fix extras deps

Author: PasoStudio73

Project.toml

@@ -1,12 +1,7 @@
 name = "SoleModels"
 uuid = "4249d9c7-3290-4ddd-961c-e1d3ec2467f8"
 license = "MIT"
-authors = [
-    "Michele GHIOTTI",
-    "Giovanni PAGLIARINI",
-    "Edoardo PONSANESI",
-    "Eduard I. STAN",
-]
+authors = ["Michele GHIOTTI", "Giovanni PAGLIARINI", "Edoardo PONSANESI", "Eduard I. STAN"]
 version = "0.10.0"
 
 [deps]
@@ -45,7 +40,6 @@ XGBoostExt = "XGBoost"
 
 [compat]
 AbstractTrees = "0.4"
-BenchmarkTools = "1"
 CSV = "0.10"
 CategoricalArrays = "0.10"
 DataFrames = "1"
@@ -74,19 +68,23 @@ ZipFile = "0.10"
 julia = "1"
 
 [extras]
-BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
+CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+DecisionTree = "7806a523-6efd-50cb-b5f6-3fa6f1930dbb"
+FunctionWrappers = "069b7b12-0de2-55c6-9aab-29f3d0a68a2e"
 InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
 MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
 MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 MLJDecisionTreeInterface = "c6f25543-311c-4c74-83dc-3ea6d1015661"
 MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea"
 Markdown = "d6f4376e-aef5-505a-96c1-9c027394607a"
 MultiData = "8cc5100c-b3d1-4f82-90cb-0ea93d317aba"
-PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SoleData = "123f1ae1-6307-4526-ab5b-aab3a92a2b8c"
+SoleLogics = "b002da8f-3cb3-4d91-bbe3-2953433912b5"
+SoleModels = "4249d9c7-3290-4ddd-961c-e1d3ec2467f8"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+XGBoost = "009559a3-9522-5dbb-924b-0b6ed2b22bb9"
 
 [targets]
-test = ["Test", "DataFrames", "Random", "MLJ", "MultiData", "Markdown", "InteractiveUtils", "BenchmarkTools", "MLJBase", "XGBoost", "DecisionTree", "MLJModelInterface", "MLJDecisionTreeInterface", "SoleData"]
+test = ["Test", "CategoricalArrays", "DataFrames", "Random", "MLJ", "MultiData", "Markdown", "InteractiveUtils", "MLJBase", "XGBoost", "DecisionTree", "MLJModelInterface", "MLJDecisionTreeInterface", "SoleData", "SoleModels", "SoleLogics", "FunctionWrappers"]

ext/MLJXGBoostInterfaceExt.jl

@@ -1,7 +1,6 @@
 using Test
 
 using MLJ
-using MLJ.CategoricalArrays: levelcode, categorical
 using MLJBase
 using DataFrames
 
@@ -58,7 +57,7 @@ solem = solemodel(trees, Matrix(X_train), y_train; classlabels, featurenames, ke
 # Make test instances flow into the model
 X_test_f32 = mapcols(col -> Float32.(col), X_test)
 preds = apply(solem, X_test_f32)
-predsl = CategoricalArrays.levelcode.(categorical(preds)) .- 1
+predsl = CategoricalArrays.levelcode.(CategoricalArrays.categorical(preds)) .- 1
 
 apply!(solem, X_test, y_test)
 @test solem.info.supporting_predictions == preds
@@ -67,7 +66,7 @@ apply!(solem, X_test, y_test)
 # ---------------------------------------------------------------------------- #
 #                                 julia XGBoost                                #
 # ---------------------------------------------------------------------------- #
-yl_train = CategoricalArrays.levelcode.(categorical(y_train)) .- 1
+yl_train = CategoricalArrays.levelcode.(CategoricalArrays.categorical(y_train)) .- 1
 # create and train a gradient boosted tree model of 5 trees
 bst = XGB.xgboost(
     (X_train, yl_train),
@@ -143,9 +142,9 @@ println("RandomForest accuracy: ", rm_accuracy)
                     solem = solemodel(trees, Matrix(X_train), y_train; classlabels, featurenames)
                     X_test_f32 = mapcols(col -> Float32.(col), X_test)
                     preds = apply!(solem, X_test_f32, y_test)
-                    predsl = CategoricalArrays.levelcode.(categorical(preds)) .- 1
+                    predsl = CategoricalArrays.levelcode.(CategoricalArrays.categorical(preds)) .- 1
 
-                    yl_train = CategoricalArrays.levelcode.(categorical(y_train)) .- 1
+                    yl_train = CategoricalArrays.levelcode.(CategoricalArrays.categorical(y_train)) .- 1
                     bst = XGB.xgboost((X_train, yl_train); num_round, eta, num_class=3, objective="multi:softmax")
                     xg_preds = XGB.predict(bst, X_test)