Remove MLJ extension

Author: juliohm

Project.toml

@@ -17,20 +17,13 @@ StyledStrings = "f489334b-da3d-4c2e-b8f0-e476e12c162b"
 TableTransforms = "0d432bfd-3ee1-4ac1-886a-39f05cc69a3e"
 Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 
-[weakdeps]
-MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea"
-
-[extensions]
-StatsLearnModelsMLJModelInterfaceExt = "MLJModelInterface"
-
 [compat]
 ColumnSelectors = "1.0"
 DataScienceTraits = "1.0"
 DecisionTree = "0.12"
 Distances = "0.10"
 Distributions = "0.25"
 GLM = "1.9"
-MLJModelInterface = "1.9"
 NearestNeighbors = "0.4"
 PrettyTables = "3.0.2"
 StatsBase = "0.33, 0.34"

README.md

@@ -37,12 +37,11 @@ preds = learn(test)
 ```
 
 The function `label` is used to tag columns of the table with target labels,
-which can be categorical or continuous. All remaining columns are assumed to
-be predictors.
+which can be categorical or continuous. Remaining columns are assumed to be
+predictors.
 
-The package exports native Julia models from various packages
-in the ecosystem. It is also possible to use models from the
-[MLJ.jl](https://github.com/JuliaAI/MLJ.jl) stack.
+Please check the [models](https://github.com/JuliaML/StatsLearnModels.jl/tree/main/src/models)
+directory for documentation on available models and their parameters.
 
 [build-img]: https://img.shields.io/github/actions/workflow/status/JuliaML/StatsLearnModels.jl/CI.yml?branch=main&style=flat-square
 [build-url]: https://github.com/JuliaML/StatsLearnModels.jl/actions

ext/StatsLearnModelsMLJModelInterfaceExt.jl

@@ -2,8 +2,6 @@
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a"
-MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
-MLJDecisionTreeInterface = "c6f25543-311c-4c74-83dc-3ea6d1015661"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 TableTransforms = "0d432bfd-3ee1-4ac1-886a-39f05cc69a3e"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

test/Project.toml

@@ -7,8 +7,6 @@ using Test
 using GLM: ProbitLink
 using Distributions: Binomial
 
-import MLJ, MLJDecisionTreeInterface
-
 const SLM = StatsLearnModels
 
 @testset "StatsLearnModels.jl" begin
@@ -99,17 +97,4 @@ const SLM = StatsLearnModels
     accuracy = count(pred.target .== iris.target[test]) / length(test)
     @test accuracy > 0.9
   end
-
-  @testset "MLJ" begin
-    Random.seed!(123)
-    iris = DataFrame(MLJ.load_iris())
-    input = iris[:, Not(:target)]
-    output = iris[:, [:target]]
-    train, test = MLJ.partition(1:nrow(input), 0.7, rng=123)
-    Tree = MLJ.@load(DecisionTreeClassifier, pkg = DecisionTree, verbosity = 0)
-    fmodel = SLM.fit(Tree(), input[train, :], output[train, :])
-    pred = SLM.predict(fmodel, input[test, :])
-    accuracy = count(pred.target .== output.target[test]) / length(test)
-    @test accuracy > 0.9
-  end
 end