rename n_iter to max_iter to avoid breaking change with v1.5

JuliaAI · Jan 20, 2024 · bd5086b · bd5086b
1 parent 7aa9594
commit bd5086b
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Showing 3 changed files with 12 additions and 12 deletions.
diff --git a/src/models/linear-regressors.jl b/src/models/linear-regressors.jl
@@ -1,7 +1,6 @@
 const ARDRegressor_ = sklm(:ARDRegression)
 @sk_reg mutable struct ARDRegressor <: MMI.Deterministic
-    # TODO: rename `n_iter` to `max_iter` in v1.5
-    n_iter::Int               = 300::(_ > 0)
+    max_iter::Int             = 300::(_ > 0)
     tol::Float64              = 1e-3::(_ > 0)
     alpha_1::Float64          = 1e-6::(_ > 0)
     alpha_2::Float64          = 1e-6::(_ > 0)
@@ -19,16 +18,16 @@ MMI.fitted_params(model::ARDRegressor, (fitresult, _, _)) = (
     alpha     = fitresult.alpha_,
     lambda    = fitresult.lambda_,
     sigma     = fitresult.sigma_,
-    scores    = fitresult.scores_
+    scores    = fitresult.scores_,
+    n_iter    = fitresult.n_iter_,
     )
 add_human_name_trait(ARDRegressor, "Bayesian ARD regressor")
 @sk_feature_importances ARDRegressor
 
 # =============================================================================
 const BayesianRidgeRegressor_ = sklm(:BayesianRidge)
 @sk_reg mutable struct BayesianRidgeRegressor <: MMI.Deterministic
-    # TODO: rename `n_iter` to `max_iter` in v1.5
-    n_iter::Int         = 300::(_ ≥ 1)
+    max_iter::Int       = 300::(_ ≥ 1)
     tol::Float64        = 1e-3::(_ > 0)
     alpha_1::Float64    = 1e-6::(_ > 0)
     alpha_2::Float64    = 1e-6::(_ > 0)
@@ -45,7 +44,8 @@ MMI.fitted_params(model::BayesianRidgeRegressor, (fitresult, _, _)) = (
     alpha     = fitresult.alpha_,
     lambda    = fitresult.lambda_,
     sigma     = fitresult.sigma_,
-    scores    = fitresult.scores_
+    scores    = fitresult.scores_,
+    n_iter    = fitresult.n_iter_,
     )
 add_human_name_trait(BayesianRidgeRegressor, "Bayesian ridge regressor")
 @sk_feature_importances BayesianRidgeRegressor

diff --git a/test/macros.jl b/test/macros.jl
@@ -1,12 +1,12 @@
 @testset "reg-mdl" begin
     m = ARDRegressor()
     @test m isa MB.Deterministic
-    @test m.n_iter > 0
+    @test m.max_iter > 0
     # set
-    m = ARDRegressor(n_iter=100)
-    @test m.n_iter == 100
+    m = ARDRegressor(max_iter=100)
+    @test m.max_iter == 100
     # clean method
-    @test @test_logs (:warn, r"Constraint") ARDRegressor(n_iter=-5).n_iter > 0
+    @test @test_logs (:warn, r"Constraint") ARDRegressor(max_iter=-5).max_iter > 0
 
     # traits
     @test MB.load_path(m) == "MLJScikitLearnInterface.ARDRegressor"

diff --git a/test/models/linear-regressors.jl b/test/models/linear-regressors.jl
@@ -23,8 +23,8 @@ models = (
     )
 
 fparams = (
-    ARDRegressor=(:coef, :intercept, :alpha, :lambda, :sigma, :scores),
-    BayesianRidgeRegressor=(:coef, :intercept, :alpha, :lambda, :sigma, :scores),
+    ARDRegressor=(:coef, :intercept, :alpha, :lambda, :sigma, :scores, :n_iter),
+    BayesianRidgeRegressor=(:coef, :intercept, :alpha, :lambda, :sigma, :scores, :n_iter),
     ElasticNetRegressor=(:coef, :intercept),
     ElasticNetCVRegressor=(:coef, :intercept, :l1_ratio, :mse_path, :alphas),
     HuberRegressor=(:coef, :intercept, :scale, :outliers),