClustering]
+    models = [KMeans, KMedoids, DBSCAN, HierarchicalClustering, AffinityPropagation]
     failures, summary = MLJTestInterface.test(
         models,
         X;