Removed unnecessary include and using statements

dscolby · Oct 29, 2024 · 1897aba · 1897aba
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diff --git a/docs/src/release_notes.md b/docs/src/release_notes.md
@@ -1,7 +1,11 @@
 # Release Notes
 These release notes adhere to the [keep a changelog](https://keepachangelog.com/en/1.0.0/) format. Below is a list of changes since CausalELM was first released.
 
-## Version [v0.7.0](https://github.com/dscolby/CausalELM.jl/releases/tag/v0.6.1) - 2024-06-22
+## Version [v0.8.0](https://github.com/dscolby/CausalELM.jl/releases/tag/v0.6.1) - 2024-10-28
+### Fixed
+*   Removed unnecessary include and using statements
+
+## Version [v0.7.0](https://github.com/dscolby/CausalELM.jl/releases/tag/v0.7.0) - 2024-06-22
 ### Added
 *   Implemented bagged ensemble of extreme learning machines to use with estimators [#67](https://github.com/dscolby/CausalELM.jl/issues/67)
 *   Implemented multithreading for testing the sensitivity of estimators to the counterfactual consistency assumption

diff --git a/src/models.jl b/src/models.jl
@@ -1,5 +1,4 @@
 using Random: shuffle
-using CausalELM: mean, var_type, clip_if_binary
 
 """
     ExtremeLearner(X, Y, hidden_neurons, activation)

diff --git a/test/test_estimators.jl b/test/test_estimators.jl
@@ -2,8 +2,6 @@ using Test
 using CausalELM
 using DataFrames
 
-include("../src/models.jl")
-
 x₀, y₀, x₁, y₁ = Float64.(rand(1:200, 100, 5)), rand(100), rand(10, 5), rand(10)
 its = InterruptedTimeSeries(x₀, y₀, x₁, y₁)
 estimate_causal_effect!(its)

diff --git a/test/test_metalearners.jl b/test/test_metalearners.jl
@@ -2,8 +2,6 @@ using CausalELM
 using Test
 using DataFrames
 
-include("../src/models.jl")
-
 x, t, y = rand(100, 5), Float64.([rand() < 0.4 for i in 1:100]), vec(rand(1:100, 100, 1))
 slearner1 = SLearner(x, t, y)
 estimate_causal_effect!(slearner1)

diff --git a/test/test_models.jl b/test/test_models.jl
@@ -1,8 +1,6 @@
 using Test
 using CausalELM
 
-include("../src/models.jl")
-
 # Test classification functionality using a simple XOR test borrowed from 
 # ExtremeLearning.jl
 x = [1.0 1.0; 0.0 1.0; 0.0 0.0; 1.0 0.0]
@@ -15,27 +13,27 @@ x1 = rand(20, 5)
 y1 = rand(20)
 x1test = rand(30, 5)
 
-mock_model = ExtremeLearner(x, y, 10, σ)
+mock_model = CausalELM.ExtremeLearner(x, y, 10, σ)
 
-m1 = ExtremeLearner(x, y, 10, σ)
-f1 = fit!(m1)
-predictions1 = predict(m1, x_test)
-predict_counterfactual!(m1, x_test)
-placebo1 = placebo_test(m1)
+m1 = CausalELM.ExtremeLearner(x, y, 10, σ)
+f1 = CausalELM.fit!(m1)
+predictions1 = CausalELM.predict(m1, x_test)
+CausalELM.predict_counterfactual!(m1, x_test)
+placebo1 = CausalELM.placebo_test(m1)
 
-m3 = ExtremeLearner(x1, y1, 10, σ)
-fit!(m3)
-predictions3 = predict(m3, x1test)
+m3 = CausalELM.ExtremeLearner(x1, y1, 10, σ)
+CausalELM.fit!(m3)
+predictions3 = CausalELM.predict(m3, x1test)
 
-m4 = ExtremeLearner(rand(100, 5), rand(100), 5, relu)
-fit!(m4)
+m4 = CausalELM.ExtremeLearner(rand(100, 5), rand(100), 5, relu)
+CausalELM.fit!(m4)
 
-nofit = ExtremeLearner(x1, y1, 10, σ)
-set_weights_biases(nofit)
+nofit = CausalELM.ExtremeLearner(x1, y1, 10, σ)
+CausalELM.set_weights_biases(nofit)
 
-ensemble = ELMEnsemble(big_x, big_y, 10000, 100, 5, 10, relu)
-fit!(ensemble)
-predictions = predict(ensemble, big_x)
+ensemble = CausalELM.ELMEnsemble(big_x, big_y, 10000, 100, 5, 10, relu)
+CausalELM.fit!(ensemble)
+predictions = CausalELM.predict(ensemble, big_x)
 
 @testset "Extreme Learning Machines" begin
     @testset "Extreme Learning Machine Structure" begin
@@ -71,8 +69,8 @@ predictions = predict(ensemble, big_x)
 
     @testset "Predict Before Fit" begin
         @test isdefined(nofit, :H) == true
-        @test_throws ErrorException predict(nofit, x1test)
-        @test_throws ErrorException placebo_test(nofit)
+        @test_throws ErrorException CausalELM.predict(nofit, x1test)
+        @test_throws ErrorException CausalELM.placebo_test(nofit)
     end
 
     @testset "Print Models" begin
@@ -84,10 +82,10 @@ end
 
 @testset "Extreme Learning Machine Ensembles" begin
     @testset "Initializing Ensembles" begin
-        @test ensemble isa ELMEnsemble
+        @test ensemble isa CausalELM.ELMEnsemble
         @test ensemble.X isa Array{Float64}
         @test ensemble.Y isa Array{Float64}
-        @test ensemble.elms isa Array{ExtremeLearner}
+        @test ensemble.elms isa Array{CausalELM.ExtremeLearner}
         @test length(ensemble.elms) == 100
         @test ensemble.feat_indices isa Vector{Vector{Int64}}
         @test length(ensemble.feat_indices) == 100

diff --git a/test/test_utilities.jl b/test/test_utilities.jl
@@ -57,7 +57,7 @@ estimate_causal_effect!(dm)
 x_fold, t_fold, y_fold = CausalELM.generate_folds(dm.X, dm.T, dm.Y, dm.folds)
 
 @testset "Moments" begin
-    @test mean([1, 2, 3]) == 2
+    @test CausalELM.mean([1, 2, 3]) == 2
     @test CausalELM.var([1, 2, 3]) == 1
 end