add models to tests

Project.toml

@@ -56,10 +56,13 @@ julia = "1.10"
 
 [extras]
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+EvoTrees = "f6006082-12f8-11e9-0c9c-0d5d367ab1e5"
+MLJDecisionTreeInterface = "c6f25543-311c-4c74-83dc-3ea6d1015661"
+MLJGLMInterface = "caf8df21-4939-456d-ac9c-5fefbfb04c0c"
 MLJModels = "d491faf4-2d78-11e9-2867-c94bc002c0b7"
 Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Distributions", "Makie", "MLJModels", "StableRNGs", "Test"]
+test = ["Distributions", "EvoTrees", "Makie", "MLJDecisionTreeInterface", "MLJGLMInterface", "MLJModels", "StableRNGs", "Test"]

test/runtests.jl

@@ -3,6 +3,10 @@ import SpeciesDistributionModels as SDM
 using StableRNGs, Distributions, Test
 using Makie
 
+using MLJGLMInterface: LinearBinaryClassifier
+using EvoTrees: EvoTreeClassifier
+using MLJDecisionTreeInterface: RandomForestClassifier
+
 rng = StableRNG(0)
 #using Random; rng = Random.GLOBAL_RNG
 # some mock data
@@ -22,10 +26,10 @@ presencedata = (a = rand(rng, n), b = rand(rng, n).^2, c = sqrt.(rand(rng, n)))
 
     ## ensemble
     models = (
-        rf = SDM.random_forest(; rng),
-        rf2 = OneHotEncoder() |> SDM.random_forest(; max_depth = 3, rng),
-        lm = SDM.linear_model(),
-        brt = SDM.boosted_regression_tree(; rng)
+        rf = SDM.RandomForestClassifier(; rng),
+        rf2 = OneHotEncoder() |> SDM.RandomForestClassifier(; max_depth = 3, rng),
+        lm = SDM.LinearBinaryClassifier(),
+        brt = SDM.EvoTreeClassifier(; rng)
     )
 
     ensemble = sdm(data, models;